NQF

Agenda Synopsis

Full Agenda

Measure Index

• Hospital Visits following General Surgery Ambulatory Surgical Center Procedures (Workgroup Recommendation: Conditional Support for Rulemaking; Public comments received:7; MUC ID: MUC17-233)
  
  

• Medication Reconciliation for Patients Receiving Care at Dialysis Facilities (Workgroup Recommendation: Support for Rulemaking; Public comments received:2; MUC ID: MUC17-176)
  
  
• Percentage of Prevalent Patients Waitlisted (PPPW) (Workgroup Recommendation: Conditional Support for Rulemaking; Public comments received:6; MUC ID: MUC17-241)
  
  
• Standardized First Kidney Transplant Waitlist Ratio for Incident Dialysis Patients (SWR) (Workgroup Recommendation: Conditional Support for Rulemaking; Public comments received:5; MUC ID: MUC17-245)
  
  

• Hospital-Wide All-Cause Risk Standardized Mortality Measure (Workgroup Recommendation: Conditional Support for Rulemaking; Public comments received:8; MUC ID: MUC17-195)
  
  
• Hybrid Hospital-Wide All-Cause Risk Standardized Mortality Measure (Workgroup Recommendation: Conditional Support for Rulemaking; Public comments received:6; MUC ID: MUC17-196)
  
  
• Hospital Harm Performance Measure: Opioid Related Adverse Respiratory Events (Workgroup Recommendation: Refine and Resubmit Prior to Rulemaking; Public comments received:6; MUC ID: MUC17-210)
  
  

• Lumbar Spine Imaging for Low Back Pain (Workgroup Recommendation: Do Not Support for Rulemaking; Public comments received:1; MUC ID: MUC17-223)
  
  

• Continuity of Pharmacotherapy for Opioid Use Disorder (Workgroup Recommendation: Conditional Support for Rulemaking; Public comments received:1; MUC ID: MUC17-139)
  
  
• Average change in functional status following lumbar spine fusion surgery (Workgroup Recommendation: Support for Rulemaking; Public comments received:1; MUC ID: MUC17-168)
  
  
• Average change in functional status following total knee replacement surgery (Workgroup Recommendation: Support for Rulemaking; Public comments received:2; MUC ID: MUC17-169)
  
  
• Average change in functional status following lumbar discectomy laminotomy surgery (Workgroup Recommendation: Conditional Support for Rulemaking; Public comments received:1; MUC ID: MUC17-170)
  
  
• Appropriate Use of DXA Scans in Women Under 65 Years Who Do Not Meet the Risk Factor Profile for Osteoporotic Fracture (Workgroup Recommendation: Conditional Support for Rulemaking; Public comments received: 0; MUC ID: MUC17-173)
  
  
• Average change in leg pain following lumbar spine fusion surgery (Workgroup Recommendation: Conditional Support for Rulemaking; Public comments received:2; MUC ID: MUC17-177)
  
  
• Optimal Diabetes Care (Workgroup Recommendation: Conditional Support for Rulemaking; Public comments received:2; MUC ID: MUC17-181)
  
  
• Optimal Vascular Care (Workgroup Recommendation: Support for Rulemaking; Public comments received:1; MUC ID: MUC17-194)
  
  
• Diabetes A1c Control (< 8.0) (Workgroup Recommendation: Conditional Support for Rulemaking; Public comments received:1; MUC ID: MUC17-215)
  
  
• Ischemic Vascular Disease Use of Aspirin or Anti-platelet Medication (Workgroup Recommendation: Conditional Support for Rulemaking; Public comments received: 0; MUC ID: MUC17-234)
  
  
• Routine Cataract Removal with Intraocular Lens (IOL) Implantation (Workgroup Recommendation: Conditional Support for Rulemaking; Public comments received:3; MUC ID: MUC17-235)
  
  
• International Prostate Symptom Score (IPSS) or American Urological Association-Symptom Index (AUA-SI) change 6-12 months after diagnosis of Benign Prostatic Hyperplasia (Workgroup Recommendation: Conditional Support for Rulemaking; Public comments received:1; MUC ID: MUC17-239)
  
  
• Screening/Surveillance Colonoscopy (Workgroup Recommendation: Conditional Support for Rulemaking; Public comments received:2; MUC ID: MUC17-256)
  
  
• Knee Arthroplasty (Workgroup Recommendation: Conditional Support for Rulemaking; Public comments received:4; MUC ID: MUC17-261)
  
  
• ST-Elevation Myocardial Infarction (STEMI) with Percutaneous Coronary Intervention (PCI) (Workgroup Recommendation: Conditional Support for Rulemaking; Public comments received:3; MUC ID: MUC17-262)
  
  
• Revascularization for Lower Extremity Chronic Limb Ischemia (Workgroup Recommendation: Conditional Support for Rulemaking; Public comments received:2; MUC ID: MUC17-263)
  
  
• Zoster (Shingles) Vaccination (Workgroup Recommendation: Conditional Support for Rulemaking; Public comments received:1; MUC ID: MUC17-310)
  
  
• Patient reported and clinical outcomes following ilio-femoral venous stenting (Workgroup Recommendation: Refine and Resubmit Prior to Rulemaking; Public comments received: 0; MUC ID: MUC17-345)
  
  
• Elective Outpatient Percutaneous Coronary Intervention (PCI) (Workgroup Recommendation: Conditional Support for Rulemaking; Public comments received:3; MUC ID: MUC17-359)
  
  
• Intracranial Hemorrhage or Cerebral Infarction (Workgroup Recommendation: Conditional Support for Rulemaking; Public comments received:4; MUC ID: MUC17-363)
  
  
• Simple Pneumonia with Hospitalization (Workgroup Recommendation: Conditional Support for Rulemaking; Public comments received:1; MUC ID: MUC17-365)
  
  
• HIV Screening (Workgroup Recommendation: Conditional Support for Rulemaking; Public comments received:1; MUC ID: MUC17-367)
  
  

• Optimal Diabetes Care (Workgroup Recommendation: Conditional Support for Rulemaking; Public comments received:4; MUC ID: MUC17-181)
  
  
• Diabetes A1c Control (< 8.0) (Workgroup Recommendation: Conditional Support for Rulemaking; Public comments received:6; MUC ID: MUC17-215)
  
  
• Ischemic Vascular Disease Use of Aspirin or Anti-platelet Medication (Workgroup Recommendation: Conditional Support for Rulemaking; Public comments received:1; MUC ID: MUC17-234)
  
  

• 30-Day Unplanned Readmissions for Cancer Patients (Workgroup Recommendation: Support for Rulemaking; Public comments received:3; MUC ID: MUC17-178)
  
  

• CoreQ: Short Stay Discharge Measure (Workgroup Recommendation: Support for Rulemaking; Public comments received:1; MUC ID: MUC17-258)
  
  

Full Measure Information

Measure Specifications

• NQF Number (if applicable): 0
  
  
• Description: The measure assesses ASC general surgery procedure quality using the outcome of hospital visits -- including emergency department (ED) visits, observation stays, and unplanned inpatient admissions -- within 7 days of the procedure performed at an ASC.
  
  
• Numerator: For each ASC, the numerator of the ratio is the number of hospital visits predicted for the ASC’s patients, accounting for its observed rate, the number and complexity of general surgery procedures performed at the ASC, and the case mix.
  
  
• Denominator: The denominator is the number of hospital visits expected nationally for the ASC’s case/procedure mix.
  
  
• Exclusions: Procedures for patients who survived at least 7 days, but were not continuously enrolled in Medicare FFS Parts A and B in the 7 days after the surgery are excluded. These patients are excluded to ensure all patients have full data available for outcome assessment.
  
  
• HHS NQS Priority: Making Care Safer; Communication and Care Coordination
  
  
• HHS Data Source:
  
  
• Measure Type: Outcome
  
  
• Steward: Centers for Medicare & Medicaid Services
  
  
• Endorsement Status: Never Submitted
  
  
• Is the measure specified as an electronic clinical quality measure? No
  
  

Summary of Workgroup Deliberations

• Workgroup Recommendation: Conditional Support for Rulemaking
  
  
• Workgroup Rationale: MAP conditionally supported MUC17-233 for the ASCQR program pending NQF review and endorsement. MAP recognized that this measure assesses an important outcome for patients receiving care at ambulatory surgery centers and addresses crucial safety concerns by tracking if a patient requires treatment at an acute care hospital (including emergency department (ED) visits, observation stays, and unplanned inpatient admissions) within 7 days of the procedure performed at an ASC. MAP noted this measure could help balance incentives to perform more procedures on an outpatient basis. However, MAP acknowledged a number of concerns raised in public comments about the measure. Commenters raised concerns about the attribution model of measure, noting that these are relatively rare events and could disproportionately impact low-volume ASCs, and that the measure may need risk adjustment for social risk factors. MAP noted this measure should be submitted for NQF endorsement to assess the potential impact of these concerns on the reliability and validity of the measure.
  
  
• Public comments received: 7

Rationale for measure provided by HHS
Improving the quality of care provided at ASCs is a key priority in the context of growth in the number of ASCs and procedures performed in this setting. More than 60% of all medical or surgical procedures were performed at ASCs in 2006 -- a three-fold increase since the late 1990s.1 In 2013, more than 3.4 million Fee-for-Service (FFS) Medicare beneficiaries were treated at 5,364 Medicare-certified ASCs, and spending on ASC services by Medicare and its beneficiaries amounted to $3.7 billion.2 The patient population served at ASCs has increased not only in volume but also in age and complexity, which can be partially attributed to improvements in anesthetic care and innovations in minimally invasive surgical techniques.3,4 ASCs have become the preferred setting for the provision of low-risk surgical and medical procedures in the US, as many patients experience shorter wait times, prefer to avoid hospitalization, and are able to return rapidly to work.1 Therefore, in the context of growth in volume and diversity of procedures performed at ASCs, evaluating the quality of care provided at ASCs is increasingly important. In the literature, hospital visit rates following outpatient surgery vary from 0.5-9.0%, based on the type of surgery, outcome measured (admissions alone or admissions and ED visits), and timeframe for measurement after surgery.5-12 These hospital visits can occur due to a range of well-described adverse events, including major adverse events, such as bleeding, wound infection, septicemia, and venous thromboembolism. Patients also frequently report minor adverse events -- for example, uncontrolled pain, nausea, and vomiting -- that may result in unplanned acute care visits following surgery. Several factors make unanticipated hospital visits a priority quality indicator. Because ASC providers are not aware of all post-surgical hospital visits that occur among their patients, reporting this outcome will help to illuminate problems that may not be currently visible. In addition, the outcome of hospital visits is a broad, patient-centered outcome that reflects the full range of reasons leading to hospital use among patients undergoing same-day surgery. Public reporting of this outcome measure will provide ASCs with critical information and incentives to implement strategies to reduce unplanned hospital visits. Given that ASCs vary widely in their focus and the number of procedures that they perform, focusing on general surgery procedures will enable use of a quality measure to make fair comparisons of outcome rates across facilities that perform similar procedures. 1. Cullen KA, Hall MJ, Golosinskiy A, Statistics NCfH. Ambulatory surgery in the United States, 2006. US Department of Health and Human Services, Centers for Disease Control and Prevention, National Center for Health Statistics; 2009. 2. Medicare Payment Advisory Commission (MedPAC). Report to Congress: Medicare Payment Policy. March 2015; http://www.medpac.gov/docs/default-source/reports/mar2015_entirereport_revised.pdf. 3. Bettelli G. High risk patients in day surgery. Minerva anestesiologica. 2009;75(5):259-268. 4. Fuchs K. Minimally invasive surgery. Endoscopy. 2002;34(2):154-159. 5. Majholm BB. Is day surgery safe? A Danish multicentre study of morbidity after 57,709 day surgery procedures. Acta anaesthesiologica Scandinavica. 2012;56(3):323-331. 6. Whippey A, Kostandoff G, Paul J, Ma J, Thabane L, Ma HK. Predictors of unanticipated admission following ambulatory surgery: a retrospective case-control study. Canadian Journal of Anesthesia/Journal canadien d'anesthésie. 2013;60(7):675-683. 7. Fleisher LA, Pasternak LR, Herbert R, Anderson GF. Inpatient hospital admission and death after outpatient surgery in elderly patients: importance of patient and system characteristics and location of care. Arch Surg. 2004;139(1):67-72. 8. Coley KC, Williams BA, DaPos SV, Chen C, Smith RB. Retrospective evaluation of unanticipated admissions and readmissions after same day surgery and associated costs. Journal of clinical anesthesia. 2002;14(5):349-353. 9. Hollingsworth JMJM. Surgical quality among Medicare beneficiaries undergoing outpatient urological surgery. The Journal of urology. 2012;188(4):1274-1278. 10. Bain J, Kelly H, Snadden D, Staines H. Day surgery in Scotland: patient satisfaction and outcomes. Quality in Health Care. 1999;8(2):86-91. 11. Fortier J, Chung F, Su J. Unanticipated admission after ambulatory surgery--a prospective study. Canadian journal of anaesthesia = Journal canadien d'anesthesie. 1998;45(7):612-619. 12. Aldwinckle R, Montgomery J. Unplanned admission rates and postdischarge complications in patients over the age of 70 following day case surgery. Anaesthesia. 2004;59(1):57-59.

Measure Specifications

• NQF Number (if applicable): 2988
  
  
• Description: Percentage of patient-months for which medication reconciliation* was performed and documented by an eligible professional.** * “Medication reconciliation” is defined as the process of creating the most accurate list of all home medications that the patient is taking, including name, indication, dosage, frequency, and route, by comparing the most recent medication list in the dialysis medical record to one or more external list(s) of medications obtained from a patient or caregiver (including patient-/caregiver-provided “brown bag” information), pharmacotherapy information network (e.g., Surescripts), hospital, or other provider. ** For the purposes of medication reconciliation, “eligible professional” is defined as: physician, RN, ARNP, PA, pharmacist, or pharmacy technician.
  
  
• Numerator: Number of patient-months for which medication reconciliation was performed and documented by an eligible professional during the reporting period. The medication reconciliation MUST: - Include the name or other unique identifier of the eligible professional; AND - Include the date of the reconciliation; AND - Address ALL known home medications (prescriptions, over-the-counters, herbals, vitamin/mineral/dietary (nutritional) supplements, and medical marijuana); AND - Address for EACH home medication: Medication name(1), indication(2), dosage(2), frequency(2), route of administration(2), start and end date (if applicable)(2), discontinuation date (if applicable)(2), reason medication was stopped or discontinued (if applicable)(2), and identification of individual who authorized stoppage or discontinuation of medication (if applicable)(2); AND - List any allergies, intolerances, or adverse drug events experienced by the patient. 1. For patients in a clinical trial, it is acknowledged that it may be unknown as to whether the patient is receiving the therapeutic agent or a placebo. 2. “Unknown” is an acceptable response for this field. NUMERATOR STEP 1. For each patient meeting the denominator criteria in the given calculation month, identify all patients with each of the following three numerator criteria (a, b, and c) documented in the facility medical record to define the numerator for that month: A. Facility attestation that during the calculation month: 1. The patient’s most recent medication list in the dialysis medical record was reconciled to one or more external list(s) of medications obtained from the patient/caregiver (including patient-/caregiver-provided “brown-bag” information), pharmacotherapy information network (e.g., Surescripts®), hospital, or other provider AND that ALL known medications (prescriptions, OTCs, herbals, vitamin/mineral/dietary [nutritional] supplements, and medical marijuana) were reconciled; AND 2. ALL of the following items were addressed for EACH identified medication: a) Medication name; b) Indication (or “unknown”); c) Dosage (or “unknown”); d)Frequency (or “unknown”); e) Route of administration (or “unknown”); f) Start date (or “unknown”); g) End date, if applicable (or “unknown”); h) Discontinuation date, if applicable (or “unknown”); i) Reason medication was stopped or discontinued, if applicable (or “unknown”); and j) Identification of individual who authorized stoppage or discontinuation of medication, if applicable (or “unknown”); AND 3. Allergies, intolerances, and adverse drug events were addressed and documented. B. Date of the medication reconciliation. C. Identity of eligible professional performing the medication reconciliation. NUMERATOR STEP 2. Repeat “Numerator Step 1” for each month of the one-year reporting period to define the final numerator (patient-months).
  
  
• Denominator: Total number of patient-months for all patients permanently assigned to a dialysis facility during the reporting period. DENOMINATOR STEP 1. Identify all in-center and home hemodialysis and peritoneal dialysis patients permanently assigned to the dialysis facility in the given calculation month. DENOMINATOR STEP 2. For all patients included in the denominator in the given calculation month in “Denominator Step 1”, identify and remove all in-center hemodialysis patients who received < 7 dialysis treatments in the calculation month. DENOMINATOR STEP 3. Repeat “Denominator Step 1” and “Denominator Step 2” for each month of the one-year reporting period.
  
  
• Exclusions: In-center patients who receive < 7 hemodialysis treatments in the facility during the reporting month. As detailed in “Denominator Step 2” above, transient patients, defined as in-center patients who receive < 7 hemodialysis treatments in the facility during the reporting month, are excluded from the measure.
  
  
• HHS NQS Priority: Making Care Safer; Communication and Care Coordination
  
  
• HHS Data Source:
  
  
• Measure Type: Process/Care Coordination
  
  
• Steward: KCQA
  
  
• Endorsement Status: Endorsed
  
  
• Is the measure specified as an electronic clinical quality measure? No
  
  

Summary of Workgroup Deliberations

• Workgroup Recommendation: Support for Rulemaking
  
  
• Workgroup Rationale: MAP supported MUC17-176 for the ESRD QIP. This is an NQF endorsed measure that addresses both patient safety and care coordination. MAP noted that medication reconciliation is currently a gap area in the program measure set and that this measure has broad support across stakeholders. MAP emphasized that medication reconciliation is an important issue for ESRD patients who see multiple clinicians and providers and may require numerous medications. MAP noted that being given the wrong medication can have grave consequences for an ESRD patient. MAP noted that in the future measurement should address full medication management and provide greater clarity about who is qualified to perform medication reconciliation.
  
  
• Public comments received: 2

Rationale for measure provided by HHS
Medication management is a critical safety issue for all patients, but especially so for patients with ESRD, who often require 10 or more medications and take an average of 17-25 doses per day, have numerous comorbid conditions, have multiple healthcare providers and prescribers, and undergo frequent medication regimen changes(1,2,3,4). Medication-related problems (MRPs) contribute significantly to the approximately $40 billion in public and private funds spent annually on ESRD care in the United States(5,6), and it is believed that medication management practices focusing on medication documentation, review, and reconciliation could systematically identify and resolve MRPs, improve ESRD patient outcomes, and reduce total costs of care. As most hemodialysis patients are seen at least thrice weekly and peritoneal dialysis patients monthly, the dialysis facility has been suggested as a reasonable locale for medication therapy management(7).

Summary of NQF Endorsement Review

• Year of Most Recent Endorsement Review: 2017
  
  
• Project for Most Recent Endorsement Review: Patient Safety Project 2015-2017
  
  
• Review for Importance: 1. Importance to Measure and Report: The measure meets the Importance criteria (1a. Evidence, 1b. Performance Gap) 1a. Evidence: 0-H; 14-M; 4-L; 1-I 1b. Performance Gap: 7-H; 10-M; 1-L; 2-I Rationale: • The developer conducted a literature review which shows evidence to support the high incidence of medication-related problems in dialysis patients as well as evidence that supports their economic impact. • Performance scores over time are not available. However, the measure was tested using data from three Kidney Quality Alliance member dialysis organizations, each with the capacity to provide retrospective analysis from a data warehouse repository. The mean performance score obtained from these organizations was 52.62% with a median score of 48.18%.
  
  
• Review for Scientific Acceptability: 2. Scientific Acceptability of Measure Properties: The measure meets the Scientific Acceptability criteria (2a. Reliability - precise specifications, testing; 2b. Validity - testing, threats to validity) 2a. Reliability: 9-H; 10-M; 0-L; 0-I 2b. Validity: 0-H; 17-M; 2-L; 0-I Rationale: • The developer tested the measure at the score level using beta-binomial testing. The mean reliability score is 0.9935. • There was a systematic assessment of face validity by experts. Two groups of field experts in the field of ESRD / dialysis care. o 88.9% of the 9-member panel agreed it is highly likely or likely that the measure score provides an accurate reflection of medication reconciliation quality. o 77.8% of the panel agreed it is highly likely or likely that the measure can be used to distinguish good from poor quality.
  
  
• Review for Feasibility: 3. Feasibility: 6-H; 11-M; 1-L; 2-I 42 (3a. Clinical data generated during care delivery; 3b. Electronic sources; 3c. Susceptibility to inaccuracies/ unintended consequences identified 3d. Data collection strategy can be implemented) Rationale: • All data elements are defined in fields in electronic health records. • This measure is generated or collected by and used by healthcare personnel during the provision of care (e.g., blood pressure, lab value, diagnosis, depression score)
  
  
• Review for Usability: 4. Usability and Use: 5-H; 12-M; 3-L; 0-I (Used and useful to the intended audiences for 4a. Accountability and Transparency; 4b. Improvement; and 4c. Benefits outweigh evidence of unintended consequences) Rationale: • Variants of the measure are currently in use member dialysis organizations for internal quality improvement, prompting the developer to develop this measure to standardize the specifications and definitions for accountability purposes. • The developer suggests the measure be used in accountability programs in the future.
  
  
• Review for Related and Competing Measures: 5. Related and Competing Measures Related measures: • 0097: Medication Reconciliation Post-Discharge- The percentage of discharges for patients 18 years of age and older for whom the discharge medication list was reconciled with the current medication list in the outpatient medical record by a prescribing practitioner, clinical pharmacist or registered nurse. • 0554: Medication Reconciliation Post-Discharge (MRP)- The percentage of discharges during the first 11 months of the measurement year (e.g., January 1–December 1) for patients 66 years of age and older for whom medications were reconciled on or within 30 days of discharge. • 2456: Medication Reconciliation: Number of Unintentional Medication Discrepancies per Patient-This measure assesses the actual quality of the medication reconciliation process by identifying errors in admission and discharge medication orders due to problems with the medication reconciliation process. The target population is any hospitalized adult patient. The time frame is the hospitalization period. • This measure is harmonized with existing NQF-endorsed medication reconciliation measures in that all similarly specify that the medication reconciliation must address ALL prescriptions, overthe-counters, herbals, vitamin/mineral/dietary (nutritional) supplements AND must contain the medications’ name, dosage, frequency, and route. This measure, however, is unique among the currently endorsed medication reconciliation measures in that the level of analysis is the dialysis facility. The KCQA measure also moves beyond a single "check/box”, specifying multiple components that must be met to be counted as a “success”.
  
  
• Endorsement Public Comments: 6. Public and Member Comment Comments: 43 This measure received 2 comments. One comment expressed that medication reconciliation as a quality measure becomes too burdensome for providers without actually demonstrating that meaningful reconciliation has taken place. Another comment noted that the measure may not be harmonized with existing measures. Developer Response: KCQA agrees that medication reconciliation is a critical domain for patient safety and shares RPA’s belief that, ideally, a systematic approach to medication management would optimize care. We note that the publication referenced in RPA’s comment (Pai, 2013) suggests that the optimal model for such a systematic approach to medication management therapy (MTM) services for ESRD patients should be structured around the dialysis facility and provided by a pharmacist; the authors acknowledge that most dialysis facilities do not have ready access to a pharmacist. Recognizing this, the KCQA measure specifications permit medication reconciliation by appropriate, qualified professionals. We disagree that NQF 2988 will be a “paper chase,” and note that during testing in 5,292 facilities, approximately 4.5% of facilities scored 0 on the measure over the 6-month period for which data were examined. We believe it is a crucial first step towards improving medication management processes in the ESRD population that will improve patient safety. Going forward, we look forward to continuing to work with RPA, a KCQA member, and other members to improve medication management and this measure. Committee Response: The Committee agrees with the developer response and maintains their decision to recommend this measure for continued endorsement.
  
  
• Endorsement Committee Recommendation: Steering Committee Recommendation for Endorsement: 17-Y; 2-N
  
  

Measure Specifications

• NQF Number (if applicable): 0
  
  
• Description: This measure tracks the percentage of patients at each dialysis facility who were on the kidney or kidney-pancreas transplant waiting list. Results are averaged across patients prevalent on the last day of each month during the reporting year.
  
  
• Numerator: The numerator is the adjusted count of patient-months in which the patient at the dialysis facility is on the kidney or kidney-pancreas transplant waiting list as of the last day of each month during the reporting year. The number of patient-months on the kidney or kidney-pancreas transplant waiting list as of the last day of each month at a given facility, adjusted for age effect.
  
  
• Denominator: All patient-months for patients who are under the age of 75 on the last day of each month and who are assigned to the dialysis facility according to each patient’s treatment history as of the last day of each month during the reporting year. A treatment history file is the data source for the denominator calculation used for the analyses supporting this submission. This file provides a complete history of the status, location, and dialysis treatment modality of an ESRD patient from the date of the first ESRD service until the patient dies or the data collection cutoff date is reached. For each patient, a new record is created each time he/she changes facility or treatment modality. Each record represents a time period associated with a specific modality and dialysis facility. CROWNWeb is the primary basis for placing patients at dialysis facilities and dialysis claims are used as an additional source. Information regarding first ESRD service date, death, and transplant is obtained from CROWNWeb (including the CMS Medical Evidence Form (Form CMS-2728) and the Death Notification Form (Form CMS-2746)) and Medicare claims, as well as the Organ Procurement and Transplant Network (OPTN) and the Social Security Death Master File.
  
  
• Exclusions: Exclusions that are implicit in the denominator include: - Patients 75 years of age and older on the last day of each month during the reporting year. In addition, patients who were admitted to a skilled nursing facility (SNF) or hospice during the month of evaluation were excluded from that month. The CMS Medical Evidence Form and the CMS Long Term Care Minimum Data Set (MDS) were the data sources used for determining skilled nursing facility (SNF) patients. Patients who were identified in Questions 17u and 22 on the CMS Medical Evidence Form as institutionalized and SNF/Long Term Care Facility, respectively, or who had evidence of admission to a skilled nursing facility based on the MDS in the current month were identified as SNF patients. Hospice status is determined from a separate CMS file that contains final action claims submitted by Hospice providers. Once a beneficiary elects Hospice, all Hospice related claims will be found in this file, regardless if the beneficiary is in Medicare fee-for-service or in a Medicare managed care plan. Patients are identified as receiving hospice care if they have any final action claims submitted to Medicare by hospice providers in the current month.
  
  
• HHS NQS Priority: Effective Prevention and Treatment
  
  
• HHS Data Source:
  
  
• Measure Type: Process
  
  
• Steward: Centers for Medicare & Medicaid Services
  
  
• Endorsement Status: Never Submitted
  
  
• Is the measure specified as an electronic clinical quality measure? No
  
  

Summary of Workgroup Deliberations

• Workgroup Recommendation: Conditional Support for Rulemaking
  
  
• Workgroup Rationale: MAP acknowledged that this measure addresses an important quality gap for dialysis facilities; however, it discussed a number of factors that should be balanced when implementing this measure. MAP reiterated the critical need to help patients receive kidney transplants to improve their quality of life and reduce their risk of mortality. The MAP also noted there are disparities in the receipt of kidney transplants and there is a need to incentivize dialysis facilities to educate patients about wait listing processes and requirements. On the other hand, the MAP also recognized concerns about the locus of control of the measure and raised concerns that dialysis facilities may not be able to adequately influence this measure as transplant centers. The MAP also noted the need to ensure the measure is appropriately risk-adjusted and recommended the exploration of adjustment for social risk factors and proper risk model performance. The MAP ultimately supported the measure with the condition that it is submitted for NQF review and endorsement. Specifically, the MAP recommended that this measure be reviewed by the Scientific Methods Panel as well the Renal Standing Committee. The MAP recommended the endorsement process examine the validity of the measure, particularly the risk adjustment model and if it appropriately accounts for social risk. Finally, the MAP noted the need for the Attribution Expert Panel to provide further guidance on the attribution model as well as for the Disparities Standing Committee to provide guidance on potential health equity concerns.
  
  
• Public comments received: 6

Rationale for measure provided by HHS
A measure focusing on the waitlisting process is appropriate for improving access to kidney transplantation for several reasons. First, waitlisting is a necessary step prior to potential receipt of a deceased donor kidney. Second, dialysis facilities exert substantial control over the process of waitlisting. This includes proper education of dialysis patients on the option for transplant, referral of appropriate patients to a transplant center for evaluation, assisting patients with completion of the transplant evaluation process, and optimizing the health and functional status of patients in order to increase their candidacy for transplant waitlisting. These types of activities are included as part of the conditions for coverage for Medicare certification of ESRD dialysis facilities. In addition, dialysis facilities can also help maintain patients on the wait list through assistance with ongoing evaluation activities and by optimizing health and functional status. Finally, wide regional variations in waitlisting rates highlight substantial room for improvement for this process measure [1,2,3]. This measure focuses specifically on the prevalent dialysis population, examining waitlisting status monthly for each patient. This allows evaluation and encouragement of ongoing waitlisting of patients beyond the first year of dialysis initiation who have not yet been listed. Patients may not be ready, either psychologically or due to their health status, to consider transplantation early after initiation of dialysis and many choose to undergo evaluation for transplantation only after years on dialysis. In addition, as this measure assesses monthly waitlisting status of patients, it also evaluates and encourages maintenance of patients on the waitlist. This is an important area to which dialysis facilities can contribute through ensuring patients remain healthy, and complete any ongoing testing activities required to remain on the waitlist. 1. Ashby VB, Kalbfleisch JD, Wolfe RA, et al. Geographic variability in access to primary kidney transplantation in the United States, 1996-2005. American Journal of Transplantation 2007; 7 (5 Part 2):1412-1423. Abstract: This article focuses on geographic variability in patient access to kidney transplantation in the United States. It examines geographic differences and trends in access rates to kidney transplantation, in the component rates of wait-listing, and of living and deceased donor transplantation. Using data from Centers for Medicare and Medicaid Services and the Organ Procurement and Transplantation Network/Scientific Registry of Transplant Recipients, we studied 700,000+ patients under 75, who began chronic dialysis treatment, received their first living donor kidney transplant, or were placed on the waiting list pre-emptively. Relative rates of wait-listing and transplantation by State were calculated using Cox regression models, adjusted for patient demographics. There were geographic differences in access to the kidney waiting list and to a kidney transplant. Adjusted wait-list rates ranged from 37% lower to 64% higher than the national average. The living donor rate ranged from 57% lower to 166% higher, while the deceased donor transplant rate ranged from 60% lower to 150% higher than the national average. In general, States with higher wait-listing rates tended to have lower transplantation rates and States with lower wait-listing rates had higher transplant rates. Six States demonstrated both high wait-listing and deceased donor transplantation rates while six others, plus D.C. and Puerto Rico, were below the national average for both parameters. 2. Satayathum S, Pisoni RL, McCullough KP, et al. Kidney transplantation and wait-listing rates from the international Dialysis Outcomes and Practice Patterns Study (DOPPS). Kidney Intl 2005 Jul; 68 (1):330-337. Abstract: BACKGROUND: The international Dialysis Outcomes and Practice Patterns Study (DOPPS I and II) allows description of variations in kidney transplantation and wait-listing from nationally representative samples of 18- to 65-year-old hemodialysis patients. The present study examines the health status and socioeconomic characteristics of United States patients, the role of for-profit versus not-for-profit status of dialysis facilities, and the likelihood of transplant wait-listing and transplantation rates. METHODS: Analyses of transplantation rates were based on 5267 randomly selected DOPPS I patients in dialysis units in the United States, Europe, and Japan who received chronic hemodialysis therapy for at least 90 days in 2000. Left-truncated Cox regression was used to assess time to kidney transplantation. Logistic regression determined the odds of being transplant wait-listed for a cross-section of 1323 hemodialysis patients in the United States in 2000. Furthermore, kidney transplant wait-listing was determined in 12 countries from cross-sectional samples of DOPPS II hemodialysis patients in 2002 to 2003 (N= 4274). RESULTS: Transplantation rates varied widely, from very low in Japan to 25-fold higher in the United States and 75-fold higher in Spain (both P values <0.0001). Factors associated with higher rates of transplantation included younger age, nonblack race, less comorbidity, fewer years on dialysis, higher income, and higher education levels. The likelihood of being wait-listed showed wide variation internationally and by United States region but not by for-profit dialysis unit status within the United States. CONCLUSION: DOPPS I and II confirmed large variations in kidney transplantation rates by country, even after adjusting for differences in case mix. Facility size and, in the United States, profit status, were not associated with varying transplantation rates. International results consistently showed higher transplantation rates for younger, healthier, better-educated, and higher income patients. 3. Patzer RE, Plantinga L, Krisher J, Pastan SO. Dialysis facility and network factors associated with low kidney transplantation rates among United States dialysis facilities. Am J Transplant. 2014 Jul; 14(7):1562-72. Abstract: Variability in transplant rates between different dialysis units has been noted, yet little is known about facility-level factors associated with low standardized transplant ratios (STRs) across the United States End-stage Renal Disease (ESRD) Network regions. We analyzed Centers for Medicare & Medicaid Services Dialysis Facility Report data from 2007 to 2010 to examine facility-level factors associated with low STRs using multivariable mixed models. Among 4098 dialysis facilities treating 305 698 patients, there was wide variability in facility-level STRs across the 18 ESRD Networks. Four-year average STRs ranged from 0.69 (95% confidence interval [CI]: 0.64-0.73) in Network 6 (Southeastern Kidney Council) to 1.61 (95% CI: 1.47-1.76) in Network 1 (New England). Factors significantly associated with a lower Standardized Transplantation Ratio(STR) (p < 0.0001) included for-profit status, facilities with higher percentage black patients, patients with no health insurance and patients with diabetes. A greater number of facility staff, more transplant centers per 10 000 ESRD patients and a higher percentage of patients who were employed or utilized peritoneal dialysis were associated with higher STRs. The lowest performing dialysis facilities were in the Southeastern United States. Understanding the modifiable facility-level factors associated with low transplant rates may inform interventions to improve access to transplantation.

Measure Specifications

• NQF Number (if applicable): 0
  
  
• Description: This measure tracks the number of incident patients at the dialysis facility under the age of 75 listed on the kidney or kidney-pancreas transplant waitlist or who received living donor transplants within the first year of initiating dialysis.
  
  
• Numerator: Number of patients at the dialysis facility listed on the kidney or kidney-pancreas transplant waitlist or who received living donor transplants within the first year following initiation of dialysis. Data are currently aggregated across 3 years due to the low number of event rates. The numerator for the SWR is the observed number of events (i.e., waitlisting or receipt of a living-donor transplant). To be included in the numerator for a particular facility, the patient must meet one of the two criteria: - The patient is on the kidney or kidney-pancreas transplant waitlist or - The patient has received a living donor transplant
  
  
• Denominator: The denominator for the SWR is the expected number of wait listing or living donor transplant events at the facility according to each patient’s treatment history for patients within the first year following initiation of dialysis, adjusted for age and incident comorbidities, among patients under 75 years of age who were not already waitlisted prior to dialysis. A treatment history file is the data source for the denominator calculation used for the analyses supporting this submission. This file provides a complete history of the status, location, and dialysis treatment modality of an ESRD patient from the date of the first ESRD service until the patient dies or the data collection cutoff date is reached. For each patient, a new record is created each time he/she changes facility or treatment modality. Each record represents a time period associated with a specific modality and dialysis facility. CROWNWeb is the primary basis for placing patients at dialysis facilities and dialysis claims are used as an additional source. Information regarding first ESRD service date, death, and transplant is obtained from CROWNWeb (including the CMS Medical Evidence Form (Form CMS-2728) and the Death Notification Form (Form CMS-2746)) and Medicare claims, as well as the Organ Procurement and Transplant Network (OPTN) and the Social Security Death Master File. The denominator of the SWR for a given facility represents the number of expected events (waitlistings or living-donor transplants) at the facility. The estimation of this expected number accounts for the follow-up time and risk profile of each patient. The risk profile is quantified through covariate effects estimated through Cox regression (Cox, 1972; SAS Institute Inc., 2004; Kalbfleisch and Prentice, 2002; Collett, 1994).
  
  
• Exclusions: Exclusions that are implicit in the denominator definition include: - Patients at the facility who were 75 years of age and older at initiation of dialysis - Patients at the facility who were listed on the kidney or kidney-pancreas transplant waitlist prior to the start of dialysis In addition, patients who were admitted to a skilled nursing facility (SNF) or hospice at the time of initiation of dialysis were excluded. The CMS Medical Evidence Form and the CMS Long Term Care Minimum Data Set (MDS) were the data sources used for determining skilled nursing facility (SNF) patients. Patients who were identified in Questions 17u and 22 on the CMS Medical Evidence Form as institutionalized and SNF/Long Term Care Facility, respectively, or who had evidence of admission to a skilled nursing facility based on the MDS before their first service date and were not discharged prior to initiation of dialysis were identified as SNF patients. Hospice status is determined from a separate CMS file that contains final action claims submitted by Hospice providers. Once a beneficiary elects Hospice, all Hospice related claims will be found in this file, regardless if the beneficiary is in Medicare fee-for-service or in a Medicare managed care plan. Patients are identified as receiving hospice care if they have any final action claims submitted to Medicare by hospice providers in the current month.
  
  
• HHS NQS Priority: Effective Prevention and Treatment
  
  
• HHS Data Source:
  
  
• Measure Type: Process
  
  
• Steward: Centers for Medicare & Medicaid Services
  
  
• Endorsement Status: Never Submitted
  
  
• Is the measure specified as an electronic clinical quality measure? No
  
  

Summary of Workgroup Deliberations

• Workgroup Recommendation: Conditional Support for Rulemaking
  
  
• Workgroup Rationale: The MAP acknowledged that this measure addresses an important quality gap for dialysis facilities; however, it discussed a number of factors that should be balanced when implementing this measure. The MAP reiterated the critical need to help patients receive kidney transplants to improve their quality of life and reduce their risk of mortality. The MAP also noted there are disparities in the receipt of kidney transplants and there is a need to incentivize dialysis facilities to educate patients about wait listing processes and requirements. On the other hand, the MAP also recognized concerns about the locus of control of the measure and raised concerns that dialysis facilities may not be able to as meaningfully influence this measure as the transplant center. The MAP also noted the need to ensure the measure is appropriately risk-adjusted and recommended the exploration of adjustment for social risk factors and proper risk model performance. The MAP ultimately supported the measure with the condition that it is submitted for NQF review and endorsement. Specifically, the MAP recommended that this measure be reviewed by the Scientific Methods Panel as well the Renal Standing Committee. The MAP recommended the endorsement process examine the validity of the measure, particularly the risk adjustment model and if it appropriately accounts for social risk. Finally, the MAP noted the need for the Attribution Expert Panel to provide further guidance on the attribution model as well as for the Disparities Standing Committee to provide guidance on potential health equity concerns.
  
  
• Public comments received: 5

Rationale for measure provided by HHS
A measure focusing on the waitlisting process is appropriate for improving access to kidney transplantation for several reasons. First, waitlisting is a necessary step prior to potential receipt of a deceased donor kidney (receipt of a living donor kidney is also accounted for in the measure). Second, dialysis facilities exert substantial control over the process of waitlisting. This includes proper education of dialysis patients on the option for transplant, referral of appropriate patients to a transplant center for evaluation, assisting patients with completion of the transplant evaluation process, and optimizing the health and functional status of patients in order to increase their candidacy for transplant waitlisting. These types of activities are included as part of the conditions for coverage for Medicare certification of ESRD dialysis facilities. Finally, wide regional variations in waitlisting rates highlight substantial room for improvement for this process measure [1,2,3]. This measure additionally focuses specifically on the population of patients incident to dialysis, examining for waitlist or living donor transplant events occurring within a year of dialysis initiation. This will evaluate and encourage rapid attention from dialysis facilities to waitlisting of patients to ensure early access to transplantation. 1. Ashby VB, Kalbfleisch JD, Wolfe RA, et al. Geographic variability in access to primary kidney transplantation in the United States, 1996-2005. American Journal of Transplantation 2007; 7 (5 Part 2):1412-1423. Abstract: This article focuses on geographic variability in patient access to kidney transplantation in the United States. It examines geographic differences and trends in access rates to kidney transplantation, in the component rates of wait-listing, and of living and deceased donor transplantation. Using data from Centers for Medicare and Medicaid Services and the Organ Procurement and Transplantation Network/Scientific Registry of Transplant Recipients, we studied 700,000+ patients under 75, who began chronic dialysis treatment, received their first living donor kidney transplant, or were placed on the waiting list pre-emptively. Relative rates of wait-listing and transplantation by State were calculated using Cox regression models, adjusted for patient demographics. There were geographic differences in access to the kidney waiting list and to a kidney transplant. Adjusted wait-list rates ranged from 37% lower to 64% higher than the national average. The living donor rate ranged from 57% lower to 166% higher, while the deceased donor transplant rate ranged from 60% lower to 150% higher than the national average. In general, States with higher wait-listing rates tended to have lower transplantation rates and States with lower wait-listing rates had higher transplant rates. Six States demonstrated both high wait-listing and deceased donor transplantation rates while six others, plus D.C. and Puerto Rico, were below the national average for both parameters. 2. Satayathum S, Pisoni RL, McCullough KP, et al. Kidney transplantation and wait-listing rates from the international Dialysis Outcomes and Practice Patterns Study (DOPPS). Kidney Intl 2005 Jul; 68 (1):330-337. Abstract: BACKGROUND: The international Dialysis Outcomes and Practice Patterns Study (DOPPS I and II) allows description of variations in kidney transplantation and wait-listing from nationally representative samples of 18- to 65-year-old hemodialysis patients. The present study examines the health status and socioeconomic characteristics of United States patients, the role of for-profit versus not-for-profit status of dialysis facilities, and the likelihood of transplant wait-listing and transplantation rates. METHODS: Analyses of transplantation rates were based on 5267 randomly selected DOPPS I patients in dialysis units in the United States, Europe, and Japan who received chronic hemodialysis therapy for at least 90 days in 2000. Left-truncated Cox regression was used to assess time to kidney transplantation. Logistic regression determined the odds of being transplant wait-listed for a cross-section of 1323 hemodialysis patients in the United States in 2000. Furthermore, kidney transplant wait-listing was determined in 12 countries from cross-sectional samples of DOPPS II hemodialysis patients in 2002 to 2003 (N= 4274). RESULTS: Transplantation rates varied widely, from very low in Japan to 25-fold higher in the United States and 75-fold higher in Spain (both P values <0.0001). Factors associated with higher rates of transplantation included younger age, nonblack race, less comorbidity, fewer years on dialysis, higher income, and higher education levels. The likelihood of being wait-listed showed wide variation internationally and by United States region but not by for-profit dialysis unit status within the United States. CONCLUSION: DOPPS I and II confirmed large variations in kidney transplantation rates by country, even after adjusting for differences in case mix. Facility size and, in the United States, profit status, were not associated with varying transplantation rates. International results consistently showed higher transplantation rates for younger, healthier, better-educated, and higher income patients. 3. Patzer RE, Plantinga L, Krisher J, Pastan SO. Dialysis facility and network factors associated with low kidney transplantation rates among United States dialysis facilities. Am J Transplant. 2014 Jul; 14(7):1562-72. Abstract: Variability in transplant rates between different dialysis units has been noted, yet little is known about facility-level factors associated with low standardized transplant ratios (STRs) across the United States End-stage Renal Disease (ESRD) Network regions. We analyzed Centers for Medicare & Medicaid Services Dialysis Facility Report data from 2007 to 2010 to examine facility-level factors associated with low STRs using multivariable mixed models. Among 4098 dialysis facilities treating 305 698 patients, there was wide variability in facility-level STRs across the 18 ESRD Networks. Four-year average STRs ranged from 0.69 (95% confidence interval [CI]: 0.64-0.73) in Network 6 (Southeastern Kidney Council) to 1.61 (95% CI: 1.47-1.76) in Network 1 (New England). Factors significantly associated with a lower STR (p < 0.0001) included for-profit status, facilities with higher percentage black patients, patients with no health insurance and patients with diabetes. A greater number of facility staff, more transplant centers per 10 000 ESRD patients and a higher percentage of patients who were employed or utilized peritoneal dialysis were associated with higher STRs. The lowest performing dialysis facilities were in the Southeastern United States. Understanding the modifiable facility-level factors associated with low transplant rates may inform interventions to improve access to transplantation.

Measure Specifications

• NQF Number (if applicable): 0
  
  
• Description: This measure estimates hospital-level, risk-standardized mortality rate (RSMR) for Medicare fee-for-service (FFS) patients who are between the ages of 65 and 94. Death is defined as death from any cause within 30 days after the index admission date. This is a claims-based version of the Hybrid Hospital-Wide All-Cause Risk Standardized Mortality Measure.
  
  
• Numerator: This outcome measure does not have a traditional numerator and denominator. We use this field to define the measure outcome. The outcome for this measure is 30-day all-cause mortality. Mortality is defined as death for any reason within 30 days after the index admission date, including in-hospital deaths.
  
  
• Denominator: The cohort includes inpatient admissions for patients aged 65-94 years old, with a complete claims history for the 12 months prior to admission. If a patient has more than one admission in a year, one hospitalization is randomly selected for inclusion in the measure. Cohort includes index admissions for patients: - Who have not been transferred from another inpatient facility - Admitted for acute care (does not include principle discharge diagnosis of psychiatric disease, or rehabilitation care - Not enrolled in the Medicare hospice program at any time in the 12 months prior to the index admission, including the first day of the index admission - Without a principal diagnosis of cancer and also enrolled in Medicare hospice program during their index admission - Without any diagnosis of metastatic cancer - Not enrolled in the Medicare hospice program during admission or at discharge who die within two days of admission, or whose length of stay was under two days - Without a principal discharge diagnosis of a condition which hospitals have limited ability to influence survival, including anoxic brain damage (ICD-9 3481), persistent vegetative state (ICD-9 78003), prion diseases such as Creutzfeldt-Jakob disease (ICD-9 04619), Cheyne-Stokes respiration (ICD-9 78604), brain death (ICD-9 34882), respiratory arrest (ICD-9 7991), or cardiac arrest (ICD-9 4275) without a secondary diagnosis of acute myocardial infarction.
  
  
• Exclusions: The measure excludes admissions for patients: - With inconsistent or unknown vital status or other unreliable data - Discharged against medical advice - Admissions for crush injury (CCS 234), burn (CCS 240), intracranial injury (CCS 233) or spinal cord injury (CCS 227) - With a principle discharge diagnosis within a CCS with fewer than 100 admissions in that division within the measurement year.
  
  
• HHS NQS Priority: Patient and Family Engagement; Making Care Safer; Communication and Care Coordination; Effective Prevention and Treatment
  
  
• HHS Data Source:
  
  
• Measure Type: Outcome
  
  
• Steward: Centers for Medicare & Medicaid Services
  
  
• Endorsement Status: Never Submitted
  
  
• Is the measure specified as an electronic clinical quality measure? No
  
  

Summary of Workgroup Deliberations

• Workgroup Recommendation: Conditional Support for Rulemaking
  
  
• Workgroup Rationale: The MAP conditionally supported MUC17-195 for the IQR program pending NQF review and endorsement. MAP noted that this is an important measure for patient safety and that this measure could help reduce deaths due to medical errors. MAP did raise a number of potential concerns about the measure that should be vetted through the endorsement process, specifically that the measure has appropriate clinical and social risk factors in its risk adjustment model and addresses necessary exclusions. MAP noted that appropriate risk adjustment and exclusions are necessary to ensure the measure does not disproportionately penalize facilities who may see more complex patients (e.g. academic medical centers or safety net providers) or who may have smaller volumes of patients (e.g. rural providers or critical access hospitals). MAP also raised concerns about potential unintended consequences such as delayed referrals to hospice or palliative care or increased rates of unnecessary interventions at the end of a person's life. Finally, MAP noted some implementation concerns about this measure and suggested that condition specific mortality measures may be more actionable for providers and provide more detailed information to support consumer decision making.
  
  
• Public comments received: 8

Rationale for measure provided by HHS
Hospital-wide mortality has been the focus of several previous quality reporting initiatives in the U.S. and other countries. Prior efforts have met with some success and various challenges. Through our environmental scan and literature review, we identified multiple hospital-wide mortality measures reported at the state-level, and several at the health-system level. There is no hospital-wide mortality measure reported at the national-level in the United States.

Measure Specifications

• NQF Number (if applicable): 0
  
  
• Description: This measure estimates hospital-level, risk-standardized mortality rate (RSMR) for Medicare fee-for-service (FFS) patients who are between the ages of 65 and 94. Death is defined as death from any cause within 30 days after the index admission date. The measure is referred to as a hybrid because it will use Medicare fee-for-service (FFS) administrative claims to derive the cohort and outcome, and claims and clinical electronic health record (EHR) data for risk adjustment.
  
  
• Numerator: This outcome measure does not have a traditional numerator and denominator. We use this field to define the measure outcome. The outcome for this measure is 30-day all-cause mortality. Mortality is defined as death for any reason within 30 days after the index admission date, including in-hospital deaths.
  
  
• Denominator: The cohort includes inpatient admissions for patients aged 65-94 years old, with a complete claims history for the 12 months prior to admission. If a patient has more than one admission in a year, one hospitalization is randomly selected for inclusion in the measure. Cohort includes index admissions for patients: - Who have not been transferred from another inpatient facility - Admitted for acute care (does not include principle discharge diagnosis of psychiatric disease, or rehabilitation care - Not enrolled in the Medicare hospice program at any time in the 12 months prior to the index admission, including the first day of the index admission - Without a principal diagnosis of cancer and also enrolled in Medicare hospice program during their index admission - Without any diagnosis of metastatic cancer - Not enrolled in the Medicare hospice program during admission or at discharge who die within two days of admission, or whose length of stay was under two days - Without a principal discharge diagnosis of a condition which hospitals have limited ability to influence survival, including anoxic brain damage (ICD-9 3481), persistent vegetative state (ICD-9 78003), prion diseases such as Creutzfeldt-Jakob disease (ICD-9 04619), Cheyne-Stokes respiration (ICD-9 78604), brain death (ICD-9 34882), respiratory arrest (ICD-9 7991), or cardiac arrest (ICD-9 4275) without a secondary diagnosis of acute myocardial infarction.
  
  
• Exclusions: The measure excludes admissions for patients: - With inconsistent or unknown vital status or other unreliable data - Discharged against medical advice - Admissions for crush injury (CCS 234), burn (CCS 240), intracranial injury (CCS 233) or spinal cord injury (CCS 227) - With a principle discharge diagnosis within a CCS with fewer than 100 admissions in that division within the measurement year.
  
  
• HHS NQS Priority: Patient and Family Engagement; Making Care Safer; Communication and Care Coordination; Effective Prevention and Treatment
  
  
• HHS Data Source:
  
  
• Measure Type: Outcome
  
  
• Steward: Centers for Medicare & Medicaid Services
  
  
• Endorsement Status: Never Submitted
  
  
• Is the measure specified as an electronic clinical quality measure? Yes
  
  

Summary of Workgroup Deliberations

• Workgroup Recommendation: Conditional Support for Rulemaking
  
  
• Workgroup Rationale: MAP conditionally supported MUC17-196 pending NQF review and endorsement. MAP noted that this is an important measure for patient safety and that this measure could help address deaths due to medical errors. MAP did raise a number of potential concerns about the measure that should be vetted through the endorsement process, specifically that the measure has appropriate clinical and social risk factors in its risk adjustment model and addresses necessary exclusions. MAP noted that appropriate risk adjustment and exclusions are necessary to ensure the measure does not disproportionately penalize facilities who may see more complex patients (e.g. academic medical centers or safety net providers) or who may have smaller volumes of patients (e.g. rural providers or critical access hospitals). MAP also raised concerns about potential unintended consequences such as delayed referrals to hospice or palliative care or increased rates of unnecessary interventions at the end of a person's life. Finally, MAP noted some implementation concerns about this measure and suggested that condition specific mortality measures may be more actionable for providers and provide more detailed information to support consumer decision making. MAP noted this measures used EHR data to support additional factors in the risk adjustment model. Given the variability in EHR systems MAP recommended that the standing committee reviewing the measure pay special attention to the ability to consistently obtain EHR data across hospitals. MAP also recommended that CMS implement this measure with a voluntary reporting period to allow provides to test the extraction of electronic data elements.
  
  
• Public comments received: 6

Rationale for measure provided by HHS
Hospital-wide mortality has been the focus of several previous quality reporting initiatives in the U.S. and other countries. Prior efforts have met with some success and various challenges. Through our environmental scan and literature review, we identified multiple hospital-wide mortality measures reported at the state-level, and several at the health-system level. There is no hospital-wide mortality measure reported at the national-level in the United States.

Measure Specifications

• NQF Number (if applicable): 0
  
  
• Description: This measure will assess opioid related adverse respiratory events (ORARE) in the hospital setting. The goal for this measure is to assess the rate at which naloxone is given for opioid related adverse respiratory events that occur in the hospital setting, using a valid method that reliably allows comparison across hospitals.
  
  
• Numerator: Number of admissions with documentation of any of the following criteria for defining ORARE: administration of narcotic antagonist (i.e., IV naloxone), unless administered during or within 2 hours following a procedure, OR respiratory stimulant (i.e., doxapram) all within 24 hours of opioid administration, over a 12-month period.
  
  
• Denominator: The cohort will include all discharges of adult patients (age on admission 18 years or older) occurring within a 12-month measurement period.
  
  
• Exclusions: None
  
  
• HHS NQS Priority: Making Care Safer
  
  
• HHS Data Source:
  
  
• Measure Type: Outcome
  
  
• Steward: Centers for Medicare & Medicaid Services
  
  
• Endorsement Status: Never Submitted
  
  
• Is the measure specified as an electronic clinical quality measure? Yes
  
  

Summary of Workgroup Deliberations

• Workgroup Recommendation: Refine and Resubmit Prior to Rulemaking
  
  
• Workgroup Rationale: MAP recommended that this measure be revised and resubmitted prior to rulemaking. MAP raised concerns that this measure has not been tested in enough facilities to assess measure reliability across hospitals. As the developer completes testing of the measure, MAP asked that the measure developer consider the impact of chronic opioid users and patients receiving Suboxone (buprenorphine and naloxone). MAP noted that the completed testing demonstrate reliability and validity in the acute care setting and the measure has been submitted to NQF for review and endorsement. MAP recommended that the Patient Safety Standing Committee pay special attention to potential unintended consequences and noted there may be a need to balance this measure with measures assessing appropriate use of naloxone and adequate pain control.
  
  
• Public comments received: 6

Rationale for measure provided by HHS
Opiates are critical for the management of pain in hospitalized patients. However, known side effects can lead to serious adverse effects if opiate-treated patients are not properly managed. Many types of opioid related adverse respiratory events (respiratory depression, respiratory arrest, cardiopulmonary arrest, etc.) can potentially be measured electronically. Additionally, naloxone is a strong surrogate to serious adverse events after opiate administration in hospitals, and surveillance and care in administration can reduce adverse events1. Citations: 1 Lee LA, Caplan RA, Stephens LS, et al. Postoperative opioid-induced respiratory depression: a closed claims analysis. Anesthesiology. 2015;122(3):659-665. 2 Jha A, Pronovost P. Toward a safer health care system: The critical need to improve measurement. JAMA. May 3, 2016; 315(17):1831-1832. 3 Makary MA, Daniel M. Medical Error-the third leading cause of death in the US. BMJ. 2016; 353; i2139: 1-5; Available at: http://www.bmj.com/content/bmj/353/bmj.i2139.full.pdf

Measure Specifications

• NQF Number (if applicable): 514
  
  
• Description: This measure calculates the percentage of CT (computed tomography) or MRI (magnetic resonance imaging) studies of the lumbar spine with a diagnosis of low back pain on the imaging claim and for which the patient did not have prior claims-based evidence of antecedent conservative therapy. Antecedent conservative therapy may include: 1. Claim(s) for physical therapy in the 60 days preceding the lumbar spine CT or MRI. 2. Claim(s) for chiropractic evaluation and manipulative treatment in the 60 days preceding the lumbar spine CT or MRI. 3. Claim(s) for evaluation and management in the period > 28 days and < 60 days preceding the lumbar spine CT or MRI.
  
  
• Numerator: CT or MRI of the lumbar spine studies with a diagnosis of low back pain (from the denominator) without the patient having claims-based evidence of prior antecedent conservative therapy.
  
  
• Denominator: CT or MRI of the lumbar spine studies with a diagnosis of low back pain on the imaging claim.
  
  
• Exclusions: Indications for measure exclusion include any patients with diagnosis codes associated with: cancer, congenital spine & spinal cord malformations, human immunodeficiency virus (HIV), infectious conditions, inflammatory and autoimmune disorders, intraspinal abscess, intravenous drug abuse, lumbar spine surgery, neoplastic abnormalities, neurologic impairment, postoperative fluid collections and soft-tissue changes, spinal abnormalities associated with scoliosis, spinal cord infarctions, spinal vascular malformations, syringohydromyelia, treatment fields for radiation therapy, trauma, and unspecified immune deficiencies.
  
  
• HHS NQS Priority: Effective Prevention and Treatment
  
  
• HHS Data Source:
  
  
• Measure Type: Process/Overuse
  
  
• Steward: Centers for Medicare & Medicaid Services
  
  
• Endorsement Status: Failed Endorsement
  
  
• Is the measure specified as an electronic clinical quality measure? No
  
  

Summary of Workgroup Deliberations

• Workgroup Recommendation: Do Not Support for Rulemaking
  
  
• Workgroup Rationale: MAP did not support MUC17-223 for the HOQR program. MAP noted that this measure was not recommended for continued endorsement by the NQF Musculoskeletal Standing Committee in 2017. When reviewing this measure for endorsement maintenance, the Standing Committee agreed that it did not meet the validity subcriterion. The Standing Committee expressed a number of concerns including a potential misalignment between this measure being specified for Medicare Fee-for-Service beneficiaries and the inclusion of “elderly individuals "as one of the red-flag conditions in the Appropriate Use guidelines; the use of E&M visits as a proxy for antecedent conservative care as this may not capture all types of conservative care that cannot be captured in claims data (e.g. telephone visits, the use of OTC NSAIDs, acupuncture or massage) as well as concerns about coding and appropriate look back periods for exclusions.
  
  
• Public comments received: 1

Rationale for measure provided by HHS
The specifications for OP-8 are based primarily on the American College of Radiology’s Appropriateness Criteria® for low back pain. The 2015 publication of this Criteria® states that presentation of acute, subacute, or chronic uncomplicated low back pain or radiculopathy with no red flags and no prior management does not warrant imaging (using a CT or MRI). The Appropriateness Criteria® then details symptoms or diagnoses for which imaging may be appropriate, most of which are captured as measure exclusions for OP-8.

Summary of NQF Endorsement Review

• Year of Most Recent Endorsement Review: 2017
  
  
• Project for Most Recent Endorsement Review: Muscoloskeletal Off-Cycle Review
  
  
• Review for Importance: 1. Importance to Measure and Report: The measure meets the Importance criteria (1a. Evidence, 1b. Performance Gap) 1a. Evidence: Previous Evidence Evaluation Accepted; 1b. Performance Gap: H-3; M-10; L-0; I-0 Rationale: • The developer updated the evidence to include the 2015 American College of Radiology (ACR) Appropriateness Criteria: Low Back Pain. The Committee agreed that this was an appropriate update to the evidence and there was no need to re-discuss and re-vote on the evidence subcriterion. • To demonstrate opportunity for improvement, the developer provided an analysis of Medicare fee-for-service (FFS) claims data that indicates variation in the use of inappropriate MRI lumbar spine studies. Performance rates for July 2104 to June 2015 averaged 39.5% and ranged from 14.9% to 64.8% (NOTE: a lower rate is better). • Committee members noted that the performance gap data actually demonstrated a decrease in performance (from 32.5% in 2009 to 39.5% in 2014-2015). The developer indicated that this could be a result of a change in data sources that were used to compute performance scores. The developer also noted that changes in specifications over time make it difficult to interpret changes in performance across time (specifically, expanding the exclusions would decrease the measure denominator, but would not uniformly affect the measure result). • 2013 data presented by the developer showed that beneficiary age, gender, and race, as well as facility characteristics (i.e., number of beds, urban/rural locality, teaching status) were significantly associated with the rate of inappropriate MRI lumbar spine studies.
  
  
• Review for Scientific Acceptability: 2. Scientific Acceptability of Measure Properties: The measure does not meet the Scientific Acceptability criteria (2a. Reliability - precise specifications, testing; 2b. Validity - testing, threats to validity) 2a. Reliability: H-0; M-8; L-5; I-0 2b. Validity: H-0; M-3; L-9; I-1 Rationale: 17 • Committee members had several questions and concerns about the measure specifications, as follows: o The measure is specified for Medicare Fee-for-Service beneficiaries. However, “elderly individuals” is one of the red-flag conditions in the Appropriate Use guideline, indicating that imaging for the patients presenting with LBP may be appropriate. The developer interpreted the guideline as indicating that “elderly” should not be an independent indicator for imaging; however, some Committee members disagreed with this interpretation. o The measure uses evaluation and management (E&M) visits as a proxy for antecedent conservative care (in addition to claims for physical therapy or chiropractic visits). In general, the Committee agreed that the E&M visits are a reasonable proxy for some kinds of antecedent therapy, but questioned whether they would capture other types of antecedent therapy such as telephone encounters. Members noted that some types of antecedent conservative care (e.g., NSAIDs, Tylenol, massage therapy, acupuncture) cannot be captured in claims data. o Members questioned several of the look-back periods for some of the exclusions (e.g., 90 days for spine surgery, 12 months for cancer; 5 years for congenital spine and spinal cord malformations). For congenital malformations, the developer clarified that the 5- year look-back was mainly because of lack of access to historical data. o Committee members expressed concern that specific codes for neurological impairment, specifically those for which the evidence supports appropriate use of MRI, are not adequately captured in this measure. The developer agreed to look into the coding, but also noted that the red flag conditions often occur in tandem, meaning individual patients often are excluded from the measure due to several of the existing measure exclusions. Committee members noted that sciatica radiculopathy, typically does not present with other red-flag conditions. • The Committee expressed confusion about what changes, if any, have been made to the measure since the 2014 evaluation. Although the developer described the various analyses they performed (e.g., quantitative and qualitative evaluation of the look-back periods for several of the measure exclusions), it was still not clear to the Committee how the measure has been revised. Some of the confusion dates back to the 2014 evaluation, when the developer had actually added several exclusions to the measure that were not apparent in the submission materials considered by the Committee. • The developer presented updated score-level signal-to-noise reliability testing using 2013 data. Reliability scores from this analysis ranged from 22.4% to 86.6%, with a median reliability score of 44.9%. The median value was well below 0.7, which is often used as a rule-of-thumb minimal acceptable value, and lower than the 53.1% found in previous testing. The developer also provided, a couple of days prior to the evaluation webinar, another set of testing results. This new testing used a split-sample (or “test-retest”) approach to compare agreement in performance across hospitals. The intraclass correlation coefficient from this analysis was 0.59, which can be interpreted as moderate agreement (i.e., there is moderate consistency in performance within facilities). • The developer assessed the face validity of the measure score by surveying an 11-member Technical Expert Panel (TEP). They asked the TEP members to indicate whether the measure captures the most appropriate and prevalent types of antecedent conservative therapy available through claims data (8 of 11 said yes) and to indicate their agreement as to whether the 18 measure helps assess the inappropriate use of MRI lumbar-spine tests (9 of 11 agreed or strongly agreed). • The developer clarified that the intent of the measure is not to drive measure results to zero, but to decrease the number of orders for MRI on presentation of LBP and to reduce variation between facilities in inappropriate MRIs. • After much discussion, the Committee agreed that the measure did not pass the validity subcriterion and did not recommend the measure for endorsement.
  
  
• Endorsement Public Comments: 6. Public and Member Comment • NQF received five post-evaluation comments regarding this measure. (Note: One Musculoskeletal Standing Committee member submitted a comment.) Three of the commenters supported the decision of the Committee not to endorse the measure. Two of commenters supported the measure. • Commenters emphasized the importance of limiting unnecessary imaging for low back pain, but expressed concerns over the exclusions and the validity of the measure. • One commenter—the developer of the measure—formally requested a reconsideration of the validity subcriterion: “The Centers for Medicare & Medicaid Services (CMS) has requested a reconsideration of the National Quality Forum (NQF) Musculoskeletal Standing Committee’s decision not to recommend NQF #0514, MRI Lumbar Spine for Low Back Pain, for continued endorsement. NQF #0514 was originally endorsed by the Outpatient Imaging Efficiency 19 Steering Committee in October 2008; during the January 6, 2017 review webinar, it did not pass the Validity criterion. Based on NQF’s Measure Evaluation Criteria and Guidance, we believe that NQF #0514 aligns with the moderate validity recommendation from algorithm #3 (Guidance for Evaluating Validity), as it has received in prior evaluations for endorsement. The measure specifications are aligned with the most updated clinical practice guidelines and have strong face validity; additionally, measure testing confirms that threats to validity have been addressed by the exclusion of red-flag conditions. NQF #0514 also passed the Importance and Reliability criteria during endorsement maintenance review. As one Standing Committee member stated during the review webinar, there will always be exceptions in health care, and, as long as the rate of exceptions is low, performance scores will not be impacted and the measure serves its purpose; we believe that, as currently specified, the measure addresses the broader patterns of care”. • In addition, because it was unclear to the Committee what changes have been made to the measure since the 2014 review, the developer clarified that updates to the specifications include the addition of congenital spine/spinal cord malformations, inflammatory and autoimmune disorders, infectious conditions, spinal vascular malformations, spinal cord infarctions, effects from radiation, spinal abnormalities associated with scoliosis, syringohydromyelia, and postoperative fluid collections/soft tissue changes, all of which were added to the measure’s list of exclusions. NQF Post Comment Call • On the post-draft report comment call, the Committee reviewed the reconsideration request. The Committee agreed to reconsider the measure for endorsement. • The Committee again expressed concerns with using administrative claims data to identify use of antecedent conservative therapies, noting that many conservative modalities may not be captured, causing a real risk to the validity of the measure. • The Committee continued to question several of the look-back periods for some of the exclusions (e.g., 90 days for spine surgery, 12 months for cancer). • Committee members remained concerned that the specifications do not include certain diagnoses codes to account for several disease states (e.g., sciatica and radicular pain, and degenerative conditions). The developer stated that they have not received feedback from the measure’s TEP or external stakeholders that suggests these diagnoses should be excluded from the measure’s denominator, however, they welcomed the Committee’s feedback and will consider it as they continue to refine the measure during future annual updates. • After a full discussion and review of the request for reconsideration, the Committee ultimately agreed that the measure did not pass the validity subcriterion. Therefore, the measure was not recommended for endorsement. Vote Following Consideration of Public and Member Comments: Validity: H-; M-3; L-8; I-2
  
  
• Endorsement Committee Recommendation: Not Recommended for Endorsement
  
  

Measure Specifications

• NQF Number (if applicable): 3175
  
  
• Description: Percentage of adults with pharmacotherapy for opioid use disorder (OUD) who have at least 180 days of continuous treatment
  
  
• Numerator: Individuals in the denominator who have at least 180 days of continuous pharmacotherapy with a medication prescribed for OUD without a gap of more than seven days
  
  
• Denominator: Adults who had a diagnosis of OUD and at least one claim for an OUD medication
  
  
• Exclusions: There are no numerator or denominator exclusions
  
  
• HHS NQS Priority: Effective Prevention and Treatment
  
  
• HHS Data Source: Administrative claims (non-Medicare), Registry
  
  
• Measure Type: Process
  
  
• Steward: RAND Corporation
  
  
• Endorsement Status: Endorsed
  
  
• Changes to Endorsed Measure Specifications?: The MUC list indicates the measure has been modified from its endorsed version.
  
  
• Is the measure specified as an electronic clinical quality measure? No
  
  

Summary of Workgroup Deliberations

• Workgroup Recommendation: Conditional Support for Rulemaking
  
  
• Workgroup Rationale: MAP acknowledged the public health importance of measures that address opioid use disorder and noted the gap of measures in this area. However, MAP recognized that the current measure is specified and tested at the health plan and state level. MAP Conditionally Supports this measure with the condition that it is tested and endorsed at the clinician and clinician group level. MAP encourages the relevant Standing Committee to specifically evaluate the attribution method, reliability and validity of this measure at the individual clinician and practice level.
  
  
• Public comments received: 1

Rationale for measure provided by HHS
In this section, first we summarize the evidence from the systematic reviews and meta-analyses cited by the 2015 “VA/DoD clinical practice guideline for the management of substance use disorders” that support the recommendations related to pharmacotherapy for treatment of opioid use disorder. Following that, we present evidence in support of the measure definition: using a minimum of 6 months’ duration of pharmacotherapy, and no gaps of more than seven days. EVIDENCE CITED BY 2015 VA/DOD GUIDELINE SUPPORTING PHARMACOTHERAPY FOR TREATMENT OF OUD Mattick RP, Breen C, Kimber J, Davoli M. Buprenorphine maintenance versus placebo or methadone maintenance for opioid dependence. Cochrane Database Syst Rev. 2014;2:Cd002207. The results are based on 5430 patients in 31 RCTs. Fixed-dose studies of buprenorphine vs. placebo: “There is high quality of evidence that buprenorphine was superior to placebo medication in retention of participants in treatment at all doses examined. Specifically, buprenorphine retained participants better than placebo: at low doses (2 - 6 mg), 5 studies, 1131 participants, risk ratio (RR) 1.50; 95% confidence interval (CI) 1.19 to 1.88; at medium doses (7 - 15 mg), 4 studies, 887 participants, RR 1.74; 95% CI 1.06 to 2.87; and at high doses (≥ 16 mg), 5 studies, 1001 participants, RR 1.82; 95% CI 1.15 to 2.90. However, there is moderate quality of evidence that only high-dose buprenorphine (≥ 16 mg) was more effective than placebo in suppressing illicit opioid use measured by urinalysis in the trials, 3 studies, 729 participants, standardised mean difference (SMD) -1.17; 95% CI -1.85 to -0.49, notably, low-dose, (2 studies, 487 participants, SMD 0.10; 95% CI -0.80 to 1.01), and medium-dose, (2 studies, 463 participants, SMD -0.08; 95% CI -0.78 to 0.62) buprenorphine did not suppress illicit opioid use measured by urinalysis better than placebo.” Bao YP, Liu ZM, Epstein DH, Du C, Shi J, Lu L. A meta-analysis of retention in methadone maintenance by dose and dosing strategy. Am J Drug Alcohol Abuse. 2009;35(1):28-33. In univariate analyses, doses of MMT greater than or equal to 60 mg/day were associated with greater retention than doses less than 60 mg/day at 3-6 months (62.5% vs. 50.6%; p=0.0005) and 6-12 months (57.0% vs. 42.5%; p<0.0001). Flexible dosing was associated with greater retention than fixed dosing strategies at 3-6 months (61.0% vs. 49.9%; p=0.0007) and 6-12 months (61.7% vs. 45.9%; p<0.0001). In multilevel analyses (follow-up duration, dose, and dosing strategy), retention was greater with methadone doses ≥ 60 mg/day than with doses <60 mg/day (OR: 1.74, 95% CI: 1.43-2.11). Similarly, retention was greater with flexible-dose strategies than with fixed-dose strategies (OR: 1.72, 95% CI: 1.41-2.11). Mattick RP, Breen C, Kimber J, Davoli M. Methadone maintenance therapy versus no opioid replacement therapy for opioid dependence. Cochrane Database Syst Rev. 2009(3):Cd002209. The results are based on 1969 patients in 11 randomized clinical trials. “Methadone appeared statistically significantly more effective than non-pharmacological approaches in retaining patients in treatment and in the suppression of heroin use as measured by self report and urine/hair analysis (6 RCTs, RR = 0.66; 95% CI 0.56-0.78), but not statistically different in criminal activity (3 RCTs, RR=0.39; 95% CI 0.12-1.25) or mortality (4 RCTs, RR=0.48; 95% CI: 0.10-2.39).” Krupitsky E, Nunes EV, Ling W, Illeperuma A, Gastfriend DR, Silverman BL. Injectable extended-release naltrexone for opioid dependence: A double-blind, placebo-controlled, multicentre randomised trial. Lancet. Apr 30 2011;377(9776):1506-1513. The median proportion of weeks of confirmed abstinence was significantly higher in the naltrexone group than in the placebo group (90.0% for naltrexone vs. 35.0% for placebo; p=0.0002). The proportion of patients with total confirmed abstinence was higher in the naltrexone group than the placebo group (RR=1.58; 95% CI, 1.06 to 2.36; p=0.0224). Comparing clinical outcomes between the naltrexone and placebo groups yielded the following results: proportion of self-reported opioid-free days over the 24 weeks (99.2% for naltrexone vs. 60.4% for placebo; p=0.0004), mean change in opioid craving score from baseline (-10.1 for naltrexone vs. 0.7 for placebo; p<0.0001), number of days of retention (>168 days for naltrexone vs. 96 days for placebo; p=0·0042), and number of participants with positive naloxone challenge test (1 for naltrexone vs. 17 for placebo; p<0.0001). EVIDENCE SUPPORTING MEASURE DEFINITION We define treatment continuity as (1) receiving at least 180 days of treatment and (2) no gaps in medication use of more than 7 days. Our definition of minimum duration is based on the fact that the FDA registration trials for OUD drugs studied the effect of treatment over three to six months (US FDAa, undated; US FDAb, undated), and we have no evidence for effectiveness of shorter durations. In addition, several recommendations support a minimum six-month treatment period as the risk of relapse is the highest in the first 6-12 months after start of opioid abstinence (US FDAa, undated; US FDAb, undated; US DHHS, 2015). Longer treatment duration is associated with better outcomes compared to shorter treatments and the best outcomes have been observed among patients in long-term methadone maintenance programs (“Effective medical treatment of opiate addiction”, 1998; Gruber et al., 2008; Moos et al., 1999; NIDA, 1999; Ouimette et al., 1998; Peles et al., 2013). Studies with long-term follow-up suggest that ongoing pharmacotherapy is associated with improved odds of opioid abstinence (Hser et al., 2015; Weiss et al., 2015). We did not specify a maximum duration of treatment, as no upper limit for duration of treatment has been empirically established (US DHHS, 2015). We opted for using a treatment gap of more than seven days in our definition, given that the measure includes three active ingredients with different pharmacological profiles. There is substantial evidence for an elevated mortality risk immediately after treatment cessation (Cornish et al., 2010; Cousins et al., 2016; Davoli et al, 2007; Degenhardt et al., 2009; Gibson & Degenhardt, 2007;Pierce et al., 2016). Research suggests that methadone tolerance is lost after three days and this three-day threshold has been used in other observational methadone studies and in developing a United Kingdom treatment guideline which recommends revaluating patients for intoxication and withdrawal after a three-day methadone treatment gap (Cousins et al., 2016; Cousins et al., 2011; “Drug Misuse and Dependence--Guidelines on Clinical Management”, 1999). Across all of the medications, the mortality risk is highest in the first four weeks out of treatment, with many studies showing an increase in mortality in days 1-14 after treatment cessation. Citations Cornish R, Macleod J, Strang J, Vickerman P, Hickman M. Risk of death during and after opiate substitution treatment in primary care: prospective observational study in UK General Practice Research Database. Bmj. 2010;341:c5475. Cousins G, Teljeur C, Motterlini N, McCowan C, Dimitrov BD, Fahey T. Risk of drug-related mortality during periods of transition in methadone maintenance treatment: a cohort study. J Subst Abuse Treat 2011; 41: 252-60. Cousins G, Boland F, Courtney B, Barry J, Lyons S, Fahey T. Risk of mortality on and off methadone substitution treatment in primary care: a national cohort study. Addiction. 2016;111(1):73-82. Davoli M, Bargagli AM, Perucci CA, et al. Risk of fatal overdose during and after specialist drug treatment: the VEdeTTE study, a national multisite prospective cohort study. Addiction. 2007;102:1954-9. Degenhardt L, Randall D, Hall W, Law M, Butler T, Burns L. Mortality among clients of a state-wide opioid pharmacotherapy program over 20 years: risk factors and lives saved. Drug and alcohol dependence. 2009;105:9-15. “Drug Misuse and Dependence--Guidelines on Clinical Management.” Scottish Office Department of Health, Welsh Office, Social Services Northern Ireland. London: Stationery Office, 1999. Effective medical treatment of opiate addiction. National Consensus Development Panel on Effective Medical Treatment of Opiate Addiction. JAMA.1998;280:1936-1943. Gibson AE, Degenhardt LJ. Mortality related to pharmacotherapies for opioid dependence: a comparative analysis of coronial records. Drug Alcohol Rev. 2007; 26(4), 405-410. Gruber VA, Delucchi KL, Kielstein A, Batki SL. A randomized trial of 6-month methadone maintenance with standard or minimal counseling versus 21-day methadone detoxification. Drug and Alcohol Dependence. 2008;94(1-3):199-206. Hser YI, Evans E, Grella C, Ling W, Anglin D. Long-term course of opioid addiction. Harvard Review of Psychiatry. 2015;23(2):76-89. Moos RH, Finney JW, Ouimette PC, Suchinsky RT. A comparative evaluation of substance abuse treatment: I. Treatment orientation, amount of care, and 1-year outcomes. Alcohol Clin Exp Res. 1999;23(3):529-36. National Institute on Drug Abuse (NIDA). Principles of Drug Addiction Treatment: A Research-Based Guide. NIH Publication No. 99-4180. Rockville, MD: NIDA, 1999, reprinted 2000 Ouimette PC, Moos RH, Finney JW. Influence of outpatient treatment and 12-step group involvement on one-year substance abuse treatment outcomes. J Stud Alcohol. 1998;59:513-522 Peles E, Schreiber S, Adelson M. Opiate-dependent patients on a waiting list for methadone maintenance treatment are at high risk for mortality until treatment entry. J Addict Med. 2013;7(3):177-82. Pierce M, Bird SM, Hickman M, Marsden J, Dunn G, Jones A, et al. Impact of treatment for opioid dependence on fatal drug-related poisoning: a national cohort study in England. Addiction. 2016;111:298-308. U.S. Department of Health and Human Services Assistant Secretary for Planning and Evaluation Office of Disability, Aging and Long-Term Care Policy. Review of Medication-Assisted Treatment Guidelines and Measures for Opioid and Alcohol Use. Washington, DC, 2015. Accessed November 9, 2016 at: https://aspe.hhs.gov/sites/default/files/pdf/205171/MATguidelines.pdf U.S. Food and Drug Administration (FDA) (a). REVIA Label. Accessed November 24, 2016 at: http://www.accessdata.fda.gov/drugsatfda_docs/label/2013/018932s017lbl.pdf U.S. Food and Drug Administration (FDA) (b). VIVITROL Label. Accessed November 24, 2016 at: http://www.accessdata.fda.gov/drugsatfda_docs/label/2006/021897lbl.pdf Weiss RD; Potter JS; Griffin ML, et al. Long-term outcomes from the National Drug Abuse Treatment Clinical Trials Network Prescription Opioid Addiction Treatment Study. Drug and Alcohol Dependence. 2015;150:112-119. EVIDENCE SUPPORTING USE OF 7-DAY GAP IN MEASURE DEFINITION We performed a review of studies that looked at the mortality risk during treatment cessation for OUD pharmacotherapy. All of the studies found evidence for increased mortality during treatment cessation and the results were consistent for the different MAT drugs. For Buprenorphine, we found two studies that both indicated an increased risk of mortality upon treatment cessation (Cornish et al., 2010; Degenhardt et al., 2009). For Methadone, we found four studies that all indicated an increased risk of mortality upon treatment cessation (Cornish et al., 2010; Cousins et al., 2016; Davoli et al., 2007; Degenhardt et al., 2009). For Methadone/Buprenorphine, we found two studies that both indicated an increased risk of mortality upon treatment cessation (Cornish et al., 2010; Pierce et al., 2016). For Naltrexone, we found one study that indicated an increased risk of mortality upon treatment cessation (Gibson & Degenhardt , 2007). Across all the medications, the mortality risk is highest in the first four weeks out of treatment, with many studies showing an increase in mortality in days 1-14 after treatment cessation. This evidence supports the recommendation for no gaps in care of more than 7 days. Citations Cornish R, Macleod J, Strang J, Vickerman P, Hickman M. Risk of death during and after opiate substitution treatment in primary care: prospective observational study in UK General Practice Research Database. Bmj. 2010;341:c5475. Cousins G, Boland F, Courtney B, Barry J, Lyons S, Fahey T. Risk of mortality on and off methadone substitution treatment in primary care: a national cohort study. Addiction. 2016;111(1):73-82. Davoli M, Bargagli AM, Perucci CA, et al. Risk of fatal overdose during and after specialist drug treatment: the VEdeTTE study, a national multisite prospective cohort study. Addiction. 2007;102:1954-9. Degenhardt L, Randall D, Hall W, Law M, Butler T, Burns L. Mortality among clients of a state-wide opioid pharmacotherapy program over 20 years: risk factors and lives saved. Drug and alcohol dependence. 2009;105:9-15. Gibson AE, Degenhardt LJ. Mortality related to pharmacotherapies for opioid dependence: a comparative analysis of coronial records. Drug Alcohol Rev. 2007; 26(4), 405-410. Pierce M, Bird SM, Hickman M, Marsden J, Dunn G, Jones A, et al. Impact of treatment for opioid dependence on fatal drug-related poisoning: a national cohort study in England. Addiction. 2016;111:298-308.

Summary of NQF Endorsement Review

• Year of Most Recent Endorsement Review: 2017
  
  
• Project for Most Recent Endorsement Review: Behavioral Health 2016-2017
  
  
• Review for Importance: 1a. Evidence: 1b. Performance Gap, 1c. High Priority) 38 1a. Evidence: H: 3; M-10; L-0; I-5; 1b. Performance Gap: H-5; M-11; L-1; I-1 Rationale: • The developer provided guidelines on the management of substance use disorders (VA/DoD 2015). In addition, they cited evidence showing the increased mortality associated with interruption of medication, with highest risks being in the first few weeks after stopping the medication. • One Committee member noted an article not included in this submission from the New England Journal of Medicine in March 2016 on Vivitrol that looked at the efficacy of Vivitrol. • The developer also provided evidence on reasoning for choice of 6-month continuation (based on FDA trial lengths) and 7-day gap (drug effectiveness and mortality risk following interruption of medication). The developer noted there is no empirical evidence on the best length of time overall for patients to stay on these medications, and suggests this as a needed area of research. • The Committee noted the gaps in performance, with mean performance in 2014-2015 of 27.7 percent, (10th percentile at 16.2 percent and 90th percentile at 40.9 percent).
  
  
• Review for Scientific Acceptability: 2a. Reliability - precise specifications, testing; 2b. Validity -testing, threats to validity) 2a. Reliability: H-0; M-15; L-2; I-2; 2b. Validity: M-14; L-2; I-3 Rationale: • The Committee had extensive discussions about how the measure was specified –in particular, they expressed concern about the measure capturing individuals who are appropriately discontinuing their medication, as the measure cannot tell which patients have been on medication for years. The Committee asked why the measure was not specified to only look at those who had just initiated treatment. The developer acknowledged this could lead to some measurement error, but they expected this to only be a small number. The developer said they made the choice to err on the side ofsensitivity over specificity in order to be more generalizable and look at a cross-section of patients, given that the performance gap is so large. The developer also noted that the measure has a rolling 2-year timeframe. Furthermore, the developer noted that it can be difficult to identify those who have been on medications long term in commercial insurance because individuals can change plans over time. • One Committee member expressed concern that the measure could encourage providers to keep patients on their medications unnecessarily. • The Committee also questioned why the measure does not include counseling in conjunction with medication. The developer cited issues with defining counseling, and the ability to capture all types of counseling (e.g., community-based support groups). The Committee suggested in the future the measure might be expanded to set a minimum standard for the occurrence of any type of counseling. • The Committee asked why the measure had only been tested in the commercial insurance pool. The developer noted timeline constraints to submit the measure for consideration, but stated they intend to conduct testing in both the Medicare and Medicaid populations. • The developer provided a signal-to-noise analysis showing reliability rates of 0.977 at the state level and 0.891 at the health plan level. 39 • The Committee noted the face validity testing of the measure score resulted in eight of 10 experts in agreement that the measure can be used to distinguish good quality from poor quality. • The Committee had several suggestions for improvements to the measure’s specifications in the future including: o Expansion of the patient pool (e.g., Medicare, Medicaid). o Stratification of the data for patients who have just initiated medication and those who have been on medication for a longer time. o Addition of a counseling component.
  
  
• Review for Feasibility: 3. Feasibility: H-8; M-10; L-1; I-0 (3a. Data generated during care; 3b. Electronic sources; and 3c. Data collection can be implemented (eMeasure feasibility assessment of data elements and logic) Rationale: • The Committee noted that the data are readily available in electronic form and no issues have been reported in testing.
  
  
• Review for Usability: 4. Use and Usability: H-1; M-11; L-5; I-2 (4a. Accountability/transparency; and 4b. Improvement –progress demonstrated; and 4c. Benefits outweigh evidence of unintended negative consequences) Rationale: • The Committee strongly recommended that the measure not be used in pay-for-performance programs initially.
  
  
• Review for Related and Competing Measures: 5. Related and Competing Measures • This measure relates to NQF #0004: Initiation and Engagement of Alcohol and Other Drug Dependence Treatment (IET). NQF #0004 was discussed with the Committee in October 2016, and discussions around harmonization have been deferred until after an update is available. • This measure relates to NQF #1664: SUB-3 Alcohol & Other Drug Use Disorder Treatment Provided or Offered at Discharge and SUB-3a Alcohol & Other Drug Use Disorder Treatment at Discharge, a facility-level measure for the hospital setting. There are minor differences that may be considered for harmonization, but the Committee decided to table discussion.
  
  
• Endorsement Public Comments: 6. Public and Member Comment: • This measure received three comments. One comment supported the endorsement of the measure and two comments raised concerns around the endorsement of the measure at the health plan level and failure to distinguish between dangerous non-therapeutic MAT- 40 discontinuation and appropriate, planned Opioid Substitution Treatment (OST) tapers (e.g., discontinuation of Vivitrol, naltrexone for extended-release injectable suspension). • The developer of this measure also provided additional information and testing data based on Medicaid claims from national databases in response to the Committee’s request for this information during the in-person meeting.
  
  
• Endorsement Committee Recommendation: Standing Committee Recommendation for Endorsement: Y-12; N-7 • The Committee clarified that they were voting on the measure as it stands, and not considering potential updates as previously suggested (e.g., stratification of new users, addition of counseling).
  
  

Measure Specifications

• NQF Number (if applicable): 2643
  
  
• Description: For patients age 18 and older undergoing lumbar spine fusion surgery, the average change from pre-operative functional status to one year (nine to fifteen months) post-operative functional status using the Oswestry Disability Index (ODI version 2.1a) patient reported outcome tool.
  
  
• Numerator: The average change (preoperative to one year post-operative) in functional status for all patients in the denominator. There is not a traditional numerator for this measure; the measure is calculating the average change in functional status score from pre-operative to post-operative functional status score. The measure is NOT aiming for a numerator target value for a post-operative ODI score. The average change is calculated as follows: Change is first calculated for each patient and then changed scores are summed and then an average is determined. Measure calculation takes into account those patients that have an improvement and those patients whose function decreases post-operatively. Example below: Patient Pre-op ODI :I Post-op ODI :I Change in ODI Patient A: I 47 :I 18 :I 29 Patient B: I 45 :I 52 :I -7 Patient C: I 56 :I 12 :I 44 Patient D: I 62 :I 25 :I 37 Patient E: I 42 :I 57 :I -15 Patient F: I 51 :I 10 :I 41 Patient G: I 62 :I 25 :I 37 Patient H: I 43 :I 20 :I 23 Patient I: I 74 :I 35 :I 39 Patient J: I 59 :I 23 :I 36 Average change in ODI one year post-op 26.4 points on a 100 point scale
  
  
• Denominator: Eligible Population: Patients with lumbar spine fusion procedures (Arthrodesis Value Set) occurring during a 12 month period for patients age 18 and older at the start of that period. Denominator: Patients within the eligible population whose functional status was measured by the Oswestry Disability Index, version 2.1a (ODI, v2.1a) within three months preoperatively AND at one year (+/- 3 months) postoperatively. * The measure of average change in function can only be calculated if both a pre-operative and post-operative PRO assessment are completed
  
  
• Exclusions: The following exclusions must be applied to the eligible population: Patient had cancer (Spine Cancer Value Set), fracture (Spine Fracture Value Set) or infection (Spine Infection Value Set) related to the spine. Patient had idiopathic or congenital scoliosis (Congenital Scoliosis Value Set)
  
  
• HHS NQS Priority: Patient and Family Engagement
  
  
• HHS Data Source: Administrative clinical data, EHR, Paper medical record, Registry
  
  
• Measure Type: Patient Reported Outcome
  
  
• Steward: MN Community Measurement
  
  
• Endorsement Status: Endorsed
  
  
• Changes to Endorsed Measure Specifications?: The MUC list indicates the measure has been modified from its endorsed version.
  
  
• Is the measure specified as an electronic clinical quality measure? No
  
  

Summary of Workgroup Deliberations

• Workgroup Recommendation: Support for Rulemaking
  
  
• Workgroup Rationale: MAP supports this change in functional status following lumbar spine fusion surgery patient reported outcome measure.
  
  
• Public comments received: 1

Rationale for measure provided by HHS
Patient Reported Outcome Measures and Integration Into Electronic Health Records Pitzen, C. et al, Journal of Oncology Practice DOI: 10.1200/JOP.2016.014118; published online ahead of print at jop.ascopubs.org on July 26, 2016.

Summary of NQF Endorsement Review

• Year of Most Recent Endorsement Review: 2016
  
  
• Project for Most Recent Endorsement Review: Person and Family-Centered Care Phase 2
  
  
• Review for Importance: 1a. Evidence: 1b. Performance Gap, 1c. High Priority) 1a. Evidence: Y-18; N-1; 1b. Performance Gap: H-6; M-8; L-0; I-5 1c. High Priority: H-13; M-6; L-0; I-0 Rationale: • The developer introduced this new measure as a patient-reported outcome measure, which evaluates the change between a patient’s preoperative functional status and their postoperational functional status at one year. • The Committee applauded the developers for tackling this controversial and important area in utilization of surgical procedures, pointing to the developer’s statement that there is a 15-fold increase in the number of complex fusion procedures performed for Medicare beneficiaries, which is a highly variable procedure. However, the Committee stated that this measure could imply that there is a gap in quality of care, but not a gap in variability in performance, based on the pilot data. • The developer explained that this measure has gone through one phase of pilot testing involving four practices and is in the statewide quality reporting and measurement system for Minnesota, which is required of practitioners. The developer noted that they are expecting full implementation data to be available in May, 2015. • The Committee members raised additional concerns that the Oswestry tool (pain questionnaire) may not be the best tool to use, because it is primarily aimed at pain and therefore would not capture other neurological dysfunctions or potential side effects of the surgery itself. They recommended that the measure be improved by adding other questions or tools that might speak to neurological symptoms that could present without pain.
  
  
• Review for Scientific Acceptability: 2a. Reliability - precise specifications, testing; 2b. Validity - testing, threats to validity) 2a. Reliability: H-0; M-6; L-4; I-9 2b. Validity: H-X; M-X; L-X; I-X UPDATED VOTES FOR 2a. Reliability: H-3; M-15; L-0; I-0 2b. Validity: H-1; M-17; L-0; I-0 Rationale: • The Committee members commented that the specifications look very clear but the risk adjustment specifications have not been modeled yet. Further, the Committee noted that there is no score-level reliability testing data presented as well as data to demonstrate the intraclass correlations at the practice-level. The developer confirmed that similar to Measure 2653 Average Change in Functional Status Following Total Knee Replacement Surgery, they will submit final analysis of a risk adjustment methodology based on the Committee’s recommendations. 90 • This measure did not pass reliability so the Committee stopped voting at this juncture and requested the aforementioned testing information from the developers to re-consider the measure after the public comment. One Committee member offered an additional suggestion for the developers to add questions such as whether or not non-invasive treatments were tried (e.g., physical therapy or pain consults, steroid injections) to get a sense for onset of symptoms, other treatments that were tried, and clinical indications for the procedure.
  
  
• Review for Feasibility: 3. Feasibility: H-3; M-14; L-1; I-0 (3a. Data generated during care; 3b. Electronic sources; and 3c. Data collection can be implemented (eMeasure feasibility assessment of data elements and logic) Rationale: • This measure data source is Electronic Clinical Data, Electronic Health Record, Paper Medical Records, and Patient Reported Data/Survey. • Also, all data elements are in defined fields in electronic health records (EHRs)
  
  
• Review for Usability: 4. Use and Usability: H-3; M-14; L-0; I-1 (4a. Accountability/transparency; and 4b. Improvement – progress demonstrated; and 4c. Benefits outweigh evidence of unintended negative consequences) Rationale: • This measure is not currently in use but planned for use in public reporting, payment program, and regulatory and accreditation programs. • The developer also noted that this measure is planned for inclusion in the MN Department of Health (MDH) Statewide Quality Reporting and Measurement System. Mandatory data collection and reporting under 2008 MN Health Reform Legislation. MNCM was a subcontractor to MDH for measure development exploring the concept of low back pain. Statewide implementation is planned for submission in April/May 2015 for dates of procedure 1/1/2013 to 12/31/2013 with follow-up assessment period through March 31, 2015.
  
  
• Review for Related and Competing Measures: 5. Related and Competing Measures • No related or competing measures noted.
  
  
• Endorsement Public Comments: 6. Public and Member Comment Comments received: • Commenters believed this measure should be considered for endorsement once the reliability testing data is submitted by Minnesota Community Measurement because the measure focuses on an important patient-centered outcome and addressees an important gap area for quality improvement. We believe an explicit patient-centered focus on surgical outcomes is necessary and this measure begins to address this important quality issue. Committee response: • The Committee requested additional information to allow for more comprehensive evaluation of the consensus not reached and not recommended measures. This additional information 91 was discussed on the post-comment committee call and the Committee had an opportunity to re-vote on the applicable measures. This measure was recommended by the Committee after reviewing the additional information and the comments. Developer response: • Thank you for your support! We agree that these types of measures focused on patient reported outcomes and change over time, which represent newer cutting-edge measures, are more difficult to evaluate as compared to traditional measures that are expressed as a binary Yes/No. We have provided additional testing in response to the Standing committee’s concerns and look forward to continued conversation and working with NQF staff to determine the best statistical methods and tests for determining the reliability and validity performance scores. A new published study supports the use of the Oswestry Disability Index as a PROM tool appropriate for outcome measurement. “A proposed set of metrics for standardized outcome reporting in the management of low back pain.” Clement, RC et al Acta Orthopaedica 2015; 86 (4)
  
  
• Endorsement Committee Recommendation: Standing Committee Recommendation for Endorsement: Y-18; N-0
  
  

Measure Specifications

• NQF Number (if applicable): 2653
  
  
• Description: For patients age 18 and older undergoing total knee replacement surgery, the average change from pre-operative functional status to one year (nine to fifteen months) post-operative functional status using the Oxford Knee Score (OKS) patient reported outcome tool.
  
  
• Numerator: There is not a traditional numerator for this measure; the measure is calculating the average change in functional status score from pre-operative to post-operative functional status score. The measure is NOT aiming for a numerator target value for a post-operative OKS score. For example: The average change in knee function was an increase of 15.9 points one year post-operatively on a 48 point scale. The average change is calculated as follows: Change is first calculated for each patient and then changed scores are summed and then an average is determined. Measure calculation takes into account those patients that have an improvement and those patients whose function decreases post-operatively. Example below: Patient Pre-op OKS :I Postop OKS :I Change in OKS Patient A: I 33 :I 45 :I 12 Patient B: I 17 :I 39 :I 22 Patient C: I 16 :I 31 :I 15 Patient D: I 23 :I 40 :I 17 Patient E: I 34 :I 42 :I 8 Patient F: I 10 :I 42 :I 32 Patient G: I 14 :I 44 :I 30 Patient H: I 32 :I 44 :I 12 Patient I: I 19 :I 45 :I 26 Patient J: I 26 :I 19 :I -7 Patient K: I 24 :I 43 :I 19 Patient L: I 29 :I 34 :I 5 Patient M : I 23 :I 39 :I 16 Patient N: I 29 :I 45 :I 16 Patient O: I 29 :I 45 :I 16 Patient P: I 34 :I 41 :I 7 Patient Q: I 11 :I 14 :I 3 Patient R: I 13 :I 39 :I 26 Patient S: I18 :I 45 :I 27 Average change in OKS one year post-op 15.9 points on a 48 point scale
  
  
• Denominator: Eligible Population: Patients with total knee replacement procedures (Primary TKR Value Set, Revision TKR Value Set) occurring during a 12 month period for patients age 18 and older at the start of that period. Denominator: Patients within the eligible population whose functional status was measured by the Oxford Knee Score within three months preoperatively AND at one year (+/- 3 months) postoperatively * The measure of average change in function can only be calculated if both a pre-operative and post-operative PRO assessment are completed
  
  
• Exclusions: None
  
  
• HHS NQS Priority: Patient and Family Engagement
  
  
• HHS Data Source: Administrative clinical data, EHR, Paper medical record, Registry
  
  
• Measure Type: Patient Reported Outcome
  
  
• Steward: MN Community Measurement
  
  
• Endorsement Status: Endorsed
  
  
• Changes to Endorsed Measure Specifications?: The MUC list indicates the measure has been modified from its endorsed version.
  
  
• Is the measure specified as an electronic clinical quality measure? No
  
  

Summary of Workgroup Deliberations

• Workgroup Recommendation: Support for Rulemaking
  
  
• Workgroup Rationale: MAP supports this change in functional status following total knee replacement patient reported outcome measure.
  
  
• Public comments received: 2

Rationale for measure provided by HHS
Patient-reported outcomes after total and unicompartmental knee arthroplasty: a study of 14,076 matched patients from the National Joint Registry for England and Wales. Liddle, AD et al Bone Joint J. 2015 Jun;97-B(6):793-801. doi: 10.1302/0301-620X.97B6.35155.

Summary of NQF Endorsement Review

• Year of Most Recent Endorsement Review: 2016
  
  
• Project for Most Recent Endorsement Review: Person and Family-Centered Care Phase 2
  
  
• Review for Importance: 1a. Evidence: Y-16; N-3; 1b. Performance Gap: H-3; M-12; L-3; I-1; 1c. Impact: H-8; M-9; L-2; I-0 Rationale: • The number of total knee replacements (TKR) is rising and will continue to rise over the next 10 years as the Baby Boom generation ages, especially because the standard of care for end-stage degenerative arthritis of the knee is knee arthroplasy. The Committee agreed that patients could use information on what level of functional status they can expect after a TKR. The Committee agreed that a one-year postoperative assessment was the right timeframe as much sooner would not accurately measure real outcomes. • The Committee requested more information on the effect of the measured improvement and whether is an amount that actually impacts outcomes. While the developers had not included that information in the submission form, a Committee member provided data from a different study that the standard deviation is 8, and so the difference noted in the measure would be very significant (two standard deviations). • The Committee was concerned that the measure did not collect data on postoperative interventions, such as rehabilitation, that could affect outcomes separately from the surgery and that could not be held attributable to the surgeon. However, one Committee member suggested that a surgeon should be seen as the leader of a team taking care of a knee and that this sort of measure would encourage more focus on long-term outcomes. • The developer confirmed that patients were involved in the measure development work group
  
  
• Review for Scientific Acceptability: 2a. Reliability: H-0; M-9; L-3; I-7 (consensus not reached) 2b. Validity: H-1; M-7; L-5; I-6 (consensus not reached) UPDATED VOTES FOR 2a. Reliability: H-2; M-13; L-3; I-0 2b. Validity: H-2; M-14; L-1; I-1 Rationale: • Committee members questioned why the measure is not risk adjusted; the developer explained that although a final risk adjustment strategy had not been submitted, upon the receipt of a full set of data they plan to test a number of variablesas potential adjustors. The developer utilizes a workgroup to advise on risk adjustment and a preliminary strategy has been developed and tested, but not yet finalized. • The Committee wanted to know how different the average patient population in Minnesota would be from the average patient across the US, and also raised concerns that the measure 70 was originally tested on a very different patient population, potentially affecting the reliability and validity of the instrument used. • The developer stated that this is a new type of measure that does not have a traditional numerator and denominator; it is a continuous measure. They stated that measurement science has not yet evolved to the point of determining appropriate methodology for testing reliability for this type of measure. The Committee suggested intraclass correlate testing as a possibility. • The developer mentioned they had some difficulty with the PRO tool administration rates and that they were working with a phased approach to improve those rates. • Committee members requested an estimation of reliability at the physician level and the developers agreed to follow up with that information.
  
  
• Review for Feasibility: 4. Feasibility: H-1; M-15; L-2; I-1 (4a. Clinical data generated during care delivery; 4b. Electronic sources; 4c.Susceptibility to inaccuracies/ unintended consequences identified 4d. Data collection strategy can be implemented) Rationale: • In response to a request for more information, the developers explained that the measure requires a pre-operative OKS summary score, using a simple tool. Practices submit patient-level information to a portal that calculates the measures. They noted that orthopedic practices are new to measurement, and that their pilot groups said getting information into and out of their EMRs was easier than getting the patient-reported tools into their workflows. However, they are seeing gradual improvements. • Post-operatively, the tool is filled out during an office visit or sent to the patient via mail or the patient portal. • Committee members asked if this measure is susceptible to gaming; the developers said that there are no appropriateness criteria guidelines for knee replacement but that they collect the data on all patients, whether they completed one or both assessments.
  
  
• Review for Usability: 3. Use and Usability: H-0; M-12; L-5; I-2 (Meaningful, understandable, and useful to the intended audiences for 3a. Public Reporting/Accountability and 3b. Quality Improvement) Rationale: • The measure is not currently in use, so no usability data is available, but the developer plans to report it statewide in Minnesota in 2016.
  
  
• Review for Related and Competing Measures: 5. Related and Competing Measures • The Committee considered whether this measure potentially competes with 0422: Functional Status Change for Patients with Knee Impairments (FOTO). The Committee determined that the measures have different focus in terms of the target population, provider types, and clinical settings, as well as the clinical area. The developers indicated that the FOTO measure is broader and applicable to any kind of knee impairments, as opposed to measure 2653, which only focuses on patients with knee replacements. Therefore, the Committee agreed that the measures were related but not competing. The Committee did not make recommendations for harmonization.
  
  
• Endorsement Public Comments: 6. Public and Member Comment: March 2, 2015- March 31, 2015 Comments received: • Commenters strongly urge the Committee to reconsider and recommend this measure. The measure is deemed by consumers and purchasers to be important for assessing providers of knee replacement surgery. This is a high frequency and high cost procedure, and currently there is no information that enables patients to choose providers that can achieve better outcomes as assessed by patients themselves. Therefore, this measure is a high priority for these users. Commenters also asked NQF to consider ways to improve upon the validity and reliability of this measure and other similar measures should be considered in the future. Committee response: • The Committee requested additional information to allow for more comprehensive evaluation of the consensus not reached and not recommended measures. This additional information was discussed on the post-comment committee call and the Committee had an opportunity to re-vote on the applicable measures. This measure was recommended by the Committee after reviewing the additional information and the comments. NQF response: • NQF has reviewed your comment and appreciates your input. Your comment has been forwarded to the Standing Committee and Developer for consideration. NQF is not able to improve measures as our role is to endorse measures, not maintain them, but we do encourage improvements to measures over time and at the three-year maintenance cycle review. Developer response: • Thank you for your support! We agree that these types of measures focused on patient reported outcomes and change over time, which represent newer cutting-edge measures, are more difficult to evaluate as compared to traditional measures that are expressed as a binary Yes/No. We have provided additional testing in response to the Standing committee’s concerns and look forward to continued conversation and working with NQF staff to determine the best statistical methods and tests for determining the reliability and validity performance scores. Thanks for your suggestion to determine modes that address survey burden. In addition to obtaining survey information from the patient during an in-person visit, we do allow mailed survey and when permitted by the tool developer/ copyright holder, electronic administration of the tool to the patient by patient portal. Additionally, although not yet submitted for endorsement, MN Community Measurement is also measuring the change in quality of life for this patient population, initially using the EQ5D and now transitioning to PROMIS Global Health-10. • We agree that these types of measures focused on patient reported outcomes and change over time, which represent newer cutting-edge measures, are more difficult to evaluate as compared to traditional measures that are expressed as a binary Yes/No. We have provided additional testing in response to the Standing committee’s concerns and look forward to continued conversation and working with NQF staff to determine the best statistical methods and tests for determining the reliability and validity performance scores.
  
  
• Endorsement Committee Recommendation: Standing Committee Recommendation for Endorsement: Y-11; N-8 (consensus not reached); UPDATED Y-15; N-3
  
  

Measure Specifications

• NQF Number (if applicable):
  
  
• Description: For patients age 18 and older undergoing lumbar discectomy laminotomy surgery, the average change from pre-operative functional status to three months (6 to 20 weeks) post-operative functional status using the Oswestry Disability Index (ODI version 2.1a) patient reported outcome tool.
  
  
• Numerator: The average change (preoperative to three months post-operative) in functional status for all patients in the denominator. There is not a traditional numerator for this measure; the measure is calculating the average change in functional status score from pre-operative to post-operative functional status score. The measure is NOT aiming for a numerator target value for a post-operative ODI score. The average change is calculated as follows: Change is first calculated for each patient and then changed scores are summed and then an average is determined. Measure calculation takes into account those patients that have an improvement and those patients whose function decreases post-operatively. Example below: Patient Pre-op ODI :I Post-op ODI :I Change in ODI Patient A: I 47 :I 18 :I 29 Patient B: I 45 :I 52 :I -7 Patient C: I 56 :I 12 :I 44 Patient D: I 62 :I 25 :I 37 Patient E: I 42 :I 57 :I -15 Patient F: I 51 :I 10 :I 41 Patient G: I 62 :I 25 :I 37 Patient H: I 43 :I 20 :I 23 Patient I: I 74 :I 35 :I 39 Patient J: I 59 :I 23 :I 36 Average change in ODI three months post-op 26.4 points on a 100 point scale
  
  
• Denominator: Eligible Population: Patients with lumbar discectomy laminotomy procedure (Single Disc-Lami Value Set) for a diagnosis of disc herniation (Disc Herniation Value Set)) occurring during a 12 month period for patients age 18 and older at the start of that period. Denominator: Patients within the eligible population whose functional status was measured by the Oswestry Disability Index, version 2.1a (ODI, v2.1a) within three months preoperatively AND at three months (6 to 20 weeks) postoperatively. * The measure of average change in function can only be calculated if both a pre-operative and post-operative PRO assessment are completed
  
  
• Exclusions: The following exclusions must be applied to the eligible population: Patient had any additional spine procedures performed on the same date as the lumbar discectomy laminotomy
  
  
• HHS NQS Priority: Patient and Family Engagement
  
  
• HHS Data Source: Administrative clinical data, EHR, Paper medical record, Registry
  
  
• Measure Type: Patient Reported Outcome
  
  
• Steward: MN Community Measurement
  
  
• Endorsement Status: Never Submitted
  
  
• Is the measure specified as an electronic clinical quality measure? No
  
  

Summary of Workgroup Deliberations

• Workgroup Recommendation: Conditional Support for Rulemaking
  
  
• Workgroup Rationale: MAP was encouraged to see this change in functional status following lumbar discectomy laminotomy surgery patient reported outcome measure. MAP Conditionally supported this measure with the condition that it is submitted to NQF for endorsement.
  
  
• Public comments received: 1

Rationale for measure provided by HHS
Patient Reported Outcome Measures and Integration Into Electronic Health Records Pitzen, C. et al, Journal of Oncology Practice DOI: 10.1200/JOP.2016.014118; published online ahead of print at jop.ascopubs.org on July 26, 2016.

Measure Specifications

• NQF Number (if applicable):
  
  
• Description: Percentage of female patients aged 50 to 64 without select risk factors for osteoporotic fracture who received an order for a dual-energy x-ray absorptiometry (DXA) scan during the measurement period.
  
  
• Numerator: Female patients who received an order for at least one DXA scan in the measurement period
  
  
• Denominator: Female patients ages 50 to 64 years with an encounter during the measurement period
  
  
• Exclusions: Exclude from the denominator patients with a combination of risk factors (as determined by age) or one of the independent risk factors: - Ages: 50-54 (>=4 combo risk factors) or 1 independent risk factor - Ages: 55-59 (>=3 combo risk factors) or 1 independent risk factor - Ages: 60-64 (>=2 combo risk factors) or 1 independent risk factor Combination risk factors (The following risk factors are all combination risk factors; they are grouped by when they occur in relation to the measurement period): The following risk factors may occur any time in the patient's history but must be active during the measurement period: - White (race) - BMI <= 20 kg/m2 (must be the first BMI of the measurement period) - Smoker (current during the measurement period) - Alcohol consumption (> two units per day (one unit is 12 oz. of beer, 4 oz. of wine, or 1 oz. of liquor)) The following risk factor may occur any time in the patient's history and must not start during the measurement period: - Osteopenia The following risk factors may occur at any time in the patient's history or during the measurement period: - Rheumatoid arthritis - Hyperthyroidism - Malabsorption syndromes: celiac disease, inflammatory bowel disease, ulcerative colitis, Crohn's disease, cystic fibrosis, malabsorption - Chronic liver disease - Chronic malnutrition The following risk factors may occur any time in the patient's history and do not need to be active at the start of the measurement period: - Documentation of history of hip fracture in parent - Osteoporotic fracture - Glucocorticoids (>= 5 mg/per day) [cumulative medication duration >= 90 days] Independent risk factors (The following risk factors are all independent risk factors; they are grouped by when they occur in relation to the measurement period): The following risk factors may occur at any time in the patient's history and must not start during the measurement period: - Osteoporosis The following risk factors may occur at any time in the patient's history prior to the start of the measurement period, but do not need to be active during the measurement period: - Gastric bypass - FRAX[R] 10-year probability of all major osteoporosis related fracture >= 9.3 percent - Aromatase inhibitors The following risk factors may occur at any time in the patient's history or during the measurement period: - Type I diabetes - End stage renal disease - Osteogenesis imperfecta - Ankylosing spondylitis - Psoriatic arthritis - Ehlers-Danlos syndrome - Cushings syndrome - Hyperparathyroidism - Marfan's syndrome - Lupus
  
  
• HHS NQS Priority: Making Care Affordable
  
  
• HHS Data Source: EHR (enter relevant parts in the field below)
  
  
• Measure Type: Process/Overuse
  
  
• Steward: Centers for Medicare & Medicaid Services
  
  
• Endorsement Status: Never Submitted
  
  
• Is the measure specified as an electronic clinical quality measure? Yes
  
  

Summary of Workgroup Deliberations

• Workgroup Recommendation: Conditional Support for Rulemaking
  
  
• Workgroup Rationale: This measure would addresses the inappropriate use of DXA scans for patients women age 50 – 64 years without risk factors for osteoporosis. MAP recognized the need for early detection of osteoporosis but reiterated the importance of appropriate use of this screening technique. MAP noted this measure could be complementary to the existing osteoporosis screening measure, QPP#039: Screening for Osteoporosis for Women Aged 65-85 Years of Age. MAP recognized the potential need for a balancing measure to prevent the potential underuse of DXA scans. MAP noted ideally one measure would address both the appropriate and inappropriate use of DXA scans. However, MAP recognized the potential challenges to developing such a measure. MAP recommends that this measure be Conditionally Supported with the condition of NQF endorsement. MAP also recommended the relevant NQF Standing Committee specifically consider the question of feasibility across EHRs.
  
  
• Public comments received: 0

Rationale for measure provided by HHS
Current osteoporosis guidelines recommend screening postmenopausal women younger than 65 for osteoporosis only if they meet a risk-factor profile. The risks for those under 65 that merit osteoporosis screening include, but are not limited to, previous osteoporotic fracture, osteoporosis, rheumatoid arthritis and other conditions associated with secondary osteoporosis, parental history of fractures, BMI less than 21 kg/m2, long-term use of glucocorticoids, current smoking, or excessive alcohol intake (USPSTF 2011). Although there is evidence to support the cost-effectiveness of DXA screening in women older than 65, there is not enough evidence to support screening women younger than 65 who do not meet a risk-factor profile (Lim et al. 2009). This measure is expected to increase recording of patient risks for fractures and decrease the number of inappropriate DXA scans. References Lim, L.S., L.J. Hoeksema, and K. Sherin. “Screening for Osteoporosis in the Adult U.S. Population: ACPM Position Statement on Preventive Practice.” American Journal of Preventive Medicine, vol. 36, no. 4, 2009, pp. 366-375. USPSTF. “Screening for Osteoporosis: U.S. Preventive Services Task Force Recommendation Statement.” Annals of Internal Medicine, vol. 154, no. 5, 2011, pp. 356-364.

Measure Specifications

• NQF Number (if applicable):
  
  
• Description: For patients age 18 and older undergoing lumbar spine fusion surgery, the average change from pre-operative leg pain to one year (nine to fifteen months) post-operative leg pain using the Visual Analog Scale (VAS) patient reported outcome tool.
  
  
• Numerator: The average change (preoperative to one year post-operative) in leg pain for all patients in the denominator. There is not a traditional numerator for this measure; the measure is calculating the average change in leg pain score from pre-operative to post-operative leg pain score. The measure is NOT aiming for a numerator target value for a post-operative pain score. The average change is calculated as follows: Change is first calculated for each patient and then changed scores are summed and then an average is determined. Measure calculation takes into account those patients that have an improvement and those patients whose pain increases post-operatively. Example below: Patient I: Pre-op VAS I: Post-op VAS I:(Pre-op minus Post-op) Patient A: I: 8.5 I: 3.5 I: 5.0 Patient B: I: 9.0 I: 2.5 I: 6.5 Patient C: I: 7.0 I: 0.5 I: 6.5 Patient D: I: 6.5 I: 8.0 I: -1.5 Patient E I: 8.5 I: 2.0 I: 6.5 Patient F I: 7.5 I: 1.5 I: 6.0 Patient G I: 9.0 I: 4.5 I: 4.5 Patient H I: 5.5 I: 7.5 I: -2.0 Patient I I: 9.0 I: 5.0 I: 4.0 Patient J I: 7.0 I: 2.5 I: 4.5 Average change in VAS points 4.0 Average change in leg pain one year post-op 4.0 points on a 10 point scale
  
  
• Denominator: Eligible Population: Patients with lumbar spine fusion procedures (Arthrodesis Value Set) occurring during a 12 month period for patients age 18 and older at the start of that period. Denominator: Patients within the eligible population whose leg pain was measured by the Visual Analog Scale (VAS) within three months preoperatively AND at one year (+/- 3 months) postoperatively. * The measure of average change in function can only be calculated if both a pre-operative and post-operative PRO assessment are completed
  
  
• Exclusions: The following exclusions must be applied to the eligible population: Patient had cancer (Spine Cancer Value Set), fracture (Spine Fracture Value Set) or infection (Spine Infection Value Set) related to the spine. Patient had idiopathic or congenital scoliosis (Congenital Scoliosis Value Set)
  
  
• HHS NQS Priority: Patient and Family Engagement
  
  
• HHS Data Source: Administrative clinical data, EHR, Paper medical record, Registry
  
  
• Measure Type: Patient Reported Outcome
  
  
• Steward: MN Community Measurement
  
  
• Endorsement Status: Never Submitted
  
  
• Is the measure specified as an electronic clinical quality measure? No
  
  

Summary of Workgroup Deliberations

• Workgroup Recommendation: Conditional Support for Rulemaking
  
  
• Workgroup Rationale: MAP was encouraged to see this change in leg pain following lumbar spine fusion surgery patient reported outcome measure. MAP Conditionally supported this measure with the condition that it is submitted to NQF for endorsement.
  
  
• Public comments received: 2

Rationale for measure provided by HHS
Patient Reported Outcome Measures and Integration Into Electronic Health Records Pitzen, C. et al, Journal of Oncology Practice DOI: 10.1200/JOP.2016.014118; published online ahead of print at jop.ascopubs.org on July 26, 2016.

Measure Specifications

• NQF Number (if applicable): 729
  
  
• Description: The percentage of patients 18-75 years of age who had a diagnosis of type 1 or type 2 diabetes and whose diabetes was optimally managed during the measurement period as defined by achieving ALL of the following: - HbA1c less than 8.0 mg/dL - Blood Pressure less than 140/90 mmHg - On a statin medication, unless allowed contraindications or exceptions are present - Non-tobacco user - Patient with ischemic vascular disease is on daily aspirin or anti-platelets, unless allowed contraindications or exceptions are present
  
  
• Numerator: The number of patients in the denominator whose diabetes was optimally managed during the measurement period as defined by achieving ALL of the following: - The most recent HbA1c in the measurement period has a value less than 8.0 mg/dL - The most recent Blood Pressure in the measurement period has a systolic value of less than 140 mmHg AND a diastolic value of less than 90 mmHg - On a statin medication, unless allowed contraindications or exceptions are present - Patient is not a tobacco user - Patient with ischemic vascular disease (Ischemic Vascular Disease Value Set) is on daily aspirin or anti-platelets, unless allowed contraindications or exceptions are present
  
  
• Denominator: 18 years or older at the start of the measurement period AND less than 76 years at the end of the measurement period AND Patient had a diagnosis of diabetes (Diabetes Value Set) with any contact during the current or prior measurement period OR had diabetes (Diabetes Value Set) present on an active problem list at any time during the measurement period. AND At least one established patient office visit (Established Pt Diabetes & Vasc Value Set) for any reason during the measurement period
  
  
• Exclusions: The following exclusions are allowed to be applied to the eligible population: - Patient was a permanent nursing home resident at any time during the measurement period - Patient was in hospice or receiving palliative care at any time during the measurement period - Patient died prior to the end of the measurement period - Patient was pregnant (Diabetes with Pregnancy Value Set) at any time during measurement period - Documentation that diagnosis was coded in error - Patient had only urgent care visits during the measurement period
  
  
• HHS NQS Priority: Effective Prevention and Treatment
  
  
• HHS Data Source: Administrative clinical data, EHR, Paper medical record, Claims, Registry
  
  
• Measure Type: Composite
  
  
• Steward: MN Community Measurement
  
  
• Endorsement Status: Endorsed - This measure was endorsed in 2015 as a component of composite measure NQF #0729
  
  
• Changes to Endorsed Measure Specifications?: The MUC list indicates the measure has been modified from its endorsed version.
  
  
• Is the measure specified as an electronic clinical quality measure? No
  
  

Summary of Workgroup Deliberations

• Workgroup Recommendation: Conditional Support for Rulemaking
  
  
• Workgroup Rationale: The measure would address multiple components of high quality diabetes care. MAP recognized the importance of this measure given its clinical prevalence. MAP was supportive of this composite measure but also acknowledged the utility of the individual subcomponents of the measure to drive quality improvement. MAP Conditionally Supported this measure with the condition that there are no competing measures in the program and that the measure is updated to the most current clinical guidelines.
  
  
• Public comments received: 2

Rationale for measure provided by HHS
Addressing Health Care Disparities Using Public Reporting Snowden, A. et al American Journal of Medical Quality August 2012 27 (4): 275-81

Summary of NQF Endorsement Review

• Year of Most Recent Endorsement Review: 2015
  
  
• Project for Most Recent Endorsement Review: Endocrine
  
  
• Review for Importance: 1a. Evidence, 1b. Performance Gap) 1a. Evidence: H-5; M-11; L-0; I-1; 1b. Performance Gap: H-15; 1d. Composite – Quality Construct and Rationale: H-4; M-7; L-4; I-1 Rationale: • For all but one of the components included in this composite (tobacco-free), the developer presented recommendations from the 2014 clinical practice guidelines developed by the Institute for Clinical Systems Improvement (ICSI), which were based on a systematic review of evidence that was graded either high or moderate. Additional evidence-based recommendations from the American College of Cardiology and U.S. Preventive Services Task Force also were presented. Committee members agreed that the evidence supports the relationship between each component and desired health outcomes. • Data provided by the developer indicate that for 2014, only 38.9% of diabetic patients in Minnesota met all five component targets from the composite measure. Committee members agreed that although performance on some of the components is quite high, overall performance indicates opportunity for improvement. • Although some Committee members voiced concern over the “all-or-none” structure of the measure, others agreed that a more comprehensive measure that focuses on management of multiple risk factors is needed. The Committee agreed that the developer description of the quality construct, rationale, and aggregation and weighting approach is explicitly articulated and logical.
  
  
• Review for Scientific Acceptability: 2. Scientific Acceptability of Measure Properties: The measure meets the Scientific Acceptability criteria (2a. Reliability - precise specifications, testing; 2b. Validity - testing, threats to validity) 2a. Reliability: H-9; M-7; L-0; I-0; 2b. Validity: H-1; M-10; L-4; I-1; 2d. Composite: H-1; M-10; L-4; I-1 Rationale: • Committee members noted that the specifications of the statin component of this measure have changed since the most recent endorsement of the measure due to changes in the ACC/AHA clinical practice guidelines on cholesterol management released in November, 2013. In the earlier version of the measure, the statin component assessed reaching a target LDL threshold of < 100 mg/dL; the revised version of this component assesses statin use. • Committee members questioned whether the measure assesses if a patient is on the appropriate statin dose. Developers clarified that the measure does not consider the statin dose but assesses only whether a patient is on a statin. • Members also questioned the age range of 18-75 for the statin component of the measure. The developer clarified that for patients 21-39 years of age, this component is applicable only if the patient has ischemic vascular disease or a very high LDL level, in accordance with the ACC/AHA guidelines. • The developer clarified that the level of analysis for the measure is clinician groups (not individual clinicians), and also noted that multiple clinics may form a clinician group. They also clarified that the measure does not require having a minimum of 30 patients. • Developers presented results of signal-to-noise reliability testing of the performance measure score. They clarified that the beta-binomial method was used for the reliability testing because the composite score itself is a binary (yes/no) measure. Members noted that although the 12 reliability was quite high for most clinician groups, it was lower than 0.7 for some clinician groups. • To demonstrate validity of the performance measure score, developers examined the association between the scores for this measure with the scores from the Optimal Vascular Care measure (NQF #0076), hypothesizing that clinician groups likely provide similar quality of care to different patients who also require management of multiple risk factors. The R2 value from this analysis was 0.64. The developers also described several steps occurring during the data submission process as demonstration of empirical validity testing at the data level element. • Developers also clarified that the measure is risk-adjusted for three factors (insurance type, age group, and diabetes type) and noted that the risk-adjustment strategy was developed using data from all clinicians in Minnesota. However, one member expressed some concern that the only adjustment for sociodemograhic status is insurance type. Developers clarified that other potential risk factors that were considered were not statistically significant and thus were not included in the risk-adjustment model. • Several Committee members voiced concern about holding physicians accountable for the patient’s tobacco use, as some see actual tobacco use (as opposed to efforts for tobacco cessation) as out of the control of the clinician. However, another member referred to data showing that physicians can influence their patients to stop tobacco use. Developers also noted that statewide, they have seen an approximate 2.5% increase in tobacco-free patients in Minnesota. • One Committee member noted the need for clarity about potential adverse effects related to statin use. Another member referenced the flow diagram provided by the developer that details several contraindications for statin use, while another member echoed the importance of the potential for adverse reactions when making treatment decisions. • After developers clarified the performance rates for each of the components, Committee members questioned whether the aspirin component (performance rate =99.5% in MN) is needed in the composite. Developers noted that while this component may be "topped out" in MN, this happened over a four-year period of focus on this component. They also referenced a New England Journal of Medicine article that found a 34.8% performance rate nationally in the primary care setting. Finally, they noted that performance on this component across ACOs is, on average, 75.3%.
  
  
• Review for Feasibility: 3. Feasibility: H-7; M-4; L-4; I-0 (3a. Clinical data generated during care delivery; 3b. Electronic sources; 3c.Susceptibility to inaccuracies/ unintended consequences identified 3d. Data collection strategy can be implemented) Rationale: • The measure data can be collected through electronic clinical data and paper records. • One Committee member noted that, due to the number of components included in the composite, the data collection effort for this composite measure may be intensive. Developers stated that submission of this measure by all clinician groups in MN is mandated by the state. While they acknowledged that MN has many large practices that use EHRs, small practices— even those who still use paper medical records—are able to submit data on this measure. The developers did, however, acknowledge the data collection burden for the new statin component if a patient has not been prescribed a statin (i.e., identifying exceptions due to contraindications).
  
  
• Review for Usability: 4. Usability and Use: H-5; M-7; L-4; I-0 ((Used and useful to the intended audiences for 4a. Accountability and Transparency; 4b. Improvement; and 4c. Benefits outweigh evidence of unintended consequences) Rationale: • Committee members noted that the measure is publicly reported and is used in pay-forperformance and accreditation programs. Performance is slowly increasing across the state of Minnesota, suggesting quality of care may be improving. • Data submitted by the developer demonstrate relatively consistent improvement of performance in MN from the years 2006-2014. • Committee members agreed that this composite measure is patient-centric and acknowledged the importance of using a comprehensive measure that assesses performance of reducing multiple risk factors. • Some Committee members expressed concern that the measure could incent some providers to "cherry-pick" patients or make their practices less hospitable to certain patients or certain subgroups of patients (the tobacco-free component of the measure was a particular concern).
  
  
• Review for Related and Competing Measures: 5. Related and Competing Measures • This measure is a competing measure to the following measures o 0061: Comprehensive Diabetes Care: Blood Pressure Control (< 7% to < 8% depending on individual patient factors. o 0575: Comprehensive Diabetes Care: Hemoglobin A1c (HbA1c) control (<8%) • NQF staff asked the Committee to discuss whether there is justification for continued endorsement of the individual measures if the composite retains endorsement. The Committee discussed the pros and cons of endorsing both individual measures and the composite measure. The Committee ultimately agreed that while the composite measure is useful to assess patientcentric performance across a variety of clinical areas, endorsement of individual measures also can be beneficial, particularly for users who want to focus on certain components of the composite or those who have data collection constraints and cannot use the composite. The Committee therefore recommended continued endorsement of both the individual measures and the composite measure.
  
  
• Endorsement Public Comments: 6. Public and Member Comment Comments received: • Two commenters raised concern over the glucose control component of the composite, referencing the National Action Plan for Adverse Event Prevention, which was released in August, 2014. The National Action Plan states that the blood glucose threshold of < 7% to < 8% depending on individual patient factors. Benefits: Achieving near-normal glycemic control lowers risk of diabetes microvascular complications such as retinopathy, nephropathy and amputations. Achieving A1c of 6.9 to 7.9% may also significantly reduce macrovascular complications based on Steno-2 and UKPDS data. Quality of Evidence: High Strength of Recommendation: Strong. Measurement does not and should not preclude good clinical judgement; however the measure development work group believes that a target of < 8.0 is reasonable and supported by guidelines. Our measure does have an upper age limit cut-off of 75 years and we allow exclusions for death, permanent nursing home resident or patients who are receiving hospice or palliative care services. • Two commenters were critical of the composite measure itself, citing concern that use of the composite measure could mask the individual care processes that most need improvement. Developer response: While it is true that the measure is reported at the composite level, the individual components and the associated rates are available to the medical groups for better understanding their rates and for use in quality improvement to know which areas have opportunity for improvement. MNCM and the measure development work group firmly believe that achieving the intermediate physiological outcome targets related to blood pressure and glycemic control in addition being tobacco free and use of daily aspirin and statins where appropriate are the diabetic patient’s best mechanisms of avoiding or postponing long term complications associated with this chronic condition which affects millions of Americans. Measuring providers separately on individual targets is not as patient centric as a measure that seeks to reduce multiple risk factors for each patient. Diabetic patients are more likely to reduce their overall risk and maximize health outcomes by achieving several intermediate physiological targets. • Two commenters noted that documenting HbA1c levels >8% but less than 9% cannot be done using CPT-II coding, necessitating need for medical chart review. Developer response: A point of clarification, these measure do not rely on CPTII codes for numerator compliance, nor are they indicated anywhere in our measure specification. Measure specifications focus on the electronic health record as a source of clinical information for calculating numerator compliance; actual A1c values are utilized in the case of the A1c target. Additionally, 80 to 90% of all the clinics in MN are reporting this information from their electronic health records without the need for additional chart abstraction. • One commenter suggested a need for including sociodemographic factors in the risk-adjustment approach. Developer response: Our risk adjustment model does include insurance product, which is a proxy for socioeconomic status. During the process of measure development, the expert panel discusses potential variables for risk adjustment that are important to consider for the measured population. For this measure, variables that are available for evaluation include gender, age, zip, race/ethnicity, country of origin, primary language, insurance product, diabetes type, depression and ischemic vascular disease. The potential risk adjustment variables are then evaluated for appropriate inclusion in the model based on a t value outside the range of -2.0 and +2.0. Currently, the variables that have demonstrated acceptable properties are insurance product, age bands (18-25, 26-50, 51-65 and 65 to 75) and diabetes type (1 or 2). Race/ethnicity has been collected for this measure in MN for the past few years, but has now reached a level of 15 reliability in which it can be evaluated for its impact. MNCM continues to review variables and their impact on the measure and part of its measure risk adjustment strategy. • One commenter suggested the need for additional detail regarding moderate or high intensity in the description of statin use for the measure. Developer response: The measure development work group thoroughly discussed the pros and cons of specifying a certain dose of the statin medication and based on the following factors ultimately decided to not specify a dose of moderate or high intensity for numerator compliance: 1) data burden for practices, 2) controversy and burden surrounding the CV risk calculator, 3) ICSI 2014 Diabetes Guideline recommendations for measurement and 4) cardiology work group member’s believe that there is some benefit for some patients who can only tolerate a lower intensity dose. Committee response: • During its review of the individual measure assessing HbA1c
  
  
• Endorsement Committee Recommendation: Standing Committee Recommendation for Endorsement: Y-13; N-4
  
  

Measure Specifications

• NQF Number (if applicable): 76
  
  
• Description: The percentage of patients 18-75 years of age who had a diagnosis of ischemic vascular disease (IVD) and whose IVD was optimally managed during the measurement period as defined by achieving ALL of the following: - Blood Pressure less than 140/90 mmHg - On a statin medication, unless allowed contraindications or exceptions are present - Non-tobacco user - On daily aspirin or anti-platelets, unless allowed contraindications or exceptions are present The number of patients in the denominator whose IVD was optimally managed during the measurement period as defined by achieving ALL of the following: - The most recent Blood Pressure in the measurement period has a systolic value of less than 140 mmHg AND a diastolic value of less than 90 mmHg - On a statin medication, unless allowed contraindications or exceptions are present - Patient is not a tobacco user - On daily aspirin or anti-platelets, unless allowed contraindications or exceptions are present
  
  
• Numerator: The number of patients in the denominator whose IVD was optimally managed during the measurement period as defined by achieving ALL of the following: - The most recent Blood Pressure in the measurement period has a systolic value of less than 140 mmHg AND a diastolic value of less than 90 mmHg - On a statin medication, unless allowed contraindications or exceptions are present - Patient is not a tobacco user - On daily aspirin or anti-platelets, unless allowed contraindications or exceptions are present
  
  
• Denominator: 18 years or older at the start of the measurement period AND less than 76 years at the end of the measurement period AND Patient had a diagnosis of ischemic vascular disease (Ischemic Vascular Disease Value Set) with any contact during the current or prior measurement period OR had ischemic vascular disease (Ischemic Vascular Disease Value Set) present on an active problem list at any time during the measurement period. AND At least one established patient office visit (Established Pt Diabetes & Vasc Value Set) for any reason during the measurement period
  
  
• Exclusions: The following exclusions are allowed to be applied to the eligible population: - Patient was a permanent nursing home resident at any time during the measurement period - Patient was in hospice or receiving palliative care at any time during the measurement period - Patient died prior to the end of the measurement period - Documentation that diagnosis was coded in error - Patient had only urgent care visits during the measurement period
  
  
• HHS NQS Priority: Effective Prevention and Treatment
  
  
• HHS Data Source: Administrative clinical data, EHR, Paper medical record, Claims, Registry
  
  
• Measure Type: Composite
  
  
• Steward: MN Community Measurement
  
  
• Endorsement Status: Endorsed
  
  
• Changes to Endorsed Measure Specifications?: The MUC list indicates the measure has been modified from its endorsed version.
  
  
• Is the measure specified as an electronic clinical quality measure? No
  
  

Summary of Workgroup Deliberations

• Workgroup Recommendation: Support for Rulemaking
  
  
• Workgroup Rationale: MAP supports this optimal vascular care measure. The measure would address multiple components of high quality vascular care. MAP recognized the importance of this measure given its clinical prevalence. MAP was supportive of this composite measure but also acknowledged the utility of the individual subcomponents of the measure to drive quality improvement. MAP discussed the need that there are no competing measures in the program and that the measure is updated to the most current clinical guidelines.
  
  
• Public comments received: 1

Rationale for measure provided by HHS
Risk Factor Optimization and Guideline-Directed Medical Therapy in US Veterans With Peripheral Arterial and Ischemic Cerebrovascular Disease Compared to Veterans With Coronary Heart Disease. Hira RS et al Am J Cardiol. 2016 Oct 15;118(8):1144-1149. doi: 10.1016/j.amjcard.2016.07.027. Epub 2016 Jul 29.

Summary of NQF Endorsement Review

• Year of Most Recent Endorsement Review: 2017
  
  
• Project for Most Recent Endorsement Review: Cardiovascular 2016-2017
  
  
• Review for Importance: 1. Importance to Measure and Report: The measure meets the Importance criteria (1a. Evidence, 1b. Performance Gap, 1c. Composite) 1a. Evidence: H-14; M-6; L-1; I-1; 1b. Performance Gap: H-14; M-7; L-1; I-0; Composite: H-12; M-8; L-1; I- 1 Rationale: • For the 2012 maintenance of endorsement evaluation, the developer provided the following clinical practice guidelines to support the blood pressure, statin medication, tobacco free (outcome measure), and daily aspirin or anti-platelet medication components: o Blood pressure, statin medication, tobacco free, and daily aspirin or anti-platelet medication components: The ICSI Stable Coronary Artery Disease (April 2011), Address Modifiable Risk Factors guideline recommended modifiable risk factors for coronary artery disease such as smoking, inadequate physical activity, stress, hyperlipidemia, obesity, hypertension and diabetes mellitus be evaluated. 21 o Blood pressure: The Comorbid Conditions Guideline and the ICSI Hypertension Diagnosis and Treatment Guideline (November 2010) recommended a target blood pressure of 140/90 mmHg or less. o Statin medication: The ICSI Lipid Management in Adults (October 2009) guideline recommended target goals for hyperlipidemic patients with coronary artery disease: LDL – less than 100 mg/dL; HDL – 40 mg/dL or greater; Triglycerides – less than 150 mg/dL. o Daily aspirin or anti-platelet medication: The ICSI Stable Coronary Artery Disease (April 2011), Address Modifiable Risk Factors guideline recommended the use of one aspirin tablet daily (81-162 mg) unless there are medical contraindications. • For the current maintenance of endorsement evaluation, the developer provided the following updated evidence for all four components: o Blood pressure: The 2015 AHA/ACC/ASH Scientific Statement on the Treatment of Hypertension in Patients with Coronary Artery Disease included 3 recommendations for blood pressure targets, including a blood pressure goal of 65 and Medicare. •o Statin medication: The ICSI Lipid Management in Adults (updated Nov 2013/completed prior to ACC/AHA release) recommends that clinicians initiate statin therapy regardless of LDL in patients with established atherosclerotic cardiovascular disease (ASVCD). The 2013 ACC/AHA Guideline for the Treatment of Blood Cholesterol to Reduce Atherosclerotic Cardiovascular Risk in Adults recommends high-intensity statin therapy be initiated or continued as first-line therapy in women and meno Tobacco free outcome measure: The developer provided evidence from the United States Preventive Services Task Force (USPSTF) stating that despite considerable progress in tobacco control over the past 50 years, in 2013, an estimated 17.8% of U.S. adults and 15.9% of pregnant women aged 15 to 44 years were current cigarette smokers. o Daily aspirin or anti-platelet medication: The developer provided three recommendations for antiplatelet agents/anticoagulants for patients with ischemic vascular disease from the AHA/ACCF Secondary Prevention and Risk Reduction Therapy for Patients with Coronary and Other Atherosclerotic Vascular Disease: 2011 Update. • The Standing Committee discussed the potential changes to blood pressure parameters based on the results of the Systolic Blood Pressure Intervention Trial (SPRINT), which compared the benefit of treatment of systolic blood pressure to a target of less than 120 mm Hg with treatment to a target of less than 140 mm Hg. The Committee also discussed the anticipated blood pressure guidelines to be released by AHA/ACC sometime in the future. NQF staff asked the Committee to consider the quantity, quality, and consistency of the body of evidence that was presented in the measure submission form. NQF staff reassured the Committee that the NQF process allows for a measure to be reviewed when new evidence becomes available. One of the Committee members noted that the USPSTF recommendations for daily aspirin include patients aged 50 to 70 years old, while the measure includes patients up to 75 years old. Other Committee members noted that the USPSTF recommendations are for primary prevention rather than patients with a diagnosis of ischemic vascular disease (IVD). Overall, the Standing Committee agreed that the updated evidence supports blood pressure control, statin use, daily aspirin or anti-platelet medication, and tobacco use assessment and 22 intervention(s) in patients to avoid or postpone long-term complications associated with a diagnosis with IVD. The developer provided composite performance rates from clinics in Minnesota for Report Year 2007-2016 (Dates of Service 2006-2015). o In 2007, the rate was 38.9% for 4,662 patients and 33.8% in 2010 for 63,241 patients. In 2011, the blood pressure component target changed from, and the performance rate increase to 39.7% for 66,910 patients. o In 2015, the cholesterol management component was suppressed during redesign of the measure and the performance rate increased to 69.3% for 102,654 patients. o In 2016, the cholesterol management component was changed from LDL <100 to appropriate statin use and the performance rate was 66.1% for 104,395 patients. • The developer also provided performance rates for the individual components. o The blood pressure component increased from 84.0% in 2012 to 85.0% in 2016. o Daily aspirin use or anti-platelet medication use increased from 92.5% in 2009 to 96.7% in 2016. o The number of tobacco free patients increased from 82.4% in 2009 to 83.0 in 2016. o Statin use was 95.2% in 2016 (this was the first year the new component was reported) The developer provided 2014 disparities data from the measure as specified demonstrating a performance rate of 67.2% for White patients, 47.6% for Black/African-American patients, 51.8% for American Indian/Alaska Native patients, and 53.4% for multi-racial patients. The data also showed a higher performance gap for female patients and younger patients. The Committee asked the developer if there were trend data on disparities that demonstrated a change in performance over time and by individual clinic. The developer did not have additional, specific disparities data. However, according to the developer, some clinics that care for a greater proportion of minority patients have lower performance rates but there are a couple of clinics that are excelling in minimizing disparities. • The Standing Committee agreed that the data provided demonstrated a performance gap and opportunity for improvement in optimal vascular care for patients with IVD. • This is an all-or-none composite measure that requires patients to meet all four component targets in the composite measure to be considered ‘optimally managed’; all four components are weighted equally. The developer noted that measuring providers on individual targets is not as patient-centric as this composite measure that seeks to reduce multiple risk factors in patients with IVD and maximize health outcomes. One of the members of the Standing Committee noted that the tobacco free component would be more appropriate as a process measure. The Committee member noted that smoking rates are often influenced by geographic location. Providers in areas with high rates of tobacco use will not appear as effective in increasing the number of tobacco free patients as those in areas where tobacco use is less prevalent. In the pre-evaluation comments, another Committee member noted that the absolute benefit of each component is not equal; achieving blood pressure control or smoking cessation is much more difficult than prescribing a statin or aspirin/anti-platelet medication. • The Standing Committee agreed, that overall, the quality construct and rational for the composite was clearly stated and logical.
  
  
• Review for Scientific Acceptability: 2. For the 2012 maintenance of endorsement evaluation, patient-level data element validity testing was conducted on 63,241 patients with IVD from 128 medical groups representing 573 clinics that submitted data to Minnesota Community Measurement for 2009 dates of service reported in 2010. After data submission, in-person validation audits requiring a 90% accuracy rate were conducted to compare the submission to the patient’s medical record. Of the 128 medical groups that submitted data in 2010, 17 groups initially failed the audit and remedy plans were developed. All 17 groups resubmitted and passed subsequent audit. • For the current maintenance of endorsement evaluation, the measure was tested at the measure score level using a dataset that included 104,395 patients with IVD in Minnesota and neighboring communities from 111 medical groups representing 671 clinics for dates of service from January 1, 2015 to December 31, 2015. • To test the reliability of the measure score, the developer used a beta-binomial model to assess the signal-to-noise ratio. A reliability score of 0.00 implies that all the variability in a measure is attributable to measurement error. A reliability score of 1.00 implies that all the variability is attributable to real differences in performance. The higher the reliability score, the greater is the confidence with which one can distinguish the performance of one facility from another. This is an appropriate test for measure score reliability. A reliability score of 0.70 is generally considered a minimum threshold for reliability. The overall reliability for the composite measure was 0.90 and 0.61 at the minimum number of patients per reportable clinic (=30). The distribution of reliability scores by number of eligible patients per reportable clinic (=30) ranged from 0.61 for 30 patients per clinic to 0.99 for 4,441 patients per clinic. • In the pre-evaluation comments, a member of the Standing Committee mentioned that assessing prescribing behavior of statin therapy (as noted in the specifications) is not consistent with the evidence provided to support the statin component. The Committee member noted that prescribing the lowest dose of the weakest statin would meet the intent of the measure but not generate clinically significant outcomes in the IVD population. Other Committee members questioned why ‘permanent nursing home residents’ are excluded from the denominator. The Committee discussed the fall risks associated with administering blood pressure medication to nursing home patients, excessive treatment in patients with advanced illness, and the lack of clinical trials for these types of medications in the nursing home population. • The Standing Committee did not express additional concerns with the reliability of the measure, but ultimately decided the testing results were sufficient. • For the 2012 maintenance of endorsement evaluation, content and face validity were assessed through the Measurement and Reporting Committee and a panel of experts. There was consensus among the expert workgroup that the target components reflected a quality of care that will reduce patients heart attack and stroke risk. • For the current maintenance of endorsement evaluation, empirical validity testing of the composite measure score was conducted by testing the correlation of a medical group’s performance with their performance on the Optimal Diabetes Care measure (#0729). It is expected that the quality of care provided by a medical group to a patient with ischemic vascular disease would be of similar quality as the care provided to a patient with diabetes, therefore the respective performance measure scores should be similar. This is an appropriate method for assessing conceptually and theoretically sound hypothesized relationships. The Optimal Diabetes Care measure (#0729) includes the same four components as #0074 plus a component for hemoglobin A1C; it also measures a different population. The linear regression analysis demonstrated an R2 value of 0.635, which means that 64.0% of the total variation in performance on the Optimal Vascular Care measure can be explained by variation in performance on the Optimal Diabetes Care measure. The remaining 36.0% of total variation on the Optimal Vascular Care measure remains unexplained. • This measure is risk-adjusted. The final risk factors selected for the risk model were age and insurance product (Medicare, Medicaid, MSHO, Special Needs, Self-pay, Uninsured). The developer analyzed gender and depression as well, but gender did not show sufficient variation between clinics and ‘depression’ was not selected due to the high cost of collection. The developer stated that race, ethnicity, language, and country of origin (RELO) were not considered for risk adjustment because these variables did not have a high completion rate across all clinics. The developer is continuing to work with the medical community to achieve the goal of evaluating RELO at the clinic level. The developer conducted an Analysis of Maximum Likelihood Estimates on the 2014 Dates of Service to compare the optimal rate of patients by insurance product (Commercial, MHCP, and Uninsured) to patients with Medicare and patient age (18-25; 26-50; 51-65) to patients aged 66-75. The Analysis of Maximum Likelihood Estimates demonstrated that all of the results for both variables, age and insurance product, were significant, except for ages less than 26 due to the small sample size (n = 44). The developer also found that the only two variables that were correlated were age >65 and Medicare. The Standing Committee did not express any concerns on the threats to validity and agreed that the testing results satisfied the validity criterion. • The developer conducted a Pearson Correlation Analysis of the individual components rates and the composite rates. The Pearson correlation coefficient value, r, ranges from +1.00 to -1.00. A value of 0.00 indicates that there is no association between the two variables. A value greater than 0.00 indicates a positive association; that is, as the value of one variable increases, so does the value of the other variable. A value less than 0.00 indicates a negative association; that is, as the value of one variable increases, the value of the other variable decreases. The developer conducted the following Pearson Correlation Analysis for each component: Variable Mean Pearson r coefficient Blood pressure 0.85048 0.69813 Tobacco Free 0.80901 0.71336 Daily ASA Use 0.96271 0.59223 Statin Use 0.93973 0.62327 Optimal Vascular Care Rate = 0.63919 • The developer concluded that practices in Minnesota demonstrate relatively high compliance for all of the components; however, there is still an opportunity for improvement at the clinic level. The blood pressure control and tobacco free components demonstrated the most variability, opportunity for improvement, and impact the ability to achieve all four components. Another Committee member suggested that if the two variables with the most variability were more heavily weighted than the other components, the measure would be more impactful. Another Committee member pointed out that three of the components were under the direct control of the provider, yet it was not clear how the tobacco free component captured the quality of care provided by the clinician. A member of the Standing Committee questioned whether there was evidence showing that meeting all four component targets would not 25 generate the same patient outcomes as meeting two or three of the components. The developer pointed out that various combinations of the components and the proportion of patients meeting the different combinations were provided. • The Standing Committee did not express additional concerns with the construct of the composite measure and agreed the information provided was sufficient to satisfy the criterion for composite construct.
  
  
• Review for Feasibility: 3. Feasibility: H-12; M-10; L-0; I-0 (3a. Clinical data generated during care delivery; 3b. Electronic sources; 3c. Susceptibility to inaccuracies/ unintended consequences identified 3d. Data collection strategy can be implemented) Rationale: • All of the data elements are in defined fields in electronic sources and there are no fees, licensure, or other requirements necessary to use this measure. The Standing Committee agreed this measure met the feasibility criterion.
  
  
• Review for Usability: 4. Usability and Use: H-13; M-8; L-1; I-0 (Used and useful to the intended audiences for 4a. Accountability and Transparency; 4b. Improvement; and 4c. Benefits outweigh evidence of unintended consequences) Rationale: • The measure is widely used in Minnesota for public reporting, payment, regulatory and accreditation programs, and quality improvement with external benchmarking to multiple organizations. 5. Related and Competing Measures • This measure is related to: o #0067: Chronic Stable Coronary Artery Disease: Antiplatelet Therapy (ACC) o #0068: Ischemic Vascular Disease (IVD): Use of Aspirin or Another Antiplatelet (NCQA) o #0073: Ischemic Vascular Disease (IVD): Blood Pressure Control (NCQA) • The developer stated that #0068 and #0073 focus on the inpatient setting and patients discharged with AMI, CABG, or PCI. #0067 focuses on patients with CAD.
  
  
• Review for Related and Competing Measures: 5. Related and Competing Measures • This measure is related to: o #0067: Chronic Stable Coronary Artery Disease: Antiplatelet Therapy (ACC) o #0068: Ischemic Vascular Disease (IVD): Use of Aspirin or Another Antiplatelet (NCQA) o #0073: Ischemic Vascular Disease (IVD): Blood Pressure Control (NCQA) • The developer stated that #0068 and #0073 focus on the inpatient setting and patients discharged with AMI, CABG, or PCI. #0067 focuses on patients with CAD.
  
  
• Endorsement Public Comments: 6. Public and Member Comment • One commenter did not agree with statin use as a component to address dyslipidemia and believed it would be misleading to include this as a component of “optimal care.” The commenter believed including this component would lead to the lowest level of acceptable care being considered optimal care and would do little to move the quality of care forward. • Developer Response: Thank you for your comment and suggestion for the inclusion of the dose of statin (moderate or high) in the calculation of the cholesterol component of this patient level all-or-none composite measure. While ACC/ AHA guidelines do indicate that most patients with ischemic vascular disease would benefit from high dose intensity statins, there 26 are provisions for moderate intensity statins for patients who cannot tolerate high intensity doses. The measure development work group thoroughly discussed the pros and cons of specifying a certain dose of the statin medication for numerator component compliance and determined that requiring the submission of the dose of statin would cause undue data collection burden for the practices. Additionally, the cardiologists on the workgroup strongly believe that there is some benefit for patients who can only tolerate a low dose of statin. We do not discount the role of ongoing LDL monitoring to determine effectiveness of statin therapy, but having a physiological target (e.g. LDL < 100) is no longer supported by evidence. The American College of Cardiology/ American Heart Associate guidelines for the treatment of blood cholesterol indicate the following: “Treat to target — this strategy has been the most widely used the past 15 years but there are 3 problems with this approach. First, current clinical trial data do not indicate what the target should be. Second, we do not know the magnitude of additional ASCVD risk reduction that would be achieved with one target lower than another. Third, it does not take into account potential adverse effects from multidrug therapy that might be needed to achieve a specific goal. Thus, in the absence of these data, this approach is less useful than it appears (Section 3). It is possible that future clinical trials may provide information warranting reconsideration of this strategy” (pg. 17) Yes, our component rates for prescribing statins are high in MN, which is a little bit unexpected for the newly re-designed component, however we would like to clarify the cholesterol component of statin use is not reported as a stand-alone measure. The Optimal Vascular Care measure is reported as an all-or-none composite, patients achieving multiple components of modifiable risk factors to reduce or delay long term complications. Statin use is one component, the other three are blood pressure control, tobacco-free and daily aspirin or antiplatelet medication. • Committee Response: Thank you for your comment. The Committee agrees that monitoring LDL levels remains an important part of providing care for patients with IVD. However, the statin component in this measure aligns with the 2013 ACC/AHA Guideline for the Treatment of Blood Cholesterol to Reduce Atherosclerotic Cardiovascular Risk in Adults.
  
  
• Endorsement Committee Recommendation: Standing Committee Recommendation for Endorsement: Y-19; N-3
  
  

Measure Specifications

• NQF Number (if applicable): *Note - 729 (This measure is a component of the endorsed composite measure NQF #729)
  
  
• Description: The percentage of patients 18-75 years of age who had a diagnosis of type 1 or type 2 diabetes and whose most recent HbA1c during the measurement period was less than 8.0 mg/dL.
  
  
• Numerator: Denominator patients whose most recent HbA1c during the measurement period was less than 8.0 mg/dL.
  
  
• Denominator: 18 years or older at the start of the measurement period AND less than 76 years at the end of the measurement period AND Patient had a diagnosis of diabetes (Diabetes Value Set) with any contact during the current or prior measurement period OR had diabetes (Diabetes Value Set) present on an active problem list at any time during the measurement period. AND At least one established patient office visit (Established Pt Diabetes & Vasc Value Set) for any reason during the measurement period
  
  
• Exclusions: The following exclusions are allowed to be applied to the eligible population: - Patient was a permanent nursing home resident at any time during the measurement period - Patient was in hospice or receiving palliative care at any time during the measurement period - Patient died prior to the end of the measurement period - Patient was pregnant (Diabetes with Pregnancy Value Set) at any time during measurement period - Documentation that diagnosis was coded in error - Patient had only urgent care visits during the measurement period
  
  
• HHS NQS Priority: Effective Prevention and Treatment
  
  
• HHS Data Source: Administrative clinical data, EHR, Paper medical record, Claims, Registry
  
  
• Measure Type: Intermediate Outcome
  
  
• Steward: MN Community Measurement
  
  
• Endorsement Status: Endorsed - This measure was endorsed in 2015 as a component of composite measure NQF #0729
  
  
• Changes to Endorsed Measure Specifications?: The MUC list indicates the measure has not been modified from its endorsed version.
  
  
• Is the measure specified as an electronic clinical quality measure? No
  
  

Summary of Workgroup Deliberations

• Workgroup Recommendation: Conditional Support for Rulemaking
  
  
• Workgroup Rationale: MAP acknowledged the importance of A1c Control (< 8.0) as a critical element of high quality diabetes care. While this measure is included in the Optimal Diabetes Care composite measure, MAP recognized that clinicians may still report A1c control measures separately to drive quality improvement. MAP also discussed the competing measure, QPP #001, that measures patients with A1c > 9.0. MAP Conditionally Supported this measure with the condition that there are no competing measures in the program.
  
  
• Public comments received: 1

Rationale for measure provided by HHS
Addressing Health Care Disparities Using Public Reporting Snowden, A. et al American Journal of Medical Quality August 2012 27 (4): 275-81

Summary of NQF Endorsement Review

• Year of Most Recent Endorsement Review: 2015
  
  
• Project for Most Recent Endorsement Review: Endocrine
  
  
• Review for Importance: 1a. Evidence, 1b. Performance Gap) 1a. Evidence: H-5; M-11; L-0; I-1; 1b. Performance Gap: H-15; 1d. Composite – Quality Construct and Rationale: H-4; M-7; L-4; I-1 Rationale: • For all but one of the components included in this composite (tobacco-free), the developer presented recommendations from the 2014 clinical practice guidelines developed by the Institute for Clinical Systems Improvement (ICSI), which were based on a systematic review of evidence that was graded either high or moderate. Additional evidence-based recommendations from the American College of Cardiology and U.S. Preventive Services Task Force also were presented. Committee members agreed that the evidence supports the relationship between each component and desired health outcomes. • Data provided by the developer indicate that for 2014, only 38.9% of diabetic patients in Minnesota met all five component targets from the composite measure. Committee members agreed that although performance on some of the components is quite high, overall performance indicates opportunity for improvement. • Although some Committee members voiced concern over the “all-or-none” structure of the measure, others agreed that a more comprehensive measure that focuses on management of multiple risk factors is needed. The Committee agreed that the developer description of the quality construct, rationale, and aggregation and weighting approach is explicitly articulated and logical.
  
  
• Review for Scientific Acceptability: 2. Scientific Acceptability of Measure Properties: The measure meets the Scientific Acceptability criteria (2a. Reliability - precise specifications, testing; 2b. Validity - testing, threats to validity) 2a. Reliability: H-9; M-7; L-0; I-0; 2b. Validity: H-1; M-10; L-4; I-1; 2d. Composite: H-1; M-10; L-4; I-1 Rationale: • Committee members noted that the specifications of the statin component of this measure have changed since the most recent endorsement of the measure due to changes in the ACC/AHA clinical practice guidelines on cholesterol management released in November, 2013. In the earlier version of the measure, the statin component assessed reaching a target LDL threshold of < 100 mg/dL; the revised version of this component assesses statin use. • Committee members questioned whether the measure assesses if a patient is on the appropriate statin dose. Developers clarified that the measure does not consider the statin dose but assesses only whether a patient is on a statin. • Members also questioned the age range of 18-75 for the statin component of the measure. The developer clarified that for patients 21-39 years of age, this component is applicable only if the patient has ischemic vascular disease or a very high LDL level, in accordance with the ACC/AHA guidelines. • The developer clarified that the level of analysis for the measure is clinician groups (not individual clinicians), and also noted that multiple clinics may form a clinician group. They also clarified that the measure does not require having a minimum of 30 patients. • Developers presented results of signal-to-noise reliability testing of the performance measure score. They clarified that the beta-binomial method was used for the reliability testing because the composite score itself is a binary (yes/no) measure. Members noted that although the 12 reliability was quite high for most clinician groups, it was lower than 0.7 for some clinician groups. • To demonstrate validity of the performance measure score, developers examined the association between the scores for this measure with the scores from the Optimal Vascular Care measure (NQF #0076), hypothesizing that clinician groups likely provide similar quality of care to different patients who also require management of multiple risk factors. The R2 value from this analysis was 0.64. The developers also described several steps occurring during the data submission process as demonstration of empirical validity testing at the data level element. • Developers also clarified that the measure is risk-adjusted for three factors (insurance type, age group, and diabetes type) and noted that the risk-adjustment strategy was developed using data from all clinicians in Minnesota. However, one member expressed some concern that the only adjustment for sociodemograhic status is insurance type. Developers clarified that other potential risk factors that were considered were not statistically significant and thus were not included in the risk-adjustment model. • Several Committee members voiced concern about holding physicians accountable for the patient’s tobacco use, as some see actual tobacco use (as opposed to efforts for tobacco cessation) as out of the control of the clinician. However, another member referred to data showing that physicians can influence their patients to stop tobacco use. Developers also noted that statewide, they have seen an approximate 2.5% increase in tobacco-free patients in Minnesota. • One Committee member noted the need for clarity about potential adverse effects related to statin use. Another member referenced the flow diagram provided by the developer that details several contraindications for statin use, while another member echoed the importance of the potential for adverse reactions when making treatment decisions. • After developers clarified the performance rates for each of the components, Committee members questioned whether the aspirin component (performance rate =99.5% in MN) is needed in the composite. Developers noted that while this component may be "topped out" in MN, this happened over a four-year period of focus on this component. They also referenced a New England Journal of Medicine article that found a 34.8% performance rate nationally in the primary care setting. Finally, they noted that performance on this component across ACOs is, on average, 75.3%.
  
  
• Review for Feasibility: 3. Feasibility: H-7; M-4; L-4; I-0 (3a. Clinical data generated during care delivery; 3b. Electronic sources; 3c.Susceptibility to inaccuracies/ unintended consequences identified 3d. Data collection strategy can be implemented) Rationale: • The measure data can be collected through electronic clinical data and paper records. • One Committee member noted that, due to the number of components included in the composite, the data collection effort for this composite measure may be intensive. Developers stated that submission of this measure by all clinician groups in MN is mandated by the state. While they acknowledged that MN has many large practices that use EHRs, small practices— even those who still use paper medical records—are able to submit data on this measure. The developers did, however, acknowledge the data collection burden for the new statin component if a patient has not been prescribed a statin (i.e., identifying exceptions due to contraindications).
  
  
• Review for Usability: 4. Usability and Use: H-5; M-7; L-4; I-0 ((Used and useful to the intended audiences for 4a. Accountability and Transparency; 4b. Improvement; and 4c. Benefits outweigh evidence of unintended consequences) Rationale: • Committee members noted that the measure is publicly reported and is used in pay-forperformance and accreditation programs. Performance is slowly increasing across the state of Minnesota, suggesting quality of care may be improving. • Data submitted by the developer demonstrate relatively consistent improvement of performance in MN from the years 2006-2014. • Committee members agreed that this composite measure is patient-centric and acknowledged the importance of using a comprehensive measure that assesses performance of reducing multiple risk factors. • Some Committee members expressed concern that the measure could incent some providers to "cherry-pick" patients or make their practices less hospitable to certain patients or certain subgroups of patients (the tobacco-free component of the measure was a particular concern).
  
  
• Review for Related and Competing Measures: 5. Related and Competing Measures • This measure is a competing measure to the following measures o 0061: Comprehensive Diabetes Care: Blood Pressure Control (< 7% to < 8% depending on individual patient factors. o 0575: Comprehensive Diabetes Care: Hemoglobin A1c (HbA1c) control (<8%) • NQF staff asked the Committee to discuss whether there is justification for continued endorsement of the individual measures if the composite retains endorsement. The Committee discussed the pros and cons of endorsing both individual measures and the composite measure. The Committee ultimately agreed that while the composite measure is useful to assess patientcentric performance across a variety of clinical areas, endorsement of individual measures also can be beneficial, particularly for users who want to focus on certain components of the composite or those who have data collection constraints and cannot use the composite. The Committee therefore recommended continued endorsement of both the individual measures and the composite measure.
  
  
• Endorsement Public Comments: 6. Public and Member Comment Comments received: • Two commenters raised concern over the glucose control component of the composite, referencing the National Action Plan for Adverse Event Prevention, which was released in August, 2014. The National Action Plan states that the blood glucose threshold of < 7% to < 8% depending on individual patient factors. Benefits: Achieving near-normal glycemic control lowers risk of diabetes microvascular complications such as retinopathy, nephropathy and amputations. Achieving A1c of 6.9 to 7.9% may also significantly reduce macrovascular complications based on Steno-2 and UKPDS data. Quality of Evidence: High Strength of Recommendation: Strong. Measurement does not and should not preclude good clinical judgement; however the measure development work group believes that a target of < 8.0 is reasonable and supported by guidelines. Our measure does have an upper age limit cut-off of 75 years and we allow exclusions for death, permanent nursing home resident or patients who are receiving hospice or palliative care services. • Two commenters were critical of the composite measure itself, citing concern that use of the composite measure could mask the individual care processes that most need improvement. Developer response: While it is true that the measure is reported at the composite level, the individual components and the associated rates are available to the medical groups for better understanding their rates and for use in quality improvement to know which areas have opportunity for improvement. MNCM and the measure development work group firmly believe that achieving the intermediate physiological outcome targets related to blood pressure and glycemic control in addition being tobacco free and use of daily aspirin and statins where appropriate are the diabetic patient’s best mechanisms of avoiding or postponing long term complications associated with this chronic condition which affects millions of Americans. Measuring providers separately on individual targets is not as patient centric as a measure that seeks to reduce multiple risk factors for each patient. Diabetic patients are more likely to reduce their overall risk and maximize health outcomes by achieving several intermediate physiological targets. • Two commenters noted that documenting HbA1c levels >8% but less than 9% cannot be done using CPT-II coding, necessitating need for medical chart review. Developer response: A point of clarification, these measure do not rely on CPTII codes for numerator compliance, nor are they indicated anywhere in our measure specification. Measure specifications focus on the electronic health record as a source of clinical information for calculating numerator compliance; actual A1c values are utilized in the case of the A1c target. Additionally, 80 to 90% of all the clinics in MN are reporting this information from their electronic health records without the need for additional chart abstraction. • One commenter suggested a need for including sociodemographic factors in the risk-adjustment approach. Developer response: Our risk adjustment model does include insurance product, which is a proxy for socioeconomic status. During the process of measure development, the expert panel discusses potential variables for risk adjustment that are important to consider for the measured population. For this measure, variables that are available for evaluation include gender, age, zip, race/ethnicity, country of origin, primary language, insurance product, diabetes type, depression and ischemic vascular disease. The potential risk adjustment variables are then evaluated for appropriate inclusion in the model based on a t value outside the range of -2.0 and +2.0. Currently, the variables that have demonstrated acceptable properties are insurance product, age bands (18-25, 26-50, 51-65 and 65 to 75) and diabetes type (1 or 2). Race/ethnicity has been collected for this measure in MN for the past few years, but has now reached a level of 15 reliability in which it can be evaluated for its impact. MNCM continues to review variables and their impact on the measure and part of its measure risk adjustment strategy. • One commenter suggested the need for additional detail regarding moderate or high intensity in the description of statin use for the measure. Developer response: The measure development work group thoroughly discussed the pros and cons of specifying a certain dose of the statin medication and based on the following factors ultimately decided to not specify a dose of moderate or high intensity for numerator compliance: 1) data burden for practices, 2) controversy and burden surrounding the CV risk calculator, 3) ICSI 2014 Diabetes Guideline recommendations for measurement and 4) cardiology work group member’s believe that there is some benefit for some patients who can only tolerate a lower intensity dose. Committee response: • During its review of the individual measure assessing HbA1c
  
  
• Endorsement Committee Recommendation: Standing Committee Recommendation for Endorsement: Y-13; N-4
  
  

Measure Specifications

• NQF Number (if applicable): *Note- 76 (This measure is a component of the endorsed composite measure NQF #76)
  
  
• Description: The percentage of patients 18-75 years of age who had a diagnosis of ischemic vascular disease (IVD) and were on daily aspirin or anti-platelet medication, unless allowed contraindications or exceptions are present.
  
  
• Numerator: Denominator patients with documentation that the patient was on daily aspirin or anti-platelet medication during the measurement period, unless allowed contraindications or exceptions are present.
  
  
• Denominator: 18 years or older at the start of the measurement period AND less than 76 years at the end of the measurement period AND Patient had a diagnosis of ischemic vascular disease (Ischemic Vascular Disease Value Set) with any contact during the current or prior measurement period OR had ischemic vascular disease (Ischemic Vascular Disease Value Set) present on an active problem list at any time during the measurement period. AND At least one established patient office visit (Established Pt Diabetes & Vasc Value Set) for any reason during the measurement period
  
  
• Exclusions: The following exclusions are allowed to be applied to the eligible population: - Patient was a permanent nursing home resident at any time during the measurement period - Patient was in hospice or receiving palliative care at any time during the measurement period - Patient died prior to the end of the measurement period - Documentation that diagnosis was coded in error - Patient had only urgent care visits during the measurement period
  
  
• HHS NQS Priority: Effective Prevention and Treatment
  
  
• HHS Data Source: Administrative clinical data, EHR, Paper medical record
  
  
• Measure Type: Process
  
  
• Steward: MN Community Measurement
  
  
• Endorsement Status: Endorsed
  
  
• Changes to Endorsed Measure Specifications?: The MUC list indicates the measure has not been modified from its endorsed version.
  
  
• Is the measure specified as an electronic clinical quality measure? No
  
  

Summary of Workgroup Deliberations

• Workgroup Recommendation: Conditional Support for Rulemaking
  
  
• Workgroup Rationale: MAP acknowledged the importance of Use of Aspirin or Anti-platelet Medication as a critical element of high quality vascular care. While this measure is included in the Optimal Vascular Care composite measure, MAP recognized that clinicians may still report Aspirin or Anti-platelet Medication measures separately to drive quality improvement. MAP also discussed that there is a competing measure in the program, QPP #204: IVD Use of Aspirin or Another Antiplatelet. MAP Conditionally Supported this measure with the condition that there are no competing measures in the program.
  
  
• Public comments received: 0

Rationale for measure provided by HHS
Risk Factor Optimization and Guideline-Directed Medical Therapy in US Veterans With Peripheral Arterial and Ischemic Cerebrovascular Disease Compared to Veterans With Coronary Heart Disease. Hira RS et al Am J Cardiol. 2016 Oct 15;118(8):1144-1149. doi: 10.1016/j.amjcard.2016.07.027. Epub 2016 Jul 29. Age-specific risks, severity, time course and outcome of bleeding on long-term anti-platelet treatment after vascular events: a population based cohort study. Linix, L et al Published online June 13, 2017 http://dx.doi.org/10.1016/S0140-6736(17)30770-5

Summary of NQF Endorsement Review

• Year of Most Recent Endorsement Review: 2017
  
  
• Project for Most Recent Endorsement Review: Cardiovascular 2016-2017
  
  
• Review for Importance: 1. Importance to Measure and Report: The measure meets the Importance criteria (1a. Evidence, 1b. Performance Gap, 1c. Composite) 1a. Evidence: H-14; M-6; L-1; I-1; 1b. Performance Gap: H-14; M-7; L-1; I-0; Composite: H-12; M-8; L-1; I- 1 Rationale: • For the 2012 maintenance of endorsement evaluation, the developer provided the following clinical practice guidelines to support the blood pressure, statin medication, tobacco free (outcome measure), and daily aspirin or anti-platelet medication components: o Blood pressure, statin medication, tobacco free, and daily aspirin or anti-platelet medication components: The ICSI Stable Coronary Artery Disease (April 2011), Address Modifiable Risk Factors guideline recommended modifiable risk factors for coronary artery disease such as smoking, inadequate physical activity, stress, hyperlipidemia, obesity, hypertension and diabetes mellitus be evaluated. 21 o Blood pressure: The Comorbid Conditions Guideline and the ICSI Hypertension Diagnosis and Treatment Guideline (November 2010) recommended a target blood pressure of 140/90 mmHg or less. o Statin medication: The ICSI Lipid Management in Adults (October 2009) guideline recommended target goals for hyperlipidemic patients with coronary artery disease: LDL – less than 100 mg/dL; HDL – 40 mg/dL or greater; Triglycerides – less than 150 mg/dL. o Daily aspirin or anti-platelet medication: The ICSI Stable Coronary Artery Disease (April 2011), Address Modifiable Risk Factors guideline recommended the use of one aspirin tablet daily (81-162 mg) unless there are medical contraindications. • For the current maintenance of endorsement evaluation, the developer provided the following updated evidence for all four components: o Blood pressure: The 2015 AHA/ACC/ASH Scientific Statement on the Treatment of Hypertension in Patients with Coronary Artery Disease included 3 recommendations for blood pressure targets, including a blood pressure goal of 65 and Medicare. •o Statin medication: The ICSI Lipid Management in Adults (updated Nov 2013/completed prior to ACC/AHA release) recommends that clinicians initiate statin therapy regardless of LDL in patients with established atherosclerotic cardiovascular disease (ASVCD). The 2013 ACC/AHA Guideline for the Treatment of Blood Cholesterol to Reduce Atherosclerotic Cardiovascular Risk in Adults recommends high-intensity statin therapy be initiated or continued as first-line therapy in women and meno Tobacco free outcome measure: The developer provided evidence from the United States Preventive Services Task Force (USPSTF) stating that despite considerable progress in tobacco control over the past 50 years, in 2013, an estimated 17.8% of U.S. adults and 15.9% of pregnant women aged 15 to 44 years were current cigarette smokers. o Daily aspirin or anti-platelet medication: The developer provided three recommendations for antiplatelet agents/anticoagulants for patients with ischemic vascular disease from the AHA/ACCF Secondary Prevention and Risk Reduction Therapy for Patients with Coronary and Other Atherosclerotic Vascular Disease: 2011 Update. • The Standing Committee discussed the potential changes to blood pressure parameters based on the results of the Systolic Blood Pressure Intervention Trial (SPRINT), which compared the benefit of treatment of systolic blood pressure to a target of less than 120 mm Hg with treatment to a target of less than 140 mm Hg. The Committee also discussed the anticipated blood pressure guidelines to be released by AHA/ACC sometime in the future. NQF staff asked the Committee to consider the quantity, quality, and consistency of the body of evidence that was presented in the measure submission form. NQF staff reassured the Committee that the NQF process allows for a measure to be reviewed when new evidence becomes available. One of the Committee members noted that the USPSTF recommendations for daily aspirin include patients aged 50 to 70 years old, while the measure includes patients up to 75 years old. Other Committee members noted that the USPSTF recommendations are for primary prevention rather than patients with a diagnosis of ischemic vascular disease (IVD). Overall, the Standing Committee agreed that the updated evidence supports blood pressure control, statin use, daily aspirin or anti-platelet medication, and tobacco use assessment and 22 intervention(s) in patients to avoid or postpone long-term complications associated with a diagnosis with IVD. The developer provided composite performance rates from clinics in Minnesota for Report Year 2007-2016 (Dates of Service 2006-2015). o In 2007, the rate was 38.9% for 4,662 patients and 33.8% in 2010 for 63,241 patients. In 2011, the blood pressure component target changed from, and the performance rate increase to 39.7% for 66,910 patients. o In 2015, the cholesterol management component was suppressed during redesign of the measure and the performance rate increased to 69.3% for 102,654 patients. o In 2016, the cholesterol management component was changed from LDL <100 to appropriate statin use and the performance rate was 66.1% for 104,395 patients. • The developer also provided performance rates for the individual components. o The blood pressure component increased from 84.0% in 2012 to 85.0% in 2016. o Daily aspirin use or anti-platelet medication use increased from 92.5% in 2009 to 96.7% in 2016. o The number of tobacco free patients increased from 82.4% in 2009 to 83.0 in 2016. o Statin use was 95.2% in 2016 (this was the first year the new component was reported) The developer provided 2014 disparities data from the measure as specified demonstrating a performance rate of 67.2% for White patients, 47.6% for Black/African-American patients, 51.8% for American Indian/Alaska Native patients, and 53.4% for multi-racial patients. The data also showed a higher performance gap for female patients and younger patients. The Committee asked the developer if there were trend data on disparities that demonstrated a change in performance over time and by individual clinic. The developer did not have additional, specific disparities data. However, according to the developer, some clinics that care for a greater proportion of minority patients have lower performance rates but there are a couple of clinics that are excelling in minimizing disparities. • The Standing Committee agreed that the data provided demonstrated a performance gap and opportunity for improvement in optimal vascular care for patients with IVD. • This is an all-or-none composite measure that requires patients to meet all four component targets in the composite measure to be considered ‘optimally managed’; all four components are weighted equally. The developer noted that measuring providers on individual targets is not as patient-centric as this composite measure that seeks to reduce multiple risk factors in patients with IVD and maximize health outcomes. One of the members of the Standing Committee noted that the tobacco free component would be more appropriate as a process measure. The Committee member noted that smoking rates are often influenced by geographic location. Providers in areas with high rates of tobacco use will not appear as effective in increasing the number of tobacco free patients as those in areas where tobacco use is less prevalent. In the pre-evaluation comments, another Committee member noted that the absolute benefit of each component is not equal; achieving blood pressure control or smoking cessation is much more difficult than prescribing a statin or aspirin/anti-platelet medication. • The Standing Committee agreed, that overall, the quality construct and rational for the composite was clearly stated and logical.
  
  
• Review for Scientific Acceptability: 2. For the 2012 maintenance of endorsement evaluation, patient-level data element validity testing was conducted on 63,241 patients with IVD from 128 medical groups representing 573 clinics that submitted data to Minnesota Community Measurement for 2009 dates of service reported in 2010. After data submission, in-person validation audits requiring a 90% accuracy rate were conducted to compare the submission to the patient’s medical record. Of the 128 medical groups that submitted data in 2010, 17 groups initially failed the audit and remedy plans were developed. All 17 groups resubmitted and passed subsequent audit. • For the current maintenance of endorsement evaluation, the measure was tested at the measure score level using a dataset that included 104,395 patients with IVD in Minnesota and neighboring communities from 111 medical groups representing 671 clinics for dates of service from January 1, 2015 to December 31, 2015. • To test the reliability of the measure score, the developer used a beta-binomial model to assess the signal-to-noise ratio. A reliability score of 0.00 implies that all the variability in a measure is attributable to measurement error. A reliability score of 1.00 implies that all the variability is attributable to real differences in performance. The higher the reliability score, the greater is the confidence with which one can distinguish the performance of one facility from another. This is an appropriate test for measure score reliability. A reliability score of 0.70 is generally considered a minimum threshold for reliability. The overall reliability for the composite measure was 0.90 and 0.61 at the minimum number of patients per reportable clinic (=30). The distribution of reliability scores by number of eligible patients per reportable clinic (=30) ranged from 0.61 for 30 patients per clinic to 0.99 for 4,441 patients per clinic. • In the pre-evaluation comments, a member of the Standing Committee mentioned that assessing prescribing behavior of statin therapy (as noted in the specifications) is not consistent with the evidence provided to support the statin component. The Committee member noted that prescribing the lowest dose of the weakest statin would meet the intent of the measure but not generate clinically significant outcomes in the IVD population. Other Committee members questioned why ‘permanent nursing home residents’ are excluded from the denominator. The Committee discussed the fall risks associated with administering blood pressure medication to nursing home patients, excessive treatment in patients with advanced illness, and the lack of clinical trials for these types of medications in the nursing home population. • The Standing Committee did not express additional concerns with the reliability of the measure, but ultimately decided the testing results were sufficient. • For the 2012 maintenance of endorsement evaluation, content and face validity were assessed through the Measurement and Reporting Committee and a panel of experts. There was consensus among the expert workgroup that the target components reflected a quality of care that will reduce patients heart attack and stroke risk. • For the current maintenance of endorsement evaluation, empirical validity testing of the composite measure score was conducted by testing the correlation of a medical group’s performance with their performance on the Optimal Diabetes Care measure (#0729). It is expected that the quality of care provided by a medical group to a patient with ischemic vascular disease would be of similar quality as the care provided to a patient with diabetes, therefore the respective performance measure scores should be similar. This is an appropriate method for assessing conceptually and theoretically sound hypothesized relationships. The Optimal Diabetes Care measure (#0729) includes the same four components as #0074 plus a component for hemoglobin A1C; it also measures a different population. The linear regression analysis demonstrated an R2 value of 0.635, which means that 64.0% of the total variation in performance on the Optimal Vascular Care measure can be explained by variation in performance on the Optimal Diabetes Care measure. The remaining 36.0% of total variation on the Optimal Vascular Care measure remains unexplained. • This measure is risk-adjusted. The final risk factors selected for the risk model were age and insurance product (Medicare, Medicaid, MSHO, Special Needs, Self-pay, Uninsured). The developer analyzed gender and depression as well, but gender did not show sufficient variation between clinics and ‘depression’ was not selected due to the high cost of collection. The developer stated that race, ethnicity, language, and country of origin (RELO) were not considered for risk adjustment because these variables did not have a high completion rate across all clinics. The developer is continuing to work with the medical community to achieve the goal of evaluating RELO at the clinic level. The developer conducted an Analysis of Maximum Likelihood Estimates on the 2014 Dates of Service to compare the optimal rate of patients by insurance product (Commercial, MHCP, and Uninsured) to patients with Medicare and patient age (18-25; 26-50; 51-65) to patients aged 66-75. The Analysis of Maximum Likelihood Estimates demonstrated that all of the results for both variables, age and insurance product, were significant, except for ages less than 26 due to the small sample size (n = 44). The developer also found that the only two variables that were correlated were age >65 and Medicare. The Standing Committee did not express any concerns on the threats to validity and agreed that the testing results satisfied the validity criterion. • The developer conducted a Pearson Correlation Analysis of the individual components rates and the composite rates. The Pearson correlation coefficient value, r, ranges from +1.00 to -1.00. A value of 0.00 indicates that there is no association between the two variables. A value greater than 0.00 indicates a positive association; that is, as the value of one variable increases, so does the value of the other variable. A value less than 0.00 indicates a negative association; that is, as the value of one variable increases, the value of the other variable decreases. The developer conducted the following Pearson Correlation Analysis for each component: Variable Mean Pearson r coefficient Blood pressure 0.85048 0.69813 Tobacco Free 0.80901 0.71336 Daily ASA Use 0.96271 0.59223 Statin Use 0.93973 0.62327 Optimal Vascular Care Rate = 0.63919 • The developer concluded that practices in Minnesota demonstrate relatively high compliance for all of the components; however, there is still an opportunity for improvement at the clinic level. The blood pressure control and tobacco free components demonstrated the most variability, opportunity for improvement, and impact the ability to achieve all four components. Another Committee member suggested that if the two variables with the most variability were more heavily weighted than the other components, the measure would be more impactful. Another Committee member pointed out that three of the components were under the direct control of the provider, yet it was not clear how the tobacco free component captured the quality of care provided by the clinician. A member of the Standing Committee questioned whether there was evidence showing that meeting all four component targets would not 25 generate the same patient outcomes as meeting two or three of the components. The developer pointed out that various combinations of the components and the proportion of patients meeting the different combinations were provided. • The Standing Committee did not express additional concerns with the construct of the composite measure and agreed the information provided was sufficient to satisfy the criterion for composite construct.
  
  
• Review for Feasibility: 3. Feasibility: H-12; M-10; L-0; I-0 (3a. Clinical data generated during care delivery; 3b. Electronic sources; 3c. Susceptibility to inaccuracies/ unintended consequences identified 3d. Data collection strategy can be implemented) Rationale: • All of the data elements are in defined fields in electronic sources and there are no fees, licensure, or other requirements necessary to use this measure. The Standing Committee agreed this measure met the feasibility criterion.
  
  
• Review for Usability: 4. Usability and Use: H-13; M-8; L-1; I-0 (Used and useful to the intended audiences for 4a. Accountability and Transparency; 4b. Improvement; and 4c. Benefits outweigh evidence of unintended consequences) Rationale: • The measure is widely used in Minnesota for public reporting, payment, regulatory and accreditation programs, and quality improvement with external benchmarking to multiple organizations. 5. Related and Competing Measures • This measure is related to: o #0067: Chronic Stable Coronary Artery Disease: Antiplatelet Therapy (ACC) o #0068: Ischemic Vascular Disease (IVD): Use of Aspirin or Another Antiplatelet (NCQA) o #0073: Ischemic Vascular Disease (IVD): Blood Pressure Control (NCQA) • The developer stated that #0068 and #0073 focus on the inpatient setting and patients discharged with AMI, CABG, or PCI. #0067 focuses on patients with CAD.
  
  
• Review for Related and Competing Measures: 5. Related and Competing Measures • This measure is related to: o #0067: Chronic Stable Coronary Artery Disease: Antiplatelet Therapy (ACC) o #0068: Ischemic Vascular Disease (IVD): Use of Aspirin or Another Antiplatelet (NCQA) o #0073: Ischemic Vascular Disease (IVD): Blood Pressure Control (NCQA) • The developer stated that #0068 and #0073 focus on the inpatient setting and patients discharged with AMI, CABG, or PCI. #0067 focuses on patients with CAD.
  
  
• Endorsement Public Comments: 6. Public and Member Comment • One commenter did not agree with statin use as a component to address dyslipidemia and believed it would be misleading to include this as a component of “optimal care.” The commenter believed including this component would lead to the lowest level of acceptable care being considered optimal care and would do little to move the quality of care forward. • Developer Response: Thank you for your comment and suggestion for the inclusion of the dose of statin (moderate or high) in the calculation of the cholesterol component of this patient level all-or-none composite measure. While ACC/ AHA guidelines do indicate that most patients with ischemic vascular disease would benefit from high dose intensity statins, there 26 are provisions for moderate intensity statins for patients who cannot tolerate high intensity doses. The measure development work group thoroughly discussed the pros and cons of specifying a certain dose of the statin medication for numerator component compliance and determined that requiring the submission of the dose of statin would cause undue data collection burden for the practices. Additionally, the cardiologists on the workgroup strongly believe that there is some benefit for patients who can only tolerate a low dose of statin. We do not discount the role of ongoing LDL monitoring to determine effectiveness of statin therapy, but having a physiological target (e.g. LDL < 100) is no longer supported by evidence. The American College of Cardiology/ American Heart Associate guidelines for the treatment of blood cholesterol indicate the following: “Treat to target — this strategy has been the most widely used the past 15 years but there are 3 problems with this approach. First, current clinical trial data do not indicate what the target should be. Second, we do not know the magnitude of additional ASCVD risk reduction that would be achieved with one target lower than another. Third, it does not take into account potential adverse effects from multidrug therapy that might be needed to achieve a specific goal. Thus, in the absence of these data, this approach is less useful than it appears (Section 3). It is possible that future clinical trials may provide information warranting reconsideration of this strategy” (pg. 17) Yes, our component rates for prescribing statins are high in MN, which is a little bit unexpected for the newly re-designed component, however we would like to clarify the cholesterol component of statin use is not reported as a stand-alone measure. The Optimal Vascular Care measure is reported as an all-or-none composite, patients achieving multiple components of modifiable risk factors to reduce or delay long term complications. Statin use is one component, the other three are blood pressure control, tobacco-free and daily aspirin or antiplatelet medication. • Committee Response: Thank you for your comment. The Committee agrees that monitoring LDL levels remains an important part of providing care for patients with IVD. However, the statin component in this measure aligns with the 2013 ACC/AHA Guideline for the Treatment of Blood Cholesterol to Reduce Atherosclerotic Cardiovascular Risk in Adults.
  
  
• Endorsement Committee Recommendation: Standing Committee Recommendation for Endorsement: Y-19; N-3
  
  

Measure Specifications

• NQF Number (if applicable):
  
  
• Description: The Routine Cataract Removal with IOL Implantation Cost Measure applies to clinicians who perform routine cataract removal with IOL implantation procedures for Medicare beneficiaries. The cost measure is calculated by determining the risk-adjusted episode cost, averaged across all of a clinician’s episodes during the measurement period. The cost of each episode is the sum of the cost to Medicare for services performed by the attributed clinician and other healthcare providers during the episode window (from 60 days prior to the trigger date to 90 days after the trigger date).
  
  
• Numerator: The numerator of the Routine Cataract Removal with IOL Implantation cost measure is the sum of the ratio of observed to expected payment-standardized cost to Medicare for all episodes attributed to a clinician. This is then multiplied by the national average observed episode cost to generate a dollar figure.
  
  
• Denominator: The cost measure denominator is the total number of episodes from the Routine Cataract Removal with IOL Implantation episode group attributed to a clinician.
  
  
• Exclusions: The following episode-level exclusions apply: (a) The beneficiary has a primary payer other than Medicare for any amount of time overlapping the episode window or in the 120 days prior to the episode trigger day. (b) No attributed clinician is found for the episode. (c) The episode is not attributed to at least one main clinician. (d) The beneficiary’s date of birth is missing. (e) The beneficiary’s death date occurred before the trigger date. (f) The beneficiary’s death date occurred before the episode ended. (g) The beneficiary was not enrolled in Medicare Part A and B for the entirety of the 120-day lookback period plus episode window, or is enrolled in Part C for any part of the lookback period plus episode window. (h) The episode trigger claim was not performed in an office, IP, OP, or ASC setting based on its place of service. Routine Cataract Removal with IOL Implantation episodes are also removed using exclusions specific to the Routine Cataract Removal with IOL Implantation measure that were developed with input from the Ophthalmologic Disease Management Clinical Subcommittee. The “Exclusions” and “Exclusions_Details” tabs in the Routine Cataract Removal with IOL Implantation Measure Codes List file include the list of these exclusions as well as the codes used to define them.
  
  
• HHS NQS Priority: Making Care Affordable
  
  
• HHS Data Source: Claims
  
  
• Measure Type: Cost/Resource Use
  
  
• Steward: Centers for Medicare & Medicaid Services
  
  
• Endorsement Status: Never Submitted
  
  
• Is the measure specified as an electronic clinical quality measure? No
  
  

Summary of Workgroup Deliberations

• Workgroup Recommendation: Conditional Support for Rulemaking
  
  
• Workgroup Rationale: MAP recognized the importance of cost measures to the MIPS program. The MAP Conditionally Supported this Routine Cataract Removal with Intraocular Lens (IOL) Implantation cost measure pending NQF endorsement. During the NQF endorsement review, the MAP encourages the Cost and Resource Use Standing Committee to specifically consider the appropriateness of the risk adjustment model to ensure clinical and social risk factors are reviewed and included when appropriate. MAP cautioned about the potential stinting of care and noted that appropriate risk adjustment could help safe guard against this practice. The Standing Committee should also examine the exclusions in the attribution rule for this measure.
  
  
• Public comments received: 3

Rationale for measure provided by HHS
Among adults in the United States, cataracts constitute the leading cause of visual impairment, and cataract surgery is the only treatment option for removing cataracts, thereby reversing the visual impairment caused by cataracts (Tseng et al., 2016). Routine cataract surgery is the most frequent surgical procedure in the United States, including among Medicare beneficiaries (Pershing et al., 2016). A study found that there were about 2.3 million procedures for Medicare beneficiaries in 2014, and Medicare covers more than 80 percent of cataract surgeries in the United States (French et al., 2017). In addition, it was estimated that Medicare spends more than $3.4 billion annually on the treatment of cataracts, and cataract extraction with IOL implantation was the most common procedure (Brown et al., 2013). References: Martin, Anne B., Micah Hartman, Benjamin Washington, Aaron Catlin, and the National Health Expenditure Accounts Team. "National Health Spending: Faster Growth in 2015 as Coverage Expands and Utilization Increases." Health Affairs (December 2, 2016 2016). Kaiser Family Foundation. “A Primer on Medicare: Key Facts About the Medicare Program and the People it Covers.” (March 2015) Brown, G. C., M. M. Brown, A. Menezes, B. G. Busbee, H. B. Lieske, and P. A. Lieke. “Cataract Surgery Cost Utility Revisited in 2012: A New Economic Paradigm.” [In eng]. Ophthalmology 120, no. 12 (Dec 2013): 2367-76. French, D. D., C. E. Margo, J.J. Behrens, and P. B. Greenberg. “Rates of Routine Cataract Surgery among Medicare Beneficiaries.” [In eng]. JAMA Ophthalmol (Jan 05 2017). Pershing, S., D. E. Morrison, and T. Hernandez-Boussard. “Cataract Surgery Complications and Revisit Rates among Three States.” [In eng]. Am J Ophthalmol 171 (Nov 2016): 130-38. Tseng, V. L., F. Yu, F. Lum, and A. L. Coleman. “Cataract Surgery and Mortality in the United States Medicare Population.” [In eng]. Ophthalmology 123, no. 5 (May 2016): 1019-26.

Measure Specifications

• NQF Number (if applicable):
  
  
• Description: Percentage of patients with an office visit within the measurement period and with a new diagnosis of clinically significant Benign Prostatic Hyperplasia who have International Prostate Symptoms Score (IPSS) or American Urological Association (AUA) Symptom Index (SI) documented at time of diagnosis and again 6 to 12 months later with an improvement of 3 points.
  
  
• Numerator: IPSS or AUASI documented at or within 1 month of BPH diagnosis and again documented 6 to 12 months after treatment initiated, showing a 3 point improvement
  
  
• Denominator: Equals initial population, which is Male patients with a new diagnosis of benign prostatic hyperplasia and an office visit during the measurement period
  
  
• Exclusions: Denominator Exclusion Patient refusal to complete IPSS or AUASI document. Denominator exceptions: Urinary retention , BPH diagnosis during hospitalization or within 30 days of hospitalization
  
  
• HHS NQS Priority: Patient and Family Engagement
  
  
• HHS Data Source: Administrative clinical data, EHR , Registry, Survey, Other
  
  
• Measure Type: Outcome
  
  
• Steward: Large Urology Group Practice Association In collaboration with Oregon Urology Institute
  
  
• Endorsement Status: Never Submitted
  
  
• Is the measure specified as an electronic clinical quality measure? Yes
  
  

Summary of Workgroup Deliberations

• Workgroup Recommendation: Conditional Support for Rulemaking
  
  
• Workgroup Rationale: This measure addresses the clinical topic of benign prostatic hyperplasia. MAP acknowledges that this measure would serve as the only measure to capture longitudinal symptomatic improvement in men suffering from a benign prostatic hyperplasia. MAP Conditionally Supports this measure with the condition that the measure is submitted for NQF endorsement.
  
  
• Public comments received: 1

Rationale for measure provided by HHS
The symptoms of BPH are LUTS symptoms. There are other disorders with similar symptoms and need to be excluded. History, physical examination and testing are required prior to a diagnosis of BPH. IPSS by itself is not a reliable diagnostic tool for LUTS suggestive of BPH, but serves as a quantitative measure of LUTS after the diagnosis is established (DSilva,2014) Medical and surgical interventions for BPH recommend a follow up IPSS evaluation to determine effectiveness of treatment. IPSS should be evaluated at the time of diagnosis and after definitive treatment.

Measure Specifications

• NQF Number (if applicable):
  
  
• Description: The Screening/Surveillance Colonoscopy cost measure applies to clinicians who perform screening/surveillance colonoscopy procedures for Medicare beneficiaries. The cost measure is calculated by determining the risk-adjusted episode cost, averaged across all of a clinician’s episodes during the measurement period. The cost of each episode is the sum of the cost to Medicare for services performed by the attributed clinician and other healthcare providers during the episode window (from the trigger date to 14 days after the trigger date).
  
  
• Numerator: The numerator of the Screening/Surveillance Colonoscopy cost measure is the sum of the ratio of observed to expected payment-standardized cost to Medicare for all episodes attributed to a clinician. This is then multiplied by the national average observed episode cost to generate a dollar figure.
  
  
• Denominator: The cost measure denominator is the total number of episodes from the Screening/Surveillance Colonoscopy episode group attributed to a clinician.
  
  
• Exclusions: The following episode-level exclusions apply: (a) The beneficiary has a primary payer other than Medicare for any amount of time overlapping the episode window or in the 120 days prior to the episode trigger day. (b) No attributed clinician is found for the episode. (c) The episode is not attributed to at least one main clinician. (d) The beneficiary’s date of birth is missing. (e) The beneficiary’s death date occurred before the trigger date. (f) The beneficiary’s death date occurred before the episode ended. (g) The beneficiary was not enrolled in Medicare Part A and B for the entirety of the 120-day lookback period plus episode window, or is enrolled in Part C for any part of the lookback period plus episode window. (h) The episode trigger claim was not performed in an office, IP, OP, or ASC setting based on its place of service.
  
  
• HHS NQS Priority: Making Care Affordable
  
  
• HHS Data Source: Claims
  
  
• Measure Type: Cost/Resource Use
  
  
• Steward: Centers for Medicare & Medicaid Services
  
  
• Endorsement Status: Never Submitted
  
  
• Is the measure specified as an electronic clinical quality measure? No
  
  

Summary of Workgroup Deliberations

• Workgroup Recommendation: Conditional Support for Rulemaking
  
  
• Workgroup Rationale: MAP recognized the importance of this Screening/Surveillance Colonoscopy cost measure given the volume of this procedure. MAP Conditionally Supported this measure pending NQF endorsement. During the NQF endorsement review, the MAP encourages the Cost and Resource Use Standing Committee to specifically consider the appropriateness of the risk adjustment model to ensure clinical and social risk factors are reviewed and included when appropriate. MAP cautioned about the potential stinting of care and noted that appropriate risk adjustment could help safe guard against this practice. Additionally, MAP expressed concern over the precision of the cohort definition and whether there was a sufficiently large cost performance distribution in this measure.
  
  
• Public comments received: 2

Rationale for measure provided by HHS
According to the American Cancer Society, colorectal cancer (CRC) is the third most diagnosed cancer among adults in the United States, with an estimated 135,430 new cases of CRC to be diagnosed in 2017, and with about 58 percent of the cases occurring in adults ages 65 and older (Siegel et al., 2017). The CRC screening guidelines released by the United States Preventive Services Task Force (USPSTF) recommend either a screening colonoscopy every 10 years or other screening methods, for adults ages 50 through 75 who are at average risk for developing CRC (Bibbins-Domingo et al., 2016). Although there are a number of CRC screening methods available, screening colonoscopy has become the most common CRC screening test in the United States (Sharaf and Ladabaum, 2013). In the past 10 years, the proportion of Medicare beneficiaries ages 65 and older who have received a colonoscopy since qualifying for Medicare at age 65 have increased from 25 percent in 2000 to 63 percent in 2013 (National Center for Health Statistics, 2016). A study found that in 2012, an estimated $239 million worth of professional fees were paid by Medicare to physicians for performing about 1.1 million screening and diagnostic colonoscopies (Mehta and Manaker, 2014). References: Siegel, R. L., K. D. Miller, S. A. Fedewa, D. J. Ahnen, R. G. Meester, A. Barzi, and A. Jemal. “Colorectal Cancer Statistics, 2017.” [In eng]. CA Cancer J Clin (Mar 1 2017). Bibbins-Domingo, K., D. C. Grossman, S.J. Curry, K. W. Davidson, J. W. Epling, Jr., F. A. Garcia, M. W. Gillman, et al. “Screening for Colorectal Cancer: Us Preventive Services Task Force Recommendation Statement.” [In eng]. JAMA 315, no. 23 (Jun 21, 2016): 2564-75. Sharaf, Ravi N., and Uri Ladabaum. “Comparative Effectiveness and Cost-Effectiveness of Screening Colonoscopy Vs. Sigmoidoscopy and Alternative Strategies.” The American Journal of Gastroenterology 108, no. 1 (2013): 120-32. In Health, United States, 2015: With Special Feature on Racial and Ethnic Health Disparities. Health, United States. Hyattsville, MD, 2016. Mehta, Shivan J., and Scott Manaker. “Should We Pay Doctors Less for Colonoscopy?”. American Journal of Managed Care 20, no. 9 (2014): e365-e68.

Measure Specifications

• NQF Number (if applicable):
  
  
• Description: The Knee Arthroplasty cost measure applies to clinicians who perform elective total and partial knee arthroplasties for Medicare beneficiaries. The cost measure is calculated by determining the risk-adjusted episode cost, averaged across all of a clinician’s episodes during the measurement period. The cost of each episode is the sum of the cost to Medicare for services performed by the attributed clinician and other healthcare providers during the episode window (from 30 days prior to the trigger date to 90 days after the trigger date).
  
  
• Numerator: The numerator of the Knee Arthroplasty cost measure is the sum of the ratio of observed to expected payment-standardized cost to Medicare for all episodes attributed to a clinician. This is then multiplied by the national average observed episode cost to generate a dollar figure.
  
  
• Denominator: The cost measure denominator is the total number of episodes from the Knee Arthroplasty episode group attributed to a clinician.
  
  
• Exclusions: The following episode-level exclusions apply: (a) The beneficiary has a primary payer other than Medicare for any amount of time overlapping the episode window or in the 120 days prior to the episode trigger day. (b) No attributed clinician is found for the episode. (c) The episode is not attributed to at least one main clinician. (d) The beneficiary’s date of birth is missing. (e) The beneficiary’s death date occurred before the trigger date. (f) The beneficiary’s death date occurred before the episode ended. (g) The beneficiary was not enrolled in Medicare Part A and B for the entirety of the 120-day lookback period plus episode window, or is enrolled in Part C for any part of the lookback period plus episode window. (h) The episode trigger claim was not performed in an office, IP, OP, or ASC setting based on its place of service. Knee Arthroplasty episodes are also removed using exclusions specific to the Knee Arthroplasty measure that were developed with input from the Musculoskeletal Disease Management - Non-Spine Clinical Subcommittee. The “Exclusions” and “Exclusions_Details” tabs in the Knee Arthroplasty Measure Codes List file include the list of these exclusions as well as the codes used to define them.
  
  
• HHS NQS Priority: Making Care Affordable
  
  
• HHS Data Source: Claims
  
  
• Measure Type: Cost/Resource Use
  
  
• Steward: Centers for Medicare & Medicaid Services
  
  
• Endorsement Status: Never Submitted
  
  
• Is the measure specified as an electronic clinical quality measure? No
  
  

Summary of Workgroup Deliberations

• Workgroup Recommendation: Conditional Support for Rulemaking
  
  
• Workgroup Rationale: MAP recognized the importance of this Knee Arthroplasty cost measure. MAP Conditionally Supported this measure pending NQF endorsement. During the NQF endorsement review, the MAP encouraged the Cost and Resource Use Standing Committee to specifically consider the appropriateness of the risk adjustment model to ensure clinical and social risk factors are reviewed and included when appropriate. MAP cautioned about the potential stinting of care and noted that appropriate risk adjustment could help safe guard against this practice. Additionally, MAP expressed concern over the precision of the cohort definition and whether there was a sufficiently large cost performance distribution in this measure.
  
  
• Public comments received: 4

Rationale for measure provided by HHS
An estimated 45 percent of adults in the United States are at risk for developing knee osteoarthritis during their lifetimes, and as a result, the rate of Medicare enrollees receiving knee arthroplasties, or knee replacements, has been increasing. Between 1991 and 2010, the number of knee arthroplasties increased from 93,230 to 243,802, an increase of more than 160 percent (Cram et al., 2012). A 2012 study observed that 615,050 knee arthroplasties were performed in 2008, a 134 percent increase from 1999, and predicted continued increases at a rate greater than predicted by population growth and prevalence of obesity (Losina et al., 2012). References: Cram, Peter, Xin Lu, Stephen L. Kates, Jasvinder A. Singh, Yue Li, and Brian R. Wolf. "Total knee arthroplasty volume, utilization, and outcomes among Medicare beneficiaries, 1991-2010." Jama 308, no. 12 (2012): 1227-1236. Losina, E., T. S. Thornhill, B. N. Rome, J. Wright, and J. N. Katz. "The Dramatic Increase in Total Knee Replacement Utilization Rates in the United States Cannot Be Fully Explained by Growth in Population Size and the Obesity Epidemic." [In eng]. J Bone Joint Surg Am 94, no. 3 (Feb 01 2012): 201-7.

Measure Specifications

• NQF Number (if applicable):
  
  
• Description: The STEMI with PCI cost measure applies to clinicians who manage the inpatient care of Medicare beneficiaries hospitalized for a STEMI requiring PCI. The cost measure is calculated by determining the risk-adjusted episode cost, averaged across all of a clinician’s episodes during the measurement period. The cost of each episode is the sum of the cost to Medicare for services performed by the attributed clinician and other healthcare providers during the episode window (from the trigger date to 30 days after the trigger date).
  
  
• Numerator: The numerator of the STEMI with PCI cost measure is the sum of the ratio of observed to expected payment-standardized cost to Medicare for all episodes attributed to a clinician. This is then multiplied by the national average observed episode cost to generate a dollar figure.
  
  
• Denominator: The cost measure denominator is the total number of episodes from the STEMI with PCI episode group attributed to a clinician.
  
  
• Exclusions: The following episode-level exclusions apply: (a) The beneficiary has a primary payer other than Medicare for any amount of time overlapping the episode window or in the 120 days prior to the episode trigger day. (b) No attributed clinician is found for the episode. (c) The episode is not attributed to at least one main clinician. (d) The beneficiary’s date of birth is missing. (e) The beneficiary’s death date occurred before the trigger date. (f) The beneficiary’s death date occurred before the episode ended. (g) The beneficiary was not enrolled in Medicare Part A and B for the entirety of the 120-day lookback period plus episode window, or is enrolled in Part C for any part of the lookback period plus episode window. (h) The episode trigger claim was not performed in an office, IP, OP, or ASC setting based on its place of service. STEMI with PCI episodes are also removed using exclusions specific to the STEMI with PCI measure that were developed with input from the Cardiovascular Disease Management Clinical Subcommittee. The “Exclusions” and “Exclusions_Details” tabs in the STEMI with PCI Measure Codes List file include the list of these exclusions as well as the codes used to define them.
  
  
• HHS NQS Priority: Making Care Affordable
  
  
• HHS Data Source: Claims
  
  
• Measure Type: Cost/Resource Use
  
  
• Steward: Centers for Medicare & Medicaid Services
  
  
• Endorsement Status: Never Submitted
  
  
• Is the measure specified as an electronic clinical quality measure? No
  
  

Summary of Workgroup Deliberations

• Workgroup Recommendation: Conditional Support for Rulemaking
  
  
• Workgroup Rationale: MAP recognized the importance of this ST-Elevation Myocardial Infarction (STEMI) with Percutaneous Coronary Intervention (PCI) cost measure. MAP Conditionally Supported this measure pending NQF endorsement. During the NQF endorsement review, the MAP encouraged the Cost and Resource Use Standing Committee to specifically consider the appropriateness of the risk adjustment model to ensure clinical and social risk factors are reviewed and included when appropriate. MAP cautioned about the potential stinting of care and noted that appropriate risk adjustment could help safe guard against this practice. Additionally, MAP expressed concern over the precision of the cohort definition and whether there was a sufficiently large cost performance distribution in this measure.
  
  
• Public comments received: 3

Rationale for measure provided by HHS
The ST-Elevation Myocardial Infarction (STEMI) with Percutaneous Coronary Intervention (PCI) Cost Measure represents one of the most common types of hospitalization among Medicare beneficiaries and is associated with high mortality. It was estimated that acute myocardial infarction (AMI) accounted for $11.5 billion in total hospital costs in 2011. There are approximately 580,000 new incidences of AMI each year in the US and 210,000 recurrent incidences (AHA, 2017). The average age at the first AMI is 65.3 years for males and 71.8 years for females, so it is a condition that affects the Medicare-aged population. The high prevalence and considerable morbidity and mortality affect beneficiaries and their family members and caregivers. It also exacts a significant economic burden on the healthcare system that has been increasing over time. A 2013 study found that Medicare spending per patient with an AMI has increased: Medicare spending increased by 16.5 percent when comparing a sample of beneficiaries with AMI from 1998 to 1999 to a sample of beneficiaries with AMI in 2008. Most of the observed expenditure growth resulted from the increased use of home health agencies, hospices, durable medical equipment, skilled nursing facilities, and inpatient services that occurred after the 30 day mark following an AMI and out of the control of Medicare’s bundle payment system (Likosky et al., 2013). References: Benjamin, Emelia J., Michael J. Blaha, Stephanie E. Chiuve, Mary Cushman, Sandeep R. Das, Rajat Deo, Sarah D. de Ferranti et al. "Heart disease and stroke statistics--2017 update: a report from the American Heart Association." Circulation 135, no. 10 (2017): e146-e603. Likosky, Donald S., Weiping Zhou, David J. Malenka, William B. Borden, Brahmajee K. Nallamothu, and Jonathan S. Skinner. "Growth in Medicare expenditures for patients with acute myocardial infarction: a comparison of 1998 through 1999 and 2008." JAMA internal medicine 173, no. 22 (2013): 2055-2061.

Measure Specifications

• NQF Number (if applicable):
  
  
• Description: The Revascularization for Lower Extremity Chronic Critical Limb Ischemia cost measure applies to clinicians who perform elective revascularization for lower extremity chronic critical limb ischemia for Medicare beneficiaries. The cost measure is calculated by determining the risk-adjusted episode cost, averaged across all of a clinician’s episodes during the measurement period. The cost of each episode is the sum of the cost to Medicare for services performed by the attributed clinician and other healthcare providers during the episode window (from 30 days prior to the trigger date to 90 days after the trigger date).
  
  
• Numerator: The numerator of the Revascularization for Lower Extremity Chronic Critical Limb Ischemia cost measure is the sum of the ratio of observed to expected payment-standardized cost to Medicare for all episodes attributed to a clinician. This is then multiplied by the national average observed episode cost to generate a dollar figure.
  
  
• Denominator: The cost measure denominator is the total number of episodes from the Revascularization for Lower Extremity Chronic Critical Limb Ischemia episode group attributed to a clinician.
  
  
• Exclusions: The following episode-level exclusions apply: (a) The beneficiary has a primary payer other than Medicare for any amount of time overlapping the episode window or in the 120 days prior to the episode trigger day. (b) No attributed clinician is found for the episode. (c) The episode is not attributed to at least one main clinician. (d) The beneficiary’s date of birth is missing. (e) The beneficiary’s death date occurred before the trigger date. (f) The beneficiary’s death date occurred before the episode ended. (g) The beneficiary was not enrolled in Medicare Part A and B for the entirety of the 120-day lookback period plus episode window, or is enrolled in Part C for any part of the lookback period plus episode window. (h) The episode trigger claim was not performed in an office, IP, OP, or ASC setting based on its place of service
  
  
• HHS NQS Priority: Making Care Affordable
  
  
• HHS Data Source: Claims
  
  
• Measure Type: Cost/Resource Use
  
  
• Steward: Centers for Medicare & Medicaid Services
  
  
• Endorsement Status: Never Submitted
  
  
• Is the measure specified as an electronic clinical quality measure? No
  
  

Summary of Workgroup Deliberations

• Workgroup Recommendation: Conditional Support for Rulemaking
  
  
• Workgroup Rationale: MAP recognized the importance of this Revascularization for Lower Extremity Chronic Limb Ischemia cost measure. MAP Conditionally Supported this measure pending NQF endorsement. During the NQF endorsement review, the MAP encouraged the Cost and Resource Use Standing Committee to specifically consider the appropriateness of the risk adjustment model to ensure clinical and social risk factors are reviewed and included when appropriate. MAP cautioned about the potential stinting of care and noted that appropriate risk adjustment could help safe guard against this practice.. Additionally, MAP encouraged the Standing Committee to review the attribution methodology and the risk scoring methodology used in this measure.
  
  
• Public comments received: 2

Rationale for measure provided by HHS
Roughly 8.5 million people in the United States are affected by Peripheral Vascular Disease (PVD), and according to the CDC this includes between 12 and 20 percent of individuals over age 60 (CDC, 2017). Additionally, five percent of Americans over the age of 50 have PVD (NIH, 2017). A host of factors increase the risk of PVD. For example, the condition affects one in three diabetics and one in three people with heart disease, and the risk of PVD increases with high blood pressure and high cholesterol (NIH, 2017). PVD is treated by a variety of methods including lifestyle change, such as exercise, cessation of smoking, and weight reduction, or for cases unresponsive to these changes alone, medication to lower blood pressure and cholesterol or dissolve clots, or surgical procedures such as revascularization (NIH, 2017).The total costs of PVD in the United States are over $21 billion annually, and PVD is associated with reduced quality of life and increased risk of amputation and death (Ogilvie et al., 2017). A subset of PVD patients has critical limb ischemia (CLI) (in which blood flow to the extremities is greatly reduced, causing pain, ulcers, or sores), and this is considered the end stage of PVD, in which revascularization is necessary to prevent the dysfunction and loss of a limb (Farber and Eberhardt, 2016). The costs of CLI in the United States are over $4 billion, and CLI patients have an annual cardiovascular event rate of 5 percent to 7 percent, as well as a 2-year mortality rate of 40 percent (Ibid). References: CDC. “Peripheral Arterial Disease (PAD) Fact Sheet.” https://www.cdc.gov/dhdsp/data_statistics/fact_sheets/fs_pad.htm [Accessed July 29, 2017]. NIH. “Facts About Peripheral Arterial Disease (P.A.D.).” NIH Publication No. 06-5837. (Aug 2006). https://www.nhlbi.nih.gov/health/educational/pad/docs/pad_extfctsht_general_508.pdf [Accessed July 29, 2017]. Ogilvie, R.P., P.L. Lutsey, G. Heiss, A.R. Folsom, and L.M. Steffen. “Dietary intake and peripheral arterial disease incidence in middle-aged adults: the Atherosclerosis Risk in Communities (ARIC) Study.” [In eng]. The American Journal of Clinical Nutrition. 105, no. 3 (Mar 2017): 651-659. Farber, A., R.T. Eberhardt. “The Current State of Critical Limb Ischemia: A Systematic Review.” [In eng]. JAMA Surgery. 151, no. 11 (Nov 2016): 1070-1077.

Measure Specifications

• NQF Number (if applicable):
  
  
• Description: The percentage of patients 60 years of age and older who have a Varicella Zoster (shingles) vaccination
  
  
• Numerator: Patients with a shingles vaccine ever recorded
  
  
• Denominator: Patients 60 years of age and older
  
  
• Exclusions: n/a
  
  
• HHS NQS Priority: Effective Prevention and Treatment (*NQF received an update following the publication of the MUC List that this should be categorized as "Working with communities to promote wide use of best practices to enable healthy living")
  
  
• HHS Data Source: Registry
  
  
• Measure Type: Process
  
  
• Steward: PPRNet 
  
  
• Endorsement Status: Submitted
  
  
• Is the measure specified as an electronic clinical quality measure? No
  
  

Summary of Workgroup Deliberations

• Workgroup Recommendation: Conditional Support for Rulemaking
  
  
• Workgroup Rationale: This measure would address the important topic of adult immunization. MAP discussed the new guidelines under development for the Zoster vaccination that could impact the amount of doses, the age of administration, and the specific vaccine that is used but also noted that guidelines are constantly evolving and measures should be routinely updated based on changing guidelines. MAP further emphasized the need for a composite adult vaccination measure, but acknowledged the data challenges in developing such a composite in the short run. MAP acknowledged a number of comments that were made about the cost and coverage of the Zoster vaccination and recommended that coverage is considered when implementing this measure. MAP recommends that that this measure be Conditionally Supported with the condition of submission for NQF endorsement and that it is updated to reflect the most current clinical guidelines.
  
  
• Public comments received: 1

Rationale for measure provided by HHS
The CDC ACIP first recommended the zoster vaccine in 2008. Harpaz R, Ortega-Sanchez IR, Seward JF. Prevention of herpes zoster: recommendations of the Advisory Committee on Immunization Practices (ACIP). MMWR 2008;57(No. RR-5) states that "Zoster is a localized, generally painful cutaneous eruption that occurs most frequently among older adults and immunocompromised persons. . Approximately one in three persons will develop zoster during their lifetime, resulting in an estimated 1 million episodes in the United States annually. A common complication of zoster is postherpetic neuralgia (PHN), a chronic, often debilitating pain condition that can last months or even years. The risk for PHN in patients with zoster is 10%-18%. Another complication of zoster is eye involvement, which occurs in 10%-25% of zoster episodes and can result in prolonged or permanent pain, facial scarring, and loss of vision. Approximately 3% of patients with zoster are hospitalized; many of these episodes involved persons with one or more immunocompromising condition." The 2014 update on the recommendation published in MMWR, August 22, 2014, Vol 63, 33:729-731 cited two studies that have evaluated the short-term efficacy of the zoster vaccine in adults aged ≥60 years. The shingles prevention study, a randomized controlled trial, followed 38,546 subjects for up to 4.9 years after vaccination and found a vaccine efficacy of 51.3% (CI = 44.2%-57.6%) for prevention of herpes zoster and 66.5% (CI = 47.5%-79.2%) for prevention of PHN. The short-term persistence substudy followed a subset of 14,270 subjects primarily 4 to 7 years after vaccination and found a vaccine efficacy of 39.6% (CI = 18.2%-55.5%) for prevention of herpes zoster and 60.1% (CI = -9.8%-86.7%) for prevention of PHN. The NQF deems zoster vaccine as a priority in its report, Priority Setting for Healthcare Performance Measurement: Addressing Performance Measure Gaps for Adult Immunizations FINAL REPORT AUGUST 15, 2014. http://www.qualityforum.org/Publications/2014/08/Adult_Immunizations_Final_Report.aspx

Measure Specifications

• NQF Number (if applicable):
  
  
• Description: Composite outcome assessment documenting an improvement in the clinical evaluation of patients using the venous clinical severity score (VCSS) and on a disease-specific PRO survey instrument following ilio-femoral venous stenting
  
  
• Numerator: Number of patients who demonstrate improvement in a disease specific patient reported quality of life score AND who document improvement in the Venous Clinical Severity Score 3-6 months after ilio-femoral venous stenting.
  
  
• Denominator: The total number of patients undergoing ilio-femoral venous stenting
  
  
• Exclusions: Patients who did not complete a disease specific patient reported quality of life score at baseline or 3-6 months post-procedure OR Did not return to clinic 3-6 months post-procedure for assessment of the Venous Clinical Severity Score
  
  
• HHS NQS Priority: Patient and Family Engagement
  
  
• HHS Data Source: Hybrid, Registry (enter which Registry in the field below)
  
  
• Measure Type: Composite Outcome
  
  
• Steward: Society of Interventional Radiology;
  
  
• Endorsement Status: Never Submitted
  
  
• Is the measure specified as an electronic clinical quality measure? No
  
  

Summary of Workgroup Deliberations

• Workgroup Recommendation: Refine and Resubmit Prior to Rulemaking
  
  
• Workgroup Rationale: MAP noted the importance of this composite measure to evaluate to evaluate patient reported and clinical outcomes following ilio-femoral venous stenting. MAP recommended Refine and Resubmit for this measure since it is early in development and has not been fully tested at the clinician level. MAP encouraged the measure developer to demonstrate that the measure adequately accounts for patients who are lost to follow-up.
  
  
• Public comments received: 0

Rationale for measure provided by HHS
The financial burden of chronic venous disease on the health-care system is enormous, with recent estimates placing the cost of CVD treatment at $3 billion per year in the United States, or up to 2% of the total health-care budget of all Western countries. The post-thrombotic syndrome (PTS) is a frequent and important complication of deep venous thrombosis (DVT) with as many as two-thirds of patients developing symptoms of pain, edema, hyperpigmentation, or ulceration. Ilio-femoral vein stenting has become a safe and effective alternative to traditional open surgery to correct iliac vein obstruction as a cause of post thrombotic syndrome. A RAND evidence review in 2013 reported relief of pain (86-94%), relief from swelling (66%-89%) and healing of venous ulcers (55-89%) in published studies, thereby improving quality of life. The RAND summary concluded the benefits outweigh the risks (1B). The Venous Clinical Severity Score (VCSS) replaced the older CEAP (clinical grade, etiology, anatomy, pathophysiology) grading system to assess the severity of chronic venous disease. Unlike the CEAP system, the venous clinical severity score is more useful in the assessment of changes in venous disease and thus is most appropriate to apply to patients undergoing treatment to assess outcomes from therapy, such as ilio-femoral venous stenting. By encouraging the routine use of the venous clinical severity score, centers will be able to objectively assess the intermediate outcome of venous stenting on the symptoms and signs of chronic venous disease. The VCSS score focuses more on the clinical signs, rather than patient symptoms, which was demonstrated to be a more useful marker for subtle changes in the severity of venous disease. o Analysis of patients from the American Venous Forum (AVF), National Venous Screening Program (NVSP) data registry from 2007 to 2009 concluded that VCSS has more global application in determining overall severity of venous disease than other venous assessment tools. (J Vasc Surg 2011;54:2S-9S.) o The Chronic Venous Insufficiency Questionnaire, the Venous Insufficiency Epidemiological and Economic Study, the Aberdeen Varicose Vein Questionnaire, and the Charing Cross Venous ulceration questionnaire, among others, are validated disease-specific instruments to assess patient symptoms before and after iliofemoral venous stenting in patient with deep venous system abnormalities. These surveys are complimentary to commonly used clinical scoring systems including the venous clinical severity score or the villalta score. Indeed one study suggests that combination of the Villalta score with a venous disease-specific quality-of-life questionnaire, to be considered the “reference standard” for the diagnosis and classification of post-thrombotic syndrome (Soosainathan A, Moore HM, Gohel MS, Davies AH. Scoring systems for the post-thrombotic syndrome. J Vasc Surg. 2013 Jan;57(1):254-61.) o In addition, this measure is supported by the following quality improvement guideline and position statement: 1. Vendantham et al. Society of Interventional Radiology Position Statement: Treatment of Acute Iliofemoral Deep Vein Thrombosis with Use of Adjunctive Catheter-Directed Intrathrombus Thyombolysis. JVIR 2006; 17: 417-434. 2. Vendantham et al. Quality improvement guidelines for the treatment of lower-extremity deep venous thrombosis with use of endovascular thrombus removal. JVIR 2014; 25: 1317-1325.

Measure Specifications

• NQF Number (if applicable):
  
  
• Description: The Elective Outpatient PCI cost measure applies to clinicians who perform elective outpatient PCIs for Medicare beneficiaries. The cost measure is calculated by determining the risk-adjusted episode cost, averaged across all of a clinician’s episodes during the measurement period. The cost of each episode is the sum of the cost to Medicare for services performed by the attributed clinician and other healthcare providers during the episode window (from the trigger date to 30 days after the trigger date).
  
  
• Numerator: The numerator of the Elective Outpatient PCI cost measure is the sum of the ratio of observed to expected payment-standardized cost to Medicare for all episodes attributed to a clinician. This is then multiplied by the national average observed episode cost to generate a dollar figure.
  
  
• Denominator: The cost measure denominator is the total number of episodes from the Elective Outpatient PCI episode group attributed to a clinician.
  
  
• Exclusions: The following episode-level exclusions apply: (a) The beneficiary has a primary payer other than Medicare for any amount of time overlapping the episode window or in the 120 days prior to the episode trigger day. (b) No attributed clinician is found for the episode. (c) The episode is not attributed to at least one main clinician. (d) The beneficiary’s date of birth is missing. (e) The beneficiary’s death date occurred before the trigger date. (f) The beneficiary’s death date occurred before the episode ended. (g) The beneficiary was not enrolled in Medicare Part A and B for the entirety of the 120-day lookback period plus episode window, or is enrolled in Part C for any part of the lookback period plus episode window. (h) The episode trigger claim was not performed in an office, IP, OP, or ASC setting based on its place of service. Elective Outpatient PCI episodes are also removed using exclusions specific to the Elective Outpatient PCI measure that were developed with input from the Cardiovascular Disease Management Clinical Subcommittee. The “Exclusions” and “Exclusions_Details” tabs in the Elective Outpatient PCI Measure Codes List file include the list of these exclusions as well as the codes used to define them.
  
  
• HHS NQS Priority: Making Care Affordable
  
  
• HHS Data Source: Claims
  
  
• Measure Type: Cost/Resource Use
  
  
• Steward: Centers for Medicare & Medicaid Services
  
  
• Endorsement Status: Never Submitted
  
  
• Is the measure specified as an electronic clinical quality measure? No
  
  

Summary of Workgroup Deliberations

• Workgroup Recommendation: Conditional Support for Rulemaking
  
  
• Workgroup Rationale: MAP recognized the importance of this Elective Outpatient Percutaneous Coronary Intervention (PCI) cost measure. MAP Conditionally Supported this measure pending NQF endorsement. During the NQF endorsement review, the MAP encouraged the Cost and Resource Use Standing Committee to specifically consider the appropriateness of the risk adjustment model to ensure clinical and social risk factors are reviewed and included when appropriate. MAP cautioned about the potential stinting of care and noted that appropriate risk adjustment could help safe guard against this practice.
  
  
• Public comments received: 3

Rationale for measure provided by HHS
Percutaneous coronary intervention (PCI) is one of the most common major medical procedures performed in the United States. PCI procedures are performed in 600,000 patients each year and have the highest aggregate costs of all cardiovascular procedures, totaling about $10 billion annually (Amin et al., 2017). Between 2005 and 2010, PCI prices increased by 19.1 percent nationally, significantly more than the rate of inflation during the same period (Dor et al., 2015). Approximately 25 percent of patients treated with PCI are 75 years or older and 12 percent are 80 years or older. This growing trend of the use of PCI in the elderly does not appear to be slowing (Vandermolen et al., 2015). With increased age, there are also greater risks for procedural complications, including bleeding (Wang et al., 2011). Other notable complications include vascular compromise (Anderson et al., 2002), stroke, recurrent infarction (Lee 2015), and death (Aggawal et al., 2013). To focus on one type of complication affecting the Medicare population, the risk of bleeding remains highest in older adults (Dodson & Maurer, 2011). This is associated with increased morbidity, mortality, lengthened hospitalization, transfusions, and other significant costs following PCI. (Dauerman et al., 2011). References: Amin, Amit P., Mark Patterson, John A. House, Helmut Giersiefen, John A. Spertus, Dmitri V. Baklanov, Adnan K. Chhatriwalla et al. "Costs associated with access site and same-day discharge among Medicare beneficiaries undergoing percutaneous coronary intervention: an evaluation of the current percutaneous coronary intervention care pathways in the United States." JACC: Cardiovascular Interventions 10, no. 4 (2017): 342-351. Dor, Avi, William E. Encinosa, and Kathleen Carey. "Medicare’s hospital compare quality reports appear to have slowed price increases for two major procedures." Health affairs 34, no. 1 (2015): 71-77. Vandermolen, Sebastian, Jane Abbott, and Kalpa De Silva. "What’s age got to do with it? A review of contemporary revascularization in the elderly." Current cardiology reviews 11, no. 3 (2015): 199-208. Wang, Tracy Y., Antonio Gutierrez, and Eric D. Peterson. "Percutaneous coronary intervention in the elderly." Nature Reviews Cardiology 8, no. 2 (2011): 79-90. Anderson, H. Vernon, Richard E. Shaw, Ralph G. Brindis, Kathleen Hewitt, Ronald J. Krone, Peter C. Block, Charles R. McKay, and William S. Weintraub. "A contemporary overview of percutaneous coronary interventions: the American College of Cardiology--National Cardiovascular Data Registry (ACC--NCDR)." Journal of the American College of Cardiology39, no. 7 (2002): 1096-1103. Lee, Joo Myung, Doyeon Hwang, Jonghanne Park, Kyung-Jin Kim, Chul Ahn, and Bon-Kwon Koo. "Percutaneous coronary intervention at centers with and without on-site surgical backup: an updated meta-analysis of 23 studies." Circulation (2015): CIRCULATIONAHA-115. Aggarwal, Bhuvnesh, Stephen G. Ellis, A. Michael Lincoff, Samir R. Kapadia, Joseph Cacchione, Russell E. Raymond, Leslie Cho et al. "Cause of death within 30 days of percutaneous coronary intervention in an era of mandatory outcome reporting." Journal of the American College of Cardiology 62, no. 5 (2013): 409-415. Dodson, John A., and Mathew S. Maurer. "Changing nature of cardiac interventions in older adults." Aging health 7, no. 2 (2011): 283-295. Dauerman, Harold L., Sunil V. Rao, Frederic S. Resnic, and Robert J. Applegate. "Bleeding avoidance strategies." Journal of the American College of Cardiology 58, no. 1 (2011): 1-10.

Measure Specifications

• NQF Number (if applicable):
  
  
• Description: This cost measure applies to clinicians who manage the inpatient care of Medicare beneficiaries hospitalized for an intracranial hemorrhage or cerebral infarction. The cost measure is calculated by determining the risk-adjusted episode cost, averaged across all of a clinician’s episodes during the measurement period. The cost of each episode is the sum of the cost to Medicare for services performed by the attributed clinician and other healthcare providers during the episode window (from the trigger date to 90 days after the trigger date).
  
  
• Numerator: The numerator of the Intracranial Hemorrhage or Cerebral Infarction cost measure is the sum of the ratio of observed to expected payment-standardized cost to Medicare for all episodes attributed to a clinician. This is then multiplied by the national average observed episode cost to generate a dollar figure.
  
  
• Denominator: The cost measure denominator is the total number of episodes from the Intracranial Hemorrhage or Cerebral Infarction episode group attributed to a clinician.
  
  
• Exclusions: The following episode-level exclusions apply: (a) The beneficiary has a primary payer other than Medicare for any amount of time overlapping the episode window or in the 120 days prior to the episode trigger day. (b) No attributed clinician is found for the episode. (c) The episode is not attributed to at least one main clinician. (d) The beneficiary’s date of birth is missing. (e) The beneficiary’s death date occurred before the trigger date. (f) The beneficiary’s death date occurred before the episode ended. (g) The beneficiary was not enrolled in Medicare Part A and B for the entirety of the 120-day lookback period plus episode window, or is enrolled in Part C for any part of the lookback period plus episode window. (h) The episode trigger claim was not performed in an office, IP, OP, or ASC setting based on its place of service.
  
  
• HHS NQS Priority: Making Care Affordable
  
  
• HHS Data Source: Claims
  
  
• Measure Type: Cost/Resource Use
  
  
• Steward: Centers for Medicare & Medicaid Services
  
  
• Endorsement Status: Never Submitted
  
  
• Is the measure specified as an electronic clinical quality measure? No
  
  

Summary of Workgroup Deliberations

• Workgroup Recommendation: Conditional Support for Rulemaking
  
  
• Workgroup Rationale: MAP recognized the importance of this Intracranial Hemorrhage or Cerebral Infarction cost measure but expressed concern with the clinical cohort definition of the measure as it captures the cost of two related conditions with different treatment plans. MAP Conditionally Supported this measure pending NQF endorsement. During the NQF endorsement review, the MAP encouraged the Cost and Resource Use Standing Committee to specifically consider the appropriateness of the clinical cohorts defined in this measure, and the appropriateness of the risk adjustment model for both clinical and social risk factors. MAP also discussed the need to ensure that this measure appropriately handles transfers for tertiary medical centers that may receive transfer patients with more severe presentation that may not be reflected in administrative claims data.
  
  
• Public comments received: 4

Rationale for measure provided by HHS
Intracranial hemorrhage and ischemic stroke are common conditions that can have serious consequences for patients and their families, such as death or permanent disability. Approximately 780,000 Americans suffer a new or recurring stroke every year (Guilhaume et al., 2010). Strokes are the leading cause of permanent disability in adults and the third leading cause of death in the US, with a 30 day mortality rate of around 8 percent for patients who have suffered an ischemic stroke and 20 percent in the case of a hemorrhagic stroke (Birenbaum 2010, Collins et al., 2003). Elderly patients are particularly at risk after suffering from either an ischemic or hemorrhagic stroke with studies showing increased mortality risk in patients age 65 years or older with an ischemic stroke and in patients age 75 years or older with a hemorrhagic stroke. The 30-day mortality rate for hemorrhagic stroke is twice that of the rate for ischemic stroke (Collins et al., 2003). Finally, a 2010 study estimated that ischemic strokes alone, which represent a majority of overall strokes, were responsible for close to $65.5 billion of healthcare spending in the US given the need for long-term care after the events (Guilhaume et al., 2010). References: Guilhaume, Chantal, Delphine Saragoussi, John Cochran, Clément François, and Mondher Toumi. "Modeling Stroke Management: A Qualitative Review of Cost-Effectiveness Analyses." The European Journal of Health Economics : HEPAC 11, no. 4 (August 2010): 419-26. Birenbaum, Dale. "Emergency Neurological Care of Strokes and Bleeds." Journal of Emergencies, Trauma and Shock 3, no. 1 (January 2010): 52-61. Collins, Tracie C., Nancy J. Petersen, Terri J. Menke, Julianne Souchek, Wednesday Foster, and Carol M. Ashton. "Short-Term, Intermediate-Term, and Long-Term Mortality in Patients Hospitalized for Stroke." Journal of Clinical Epidemiology 56, no. 1 (January 2003): 81-7.

Measure Specifications

• NQF Number (if applicable):
  
  
• Description: The Simple Pneumonia with Hospitalization cost measure applies to clinicians who manage the inpatient care of Medicare beneficiaries hospitalized with simple pneumonia. The cost measure is calculated by determining the risk-adjusted episode cost, averaged across all of a clinician’s episodes during the measurement period. The cost of each episode is the sum of the cost to Medicare for services performed by the attributed clinician and other healthcare providers during the episode window (from the trigger date to 30 days after the trigger date).
  
  
• Numerator: The numerator of the Simple Pneumonia with Hospitalization cost measure is the sum of the ratio of observed to expected payment-standardized cost to Medicare for all episodes attributed to a clinician. This is then multiplied by the national average observed episode cost to generate a dollar figure.
  
  
• Denominator: The cost measure denominator is the total number of episodes from the Simple Pneumonia with Hospitalization episode group attributed to a clinician.
  
  
• Exclusions: The following episode-level exclusions apply: (a) The beneficiary has a primary payer other than Medicare for any amount of time overlapping the episode window or in the 120 days prior to the episode trigger day. (b) No attributed clinician is found for the episode. (c) The episode is not attributed to at least one main clinician. (d) The beneficiary’s date of birth is missing. (e) The beneficiary’s death date occurred before the trigger date. (f) The beneficiary’s death date occurred before the episode ended. (g) The beneficiary was not enrolled in Medicare Part A and B for the entirety of the 120-day lookback period plus episode window, or is enrolled in Part C for any part of the lookback period plus episode window. (h) The episode trigger claim was not performed in an office, IP, OP, or ASC setting based on its place of service. Simple Pneumonia with Hospitalization episodes are also removed using exclusions specific to the Simple Pneumonia with Hospitalization measure that were developed with input from the Pulmonary Disease Management Clinical Subcommittee. The “Exclusions” and “Exclusions_Details” tabs in the Simple Pneumonia with Hospitalization Measure Codes List file include the list of these exclusions as well as the codes used to define them.
  
  
• HHS NQS Priority: Making Care Affordable
  
  
• HHS Data Source: Claims
  
  
• Measure Type: Cost/Resource Use
  
  
• Steward: Centers for Medicare & Medicaid Services
  
  
• Endorsement Status: Never Submitted
  
  
• Is the measure specified as an electronic clinical quality measure? No
  
  

Summary of Workgroup Deliberations

• Workgroup Recommendation: Conditional Support for Rulemaking
  
  
• Workgroup Rationale: MAP recognized the importance of this Simple Pneumonia with Hospitalization cost measure. MAP Conditionally Supported this measure pending NQF endorsement. During the NQF endorsement review, the MAP encouraged the Cost and Resource Use Standing Committee to specifically consider the appropriateness of the risk adjustment model to ensure clinical and social risk factors are reviewed and included when appropriate. MAP cautioned about the potential stinting of care and noted that appropriate risk adjustment could help safe guard against this practice. Additionally, MAP expressed concern over the precision of the cohort definition and whether there was a sufficiently large cost performance distribution in this measure.
  
  
• Public comments received: 1

Rationale for measure provided by HHS
Among adults in the United States, pneumonia is a leading infectious cause of hospitalization and death (Healthcare Cost and Utilization Project, 2013). Although pneumonia encompasses a broad range of diagnoses depending on -- among other things -- where the infection was acquired and certain comorbidities of the patient, simple pneumonia is mostly focused on community-acquired pneumonia (CAP), which is a major driver of Medicare morbidity and mortality. A patient’s pneumonia is considered CAP when the patient has not been hospitalized or been a resident of a long-term care facility for more than 72 hours in the past 90 days before the onset of symptoms (Fung and Monteagudo-Chu, 2010). The annual incidence of CAP requiring hospitalization was 24.8 cases per 10,000 adults, with estimated incidence increasing with age. The estimated incidences of hospitalization among adults in the United States 50 to 64 years of age, 65 to 79 years of age, and 80 years of age or older were approximately 4, 9, and 25 times as high, respectively, compared to the incidence among adults 18 to 49 years of age (Jain et al., 2015). In addition, a 2012 study found that among the Medicare fee-for-service population, there was an estimated 1.3 million CAP cases and 74,000 CAP-related deaths, accounting for an annual cost of $13 billion (Yu et al., 2012). References: Healthcare Cost and Utilization Project. “Statistical Brief #168: Costs for Hospital Stays in the United States, 2011.” (December 2013). Fung, H. B., and M. O. Monteagudo-Chu. "Community-Acquired Pneumonia in the Elderly." [In eng]. Am J Geriatr Pharmacother 8, no. 1 (Feb 2010): 47-62. Jain, S., W. H. Self, R. G. Wunderink, S. Fakhran, R. Balk, A. M. Bramley, C. Reed, et al. "Community-Acquired Pneumonia Requiring Hospitalization among U.S. Adults." [In eng]. N Engl J Med 373, no. 5 (Jul 30 2015): 415-27. Yu, H., J. Rubin, S. Dunning, S. Li, and R. Sato. "Clinical and Economic Burden of Community-Acquired Pneumonia in the Medicare Fee-for-Service Population." [In eng]. J Am Geriatr Soc 60, no. 11 (Nov 2012): 2137-43.

Measure Specifications

• NQF Number (if applicable): 3067
  
  
• Description: Percentage of patients 15-65 years of age who have ever been tested for human immunodeficiency virus (HIV)
  
  
• Numerator: Patients with documentation of the occurrence of an HIV test between their 15th and 66th birthdays and before the end of the measurement period
  
  
• Denominator: Patients 15 to 65 years of age who had an outpatient visit during the measurement period
  
  
• Exclusions: Exclude from the denominator: patients diagnosed with HIV prior to the start of the measurement period
  
  
• HHS NQS Priority: Effective Prevention and Treatment
  
  
• HHS Data Source: EHR
  
  
• Measure Type: Process/Population Health
  
  
• Steward: Centers for Disease Control and Prevention
  
  
• Endorsement Status: Failed Endorsement
  
  
• Changes to Endorsed Measure Specifications?: The MUC list indicates the measure has been modified from its endorsed version.
  
  
• Is the measure specified as an electronic clinical quality measure? Yes
  
  

Summary of Workgroup Deliberations

• Workgroup Recommendation: Conditional Support for Rulemaking
  
  
• Workgroup Rationale: MAP acknowledged the importance of HIV screening from a population health perspective but also questioned whether encouraging HIV screening through the MIPS program is the most effective strategy. MAP also expressed concern on how the measure under consideration identified individuals who may have a HIV screening in the community. MAP briefly discussed stigma of HIV screening and a MAP member expressed that stigma should not be a concern for this measure. MAP Conditionally Supported this measure with the condition of NQF endorsement. MAP requested that the relevant Standing Committee review the patient cohort definition and how community screening is handled in the endorsement review of this measure.
  
  
• Public comments received: 1

Rationale for measure provided by HHS
HIV is a communicable infection that leads to a progressive disease with a long asymptomatic period. In 2014, approximately 37,600 persons in the United States were newly infected with HIV (CDC 2017). Without treatment, most people develop acquired immunodeficiency syndrome (AIDS) within 10 years of HIV infection. Antiretroviral therapy (ART) delays this progression and increases the length of survival, but it is most effective when initiated during the asymptomatic phase. It is estimated that, on average, an HIV-infected person who is age 25 and receives high quality health care will live an additional 38 years (Farnham 2013). According to guidelines from the U.S. Department of Health and Human Services (HHS), antiretroviral therapy should be used for all HIV-infected people to reduce the risk of disease progression (regardless of CD4 cell count at diagnosis) (Panel on Antiretroviral Guidelines for Adults and Adolescents 2016). In the United States, an estimated 1.2 million people are living with human immunodeficiency virus (HIV), a serious, communicable infection that, if untreated, leads to illness and premature death (CDC 2016). At the end of 2013, 13 percent, or about 161,200, of those infected with HIV were undiagnosed, and almost 23 percent of the people who were diagnosed had a Stage 3 (AIDS) classification at the time of diagnosis (CDC 2016). One study showed that the median CD4 count at diagnosis is less than 350 cells/mm3, which is the threshold commonly used to determine when patients should initiate ART (Althoff et al. 2010). HIV screening identifies infected people who were previously unaware of their infection, which enables them to seek medical and social services that can improve their health and the quality and length of their lives. The use of ART with high levels of medication adherence has been shown to substantially reduce risk for HIV transmission (Panel on Antiretroviral Guidelines for Adults and Adolescents 2016). Based on the National Health Interview Survey, fewer than half of people 18 and older reported ever having been tested for HIV as of 2016 (Clarke 2017). References Althoff, K.N., S.J. Gange, M.B. Klein, J.T. Brooks, R.S. Hogg, R.J. Bosch, M.A. Horber, M.S. Saag, M.M. Kitahata, A.C. Justice, K.A. Gebo, J.J. Eron, S.B. Rourke, M.J. Gill, B. Rodriguez, T.R. Sterling, L.M. Calzavara, S.G. Deeks, J.N. Martin, A.R. Rachlis, S. Napravnik, L.P. Jacobson, G.D. Kirk, A.C. Collier, C.A. Benson, M.J. Silverberg, M. Kushel, J.J. Goedert, R.G. McKaig, S.E. Van Rompaey, J. Zhang, and R.D. Moore. “Late Presentation for Human Immunodeficiency Virus Care in the United States and Canada.” Clinical Infectious Diseases, vol. 50, 2010, pp. 1512-1520. CDC. “Monitoring Selected National HIV Prevention and Care Objectives by Using HIV Surveillance Data--United States and 6 U.S. Dependent Areas--2014.” HIV Surveillance Supplemental Report, vol. 21, no. 4, 2016. CDC. “HIV Incidence: Estimated Annual Infections in the U.S., 2008-2014 Overall and by Transmission Route.” Washington, DC: U.S. Department of Health and Human Services, 2017. Available at https://www.cdc.gov/nchhstp/newsroom/docs/factsheets/HIV-Incidence-Fact-Sheet_508.pdf. Accessed 6/7/2017. Clarke, T.C., Norris, T., Schiller, J.S. “Early Release of Selected Estimates Based on Data from 2016 National Health Interview Survey.” Washington, DC: National Center for Health Statistics, 2017. Available at https://www.cdc.gov/nchs/data/nhis/earlyrelease/earlyrelease201705.pdf. Farnham, P.G., Gopalappa, C., Sansom, S.L., Hutchinson, A.B., Brooks, J.T., Weidle, P.J., Marconi, V.C., Rimland, D. “Updates of Lifetime Costs of Care and Quality-of-Life Estimates for HIV-Infected Persons in the United States: Late Versus Early Diagnosis and Entry Into Care.” Journal of Acquired Immune Deficiency Syndromes, vol. 64, no. 2, 2013, pp. 183-189. Panel on Antiretroviral Guidelines for Adults and Adolescents. “Guidelines for the Use of Antiretroviral Agents in HIV-1-Infected Adults and Adolescents.” 2016. Available at http://aidsinfo.nih.gov/ContentFiles/AdultandAdolescentGL.pdf. Accessed June 13, 2017.

Summary of NQF Endorsement Review

• Year of Most Recent Endorsement Review: 2016
  
  
• Project for Most Recent Endorsement Review: Health and Well-Being 2015-2017
  
  
• Review for Importance: 1a. Evidence: H-10; M-5; L-0; I-0; 1b. Performance Gap: H-12; M-3; L-0; I-0 Rationale: • This new, HIV infection screening measure is based on a 2013 US Preventive Services Task Force (USPSTF) guideline that recommends clinicians screen for HIV infection in adolescents and adults aged 15 to 65 years. The guideline also recommends that younger adolescents and older adults who are at increased risk should also be screened. Grade A: High Certainty of Net Benefit. (Moyer, 2013). • USPSTF found no direct evidence on the effects of screening versus no screening on clinical outcomes. Since the 2013 USPSTF recommendation, however, the developer reported that two randomized controlled trials have demonstrated that immediate initiation of anti-retroviral therapy meaningfully affects morbidity, mortality, and forward transmission. • The Committee asked whether the measure captures patients who are screened, diagnosed, and referred to timely, appropriate care. The developer cited surveillance data that show approximately 70% of HIV infected patients receive care within three months of diagnosis. However, the developer also noted the difficulty of assessing these linkages, especially referral documentation in electronic health records (EHRs). The developer also mentioned unsuccessful uptake of measures that assess retention in care. • Committee members questioned the upper age limit of 65 years. The developer acknowledged interest within the CDC in reexamining the upper age bound, but doubted widespread uptake in the absence of aligned USPSTF guidelines. • The Committee asked why the lower age limit is 15 years, while the CDC recommends screening to begin at age 13. The developer noted significant resistance from influential stakeholder groups when attempts were made to align the measure with CDC’s lower age limit. • The Committee also discussed the challenges of adequately assessing screening for adolescents, specially related to confidentially and unintended consequences of disclosing screening to their parents through insurance claims. • One Committee member noted that “testing” and “screening” were used interchangeably. The CDC uses screening to refer to a generalized assessment of HIV infection, not dependent on risk. Whereas testing is used to refer to risk-based or diagnostic testing. • Several Committee members questioned how “evidence of HIV infection” in the numerator can be substantiated without testing. The developer noted that this was included to capture patients with HIV who were tested or screened at some point. The developer is willing to remove this data element from the numerator and denominator to minimize confusion. • The developer does not have national gap information for this new measure, however testing at four community health centers (CHC) found a range of 20.6-31.1%. Results for a fifth CHC with a significant high-risk pool were 65.3%. • 2011 data from the Behavioral Risk Factor Surveillance System (BRFSS) found that for adults 18- 64 years, • 66.2% of Blacks/African Americans, 44.8% of Hispanics, 38.1% of Whites and 38.8% of other races/ethnicities reported ever being tested for HIV.
  
  
• Review for Scientific Acceptability: 2. Scientific Acceptability of Measure Properties: The measure failed to meet the Scientific Acceptability criteria (2a. Reliability - precise specifications, testing; 2b. Validity - testing, threats to validity) 105 2a. Reliability: H-0; M-5; L-5; I-5; 2b. Validity: H-X; M-X; L-X; I-X Rationale: • This is a Health Quality Measures Format-compliant (HQMF) eMeasure. • All components in the measure logic of the submitted eMeasure are represented using the HQMF and Quality Data Model (QDM). • The submitted eMeasure specifications use existing value sets when possible and use new value sets that have been vetted through the Value Set Authority Center (VSAC). • The measure submission includes test results from five Chicago-area community health centers (CHC) that belong to a Health Center Controlled Network and using GE Centricity Practice Solutions (3 versions among the five sites) and that demonstrate the measure logic can be interpreted precisely and unambiguously. • The submission contained a feasibility assessment of the data elements. For one organization (five sites), data availability, data accuracy, and workflow scored three for each criterion (best possible score). For the second organization, the developer stated the feasibility assessment was conducted early in the development process, so two elements were not included; no information on individual criterion was provided for this early phase assessment. Follow-up with the developer indicated the measure logic is feasible based on an assessment by EHR vendors. • The developer assessed empirical reliability at the data element level and validity of the measure score. • Data element testing used a random sample of 300 charts; 100 patients who met the measure and 200 who did not were pulled for chart review. Data element testing results were 96% sensitivity, 100% specificity, and kappa=0.97. The developer concluded results represent a highly valid and reliable representation of the numerator elements between the manual vs. automated extractions. • Score-level testing involved examining performance at the five different CHCs, each of which involved multiple care sites and three versions of the GE Centricity platform, and also comparing these score results to other practices with established EHRs (Kaiser Permanente Mid-Atlantic States and the Department of Veterans Affairs). • For score-level testing, the developer concluded the share of visits ever screened in its sample “compares favorably” (20.6-65.3%) to the data from Kaiser (35% screened) and VA (22.9% screened for VA facilities in Chicago area. • The Committee raised concern about reliability testing of the data elements in the EHR; specifically, it questioned how patients who opt out were handled; limited geographic focus on Chicago; and verification of previous screening or test without self-reporting. • The developer confirmed that opt outs are not factored into the measure because screening should be part of standard practice. With regard to geographic variation, the developer confirmed future testing in other cities and in different health systems. Finally, the developer acknowledged potential over-testing with this measure, but concluded that the value of testing outweighed the potential risk of over-testing. • Some concerns were raised about the inclusion of HIV status in the numerator and the cumulative effect on the measure’s ability to discern meaningful differences in HIV infection screening for accountability purposes. • While the Committee was generally supportive of the measure, several concerns were raised about the numerator and denominator. Ultimately, the measure failed the Reliability criterion.
  
  

Measure Specifications

• NQF Number (if applicable): 729
  
  
• Description: The percentage of patients 18-75 years of age who had a diagnosis of type 1 or type 2 diabetes and whose diabetes was optimally managed during the measurement period as defined by achieving ALL of the following: - HbA1c less than 8.0 mg/dL - Blood Pressure less than 140/90 mmHg - On a statin medication, unless allowed contraindications or exceptions are present - Non-tobacco user - Patient with ischemic vascular disease is on daily aspirin or anti-platelets, unless allowed contraindications or exceptions are present
  
  
• Numerator: The number of patients in the denominator whose diabetes was optimally managed during the measurement period as defined by achieving ALL of the following: - The most recent HbA1c in the measurement period has a value less than 8.0 mg/dL - The most recent Blood Pressure in the measurement period has a systolic value of less than 140 mmHg AND a diastolic value of less than 90 mmHg - On a statin medication, unless allowed contraindications or exceptions are present - Patient is not a tobacco user - Patient with ischemic vascular disease (Ischemic Vascular Disease Value Set) is on daily aspirin or anti-platelets, unless allowed contraindications or exceptions are present
  
  
• Denominator: 18 years or older at the start of the measurement period AND less than 76 years at the end of the measurement period AND Patient had a diagnosis of diabetes (Diabetes Value Set) with any contact during the current or prior measurement period OR had diabetes (Diabetes Value Set) present on an active problem list at any time during the measurement period. AND At least one established patient office visit (Established Pt Diabetes & Vasc Value Set) for any reason during the measurement period
  
  
• Exclusions: The following exclusions are allowed to be applied to the eligible population: - Patient was a permanent nursing home resident at any time during the measurement period - Patient was in hospice or receiving palliative care at any time during the measurement period - Patient died prior to the end of the measurement period - Patient was pregnant (Diabetes with Pregnancy Value Set) at any time during measurement period - Documentation that diagnosis was coded in error - Patient had only urgent care visits during the measurement period
  
  
• HHS NQS Priority: Effective Prevention and Treatment
  
  
• HHS Data Source: Administrative clinical data, EHR, Paper medical record
  
  
• Measure Type: Composite
  
  
• Steward: MN Community Measurement
  
  
• Endorsement Status: Endorsed - This measure was endorsed in 2015 as a component of composite measure NQF #0729
  
  
• Changes to Endorsed Measure Specifications?: The MUC list indicates the measure has been modified from its endorsed version.
  
  
• Is the measure specified as an electronic clinical quality measure? No
  
  

Summary of Workgroup Deliberations

• Workgroup Recommendation: Conditional Support for Rulemaking
  
  
• Workgroup Rationale: The measure would address multiple components of high quality diabetes care. MAP recognized the importance of this measure given its clinical prevalence. MAP was supportive of this composite measure but also acknowledged the utility of the individual subcomponents of the measure to drive quality improvement. MAP Conditionally Supported this measure with the condition that there are no competing measures in the program and that the measure is updated to the most current clinical guidelines.
  
  
• Public comments received: 4

Rationale for measure provided by HHS
Addressing Health Care Disparities Using Public Reporting Snowden, A. et al American Journal of Medical Quality August 2012 27 (4): 275-81

Summary of NQF Endorsement Review

• Year of Most Recent Endorsement Review: 2015
  
  
• Project for Most Recent Endorsement Review: Endocrine
  
  
• Review for Importance: 1a. Evidence, 1b. Performance Gap) 1a. Evidence: H-5; M-11; L-0; I-1; 1b. Performance Gap: H-15; 1d. Composite – Quality Construct and Rationale: H-4; M-7; L-4; I-1 Rationale: • For all but one of the components included in this composite (tobacco-free), the developer presented recommendations from the 2014 clinical practice guidelines developed by the Institute for Clinical Systems Improvement (ICSI), which were based on a systematic review of evidence that was graded either high or moderate. Additional evidence-based recommendations from the American College of Cardiology and U.S. Preventive Services Task Force also were presented. Committee members agreed that the evidence supports the relationship between each component and desired health outcomes. • Data provided by the developer indicate that for 2014, only 38.9% of diabetic patients in Minnesota met all five component targets from the composite measure. Committee members agreed that although performance on some of the components is quite high, overall performance indicates opportunity for improvement. • Although some Committee members voiced concern over the “all-or-none” structure of the measure, others agreed that a more comprehensive measure that focuses on management of multiple risk factors is needed. The Committee agreed that the developer description of the quality construct, rationale, and aggregation and weighting approach is explicitly articulated and logical.
  
  
• Review for Scientific Acceptability: 2. Scientific Acceptability of Measure Properties: The measure meets the Scientific Acceptability criteria (2a. Reliability - precise specifications, testing; 2b. Validity - testing, threats to validity) 2a. Reliability: H-9; M-7; L-0; I-0; 2b. Validity: H-1; M-10; L-4; I-1; 2d. Composite: H-1; M-10; L-4; I-1 Rationale: • Committee members noted that the specifications of the statin component of this measure have changed since the most recent endorsement of the measure due to changes in the ACC/AHA clinical practice guidelines on cholesterol management released in November, 2013. In the earlier version of the measure, the statin component assessed reaching a target LDL threshold of < 100 mg/dL; the revised version of this component assesses statin use. • Committee members questioned whether the measure assesses if a patient is on the appropriate statin dose. Developers clarified that the measure does not consider the statin dose but assesses only whether a patient is on a statin. • Members also questioned the age range of 18-75 for the statin component of the measure. The developer clarified that for patients 21-39 years of age, this component is applicable only if the patient has ischemic vascular disease or a very high LDL level, in accordance with the ACC/AHA guidelines. • The developer clarified that the level of analysis for the measure is clinician groups (not individual clinicians), and also noted that multiple clinics may form a clinician group. They also clarified that the measure does not require having a minimum of 30 patients. • Developers presented results of signal-to-noise reliability testing of the performance measure score. They clarified that the beta-binomial method was used for the reliability testing because the composite score itself is a binary (yes/no) measure. Members noted that although the 12 reliability was quite high for most clinician groups, it was lower than 0.7 for some clinician groups. • To demonstrate validity of the performance measure score, developers examined the association between the scores for this measure with the scores from the Optimal Vascular Care measure (NQF #0076), hypothesizing that clinician groups likely provide similar quality of care to different patients who also require management of multiple risk factors. The R2 value from this analysis was 0.64. The developers also described several steps occurring during the data submission process as demonstration of empirical validity testing at the data level element. • Developers also clarified that the measure is risk-adjusted for three factors (insurance type, age group, and diabetes type) and noted that the risk-adjustment strategy was developed using data from all clinicians in Minnesota. However, one member expressed some concern that the only adjustment for sociodemograhic status is insurance type. Developers clarified that other potential risk factors that were considered were not statistically significant and thus were not included in the risk-adjustment model. • Several Committee members voiced concern about holding physicians accountable for the patient’s tobacco use, as some see actual tobacco use (as opposed to efforts for tobacco cessation) as out of the control of the clinician. However, another member referred to data showing that physicians can influence their patients to stop tobacco use. Developers also noted that statewide, they have seen an approximate 2.5% increase in tobacco-free patients in Minnesota. • One Committee member noted the need for clarity about potential adverse effects related to statin use. Another member referenced the flow diagram provided by the developer that details several contraindications for statin use, while another member echoed the importance of the potential for adverse reactions when making treatment decisions. • After developers clarified the performance rates for each of the components, Committee members questioned whether the aspirin component (performance rate =99.5% in MN) is needed in the composite. Developers noted that while this component may be "topped out" in MN, this happened over a four-year period of focus on this component. They also referenced a New England Journal of Medicine article that found a 34.8% performance rate nationally in the primary care setting. Finally, they noted that performance on this component across ACOs is, on average, 75.3%.
  
  
• Review for Feasibility: 3. Feasibility: H-7; M-4; L-4; I-0 (3a. Clinical data generated during care delivery; 3b. Electronic sources; 3c.Susceptibility to inaccuracies/ unintended consequences identified 3d. Data collection strategy can be implemented) Rationale: • The measure data can be collected through electronic clinical data and paper records. • One Committee member noted that, due to the number of components included in the composite, the data collection effort for this composite measure may be intensive. Developers stated that submission of this measure by all clinician groups in MN is mandated by the state. While they acknowledged that MN has many large practices that use EHRs, small practices— even those who still use paper medical records—are able to submit data on this measure. The developers did, however, acknowledge the data collection burden for the new statin component if a patient has not been prescribed a statin (i.e., identifying exceptions due to contraindications).
  
  
• Review for Usability: 4. Usability and Use: H-5; M-7; L-4; I-0 ((Used and useful to the intended audiences for 4a. Accountability and Transparency; 4b. Improvement; and 4c. Benefits outweigh evidence of unintended consequences) Rationale: • Committee members noted that the measure is publicly reported and is used in pay-forperformance and accreditation programs. Performance is slowly increasing across the state of Minnesota, suggesting quality of care may be improving. • Data submitted by the developer demonstrate relatively consistent improvement of performance in MN from the years 2006-2014. • Committee members agreed that this composite measure is patient-centric and acknowledged the importance of using a comprehensive measure that assesses performance of reducing multiple risk factors. • Some Committee members expressed concern that the measure could incent some providers to "cherry-pick" patients or make their practices less hospitable to certain patients or certain subgroups of patients (the tobacco-free component of the measure was a particular concern).
  
  
• Review for Related and Competing Measures: 5. Related and Competing Measures • This measure is a competing measure to the following measures o 0061: Comprehensive Diabetes Care: Blood Pressure Control (< 7% to < 8% depending on individual patient factors. o 0575: Comprehensive Diabetes Care: Hemoglobin A1c (HbA1c) control (<8%) • NQF staff asked the Committee to discuss whether there is justification for continued endorsement of the individual measures if the composite retains endorsement. The Committee discussed the pros and cons of endorsing both individual measures and the composite measure. The Committee ultimately agreed that while the composite measure is useful to assess patientcentric performance across a variety of clinical areas, endorsement of individual measures also can be beneficial, particularly for users who want to focus on certain components of the composite or those who have data collection constraints and cannot use the composite. The Committee therefore recommended continued endorsement of both the individual measures and the composite measure.
  
  
• Endorsement Public Comments: 6. Public and Member Comment Comments received: • Two commenters raised concern over the glucose control component of the composite, referencing the National Action Plan for Adverse Event Prevention, which was released in August, 2014. The National Action Plan states that the blood glucose threshold of < 7% to < 8% depending on individual patient factors. Benefits: Achieving near-normal glycemic control lowers risk of diabetes microvascular complications such as retinopathy, nephropathy and amputations. Achieving A1c of 6.9 to 7.9% may also significantly reduce macrovascular complications based on Steno-2 and UKPDS data. Quality of Evidence: High Strength of Recommendation: Strong. Measurement does not and should not preclude good clinical judgement; however the measure development work group believes that a target of < 8.0 is reasonable and supported by guidelines. Our measure does have an upper age limit cut-off of 75 years and we allow exclusions for death, permanent nursing home resident or patients who are receiving hospice or palliative care services. • Two commenters were critical of the composite measure itself, citing concern that use of the composite measure could mask the individual care processes that most need improvement. Developer response: While it is true that the measure is reported at the composite level, the individual components and the associated rates are available to the medical groups for better understanding their rates and for use in quality improvement to know which areas have opportunity for improvement. MNCM and the measure development work group firmly believe that achieving the intermediate physiological outcome targets related to blood pressure and glycemic control in addition being tobacco free and use of daily aspirin and statins where appropriate are the diabetic patient’s best mechanisms of avoiding or postponing long term complications associated with this chronic condition which affects millions of Americans. Measuring providers separately on individual targets is not as patient centric as a measure that seeks to reduce multiple risk factors for each patient. Diabetic patients are more likely to reduce their overall risk and maximize health outcomes by achieving several intermediate physiological targets. • Two commenters noted that documenting HbA1c levels >8% but less than 9% cannot be done using CPT-II coding, necessitating need for medical chart review. Developer response: A point of clarification, these measure do not rely on CPTII codes for numerator compliance, nor are they indicated anywhere in our measure specification. Measure specifications focus on the electronic health record as a source of clinical information for calculating numerator compliance; actual A1c values are utilized in the case of the A1c target. Additionally, 80 to 90% of all the clinics in MN are reporting this information from their electronic health records without the need for additional chart abstraction. • One commenter suggested a need for including sociodemographic factors in the risk-adjustment approach. Developer response: Our risk adjustment model does include insurance product, which is a proxy for socioeconomic status. During the process of measure development, the expert panel discusses potential variables for risk adjustment that are important to consider for the measured population. For this measure, variables that are available for evaluation include gender, age, zip, race/ethnicity, country of origin, primary language, insurance product, diabetes type, depression and ischemic vascular disease. The potential risk adjustment variables are then evaluated for appropriate inclusion in the model based on a t value outside the range of -2.0 and +2.0. Currently, the variables that have demonstrated acceptable properties are insurance product, age bands (18-25, 26-50, 51-65 and 65 to 75) and diabetes type (1 or 2). Race/ethnicity has been collected for this measure in MN for the past few years, but has now reached a level of 15 reliability in which it can be evaluated for its impact. MNCM continues to review variables and their impact on the measure and part of its measure risk adjustment strategy. • One commenter suggested the need for additional detail regarding moderate or high intensity in the description of statin use for the measure. Developer response: The measure development work group thoroughly discussed the pros and cons of specifying a certain dose of the statin medication and based on the following factors ultimately decided to not specify a dose of moderate or high intensity for numerator compliance: 1) data burden for practices, 2) controversy and burden surrounding the CV risk calculator, 3) ICSI 2014 Diabetes Guideline recommendations for measurement and 4) cardiology work group member’s believe that there is some benefit for some patients who can only tolerate a lower intensity dose. Committee response: • During its review of the individual measure assessing HbA1c
  
  
• Endorsement Committee Recommendation: Standing Committee Recommendation for Endorsement: Y-13; N-4
  
  

Measure Specifications

• NQF Number (if applicable): *Note - 729 (This measure is a component of the endorsed composite measure NQF #729)
  
  
• Description: The percentage of patients 18-75 years of age who had a diagnosis of type 1 or type 2 diabetes and whose most recent HbA1c during the measurement period was less than 8.0 mg/dL.
  
  
• Numerator: Denominator patients whose most recent HbA1c during the measurement period was less than 8.0 mg/dL.
  
  
• Denominator: 18 years or older at the start of the measurement period AND less than 76 years at the end of the measurement period AND Patient had a diagnosis of diabetes (Diabetes Value Set) with any contact during the current or prior measurement period OR had diabetes (Diabetes Value Set) present on an active problem list at any time during the measurement period. AND At least one established patient office visit (Established Pt Diabetes & Vasc Value Set) for any reason during the measurement period
  
  
• Exclusions: The following exclusions are allowed to be applied to the eligible population: - Patient was a permanent nursing home resident at any time during the measurement period - Patient was in hospice or receiving palliative care at any time during the measurement period - Patient died prior to the end of the measurement period - Patient was pregnant (Diabetes with Pregnancy Value Set) at any time during measurement period - Documentation that diagnosis was coded in error - Patient had only urgent care visits during the measurement period
  
  
• HHS NQS Priority: Effective Prevention and Treatment
  
  
• HHS Data Source: Administrative clinical data, EHR, Paper medical record
  
  
• Measure Type: Intermediate Outcome
  
  
• Steward: MN Community Measurement
  
  
• Endorsement Status: Endorsed - This measure was endorsed in 2015 as a component of composite measure NQF #0729
  
  
• Changes to Endorsed Measure Specifications?: The MUC list indicates the measure has not been modified from its endorsed version.
  
  
• Is the measure specified as an electronic clinical quality measure? No
  
  

Summary of Workgroup Deliberations

• Workgroup Recommendation: Conditional Support for Rulemaking
  
  
• Workgroup Rationale: MAP acknowledged the importance of A1c Control (< 8.0) as a critical element of high quality diabetes care. While this measure is included in the Optimal Diabetes Care composite measure, MAP recognized that clinicians may still report A1c control measures separately to drive quality improvement. MAP also discussed the competing measure, ACO #7: Hemoglobin A1c Poor Control. MAP Conditionally Supported this measure with the condition that there are no competing measures in the program.
  
  
• Public comments received: 6

Rationale for measure provided by HHS
Addressing Health Care Disparities Using Public Reporting Snowden, A. et al American Journal of Medical Quality August 2012 27 (4): 275-81

Summary of NQF Endorsement Review

• Year of Most Recent Endorsement Review: 2015
  
  
• Project for Most Recent Endorsement Review: Endocrine
  
  
• Review for Importance: 1a. Evidence, 1b. Performance Gap) 1a. Evidence: H-5; M-11; L-0; I-1; 1b. Performance Gap: H-15; 1d. Composite – Quality Construct and Rationale: H-4; M-7; L-4; I-1 Rationale: • For all but one of the components included in this composite (tobacco-free), the developer presented recommendations from the 2014 clinical practice guidelines developed by the Institute for Clinical Systems Improvement (ICSI), which were based on a systematic review of evidence that was graded either high or moderate. Additional evidence-based recommendations from the American College of Cardiology and U.S. Preventive Services Task Force also were presented. Committee members agreed that the evidence supports the relationship between each component and desired health outcomes. • Data provided by the developer indicate that for 2014, only 38.9% of diabetic patients in Minnesota met all five component targets from the composite measure. Committee members agreed that although performance on some of the components is quite high, overall performance indicates opportunity for improvement. • Although some Committee members voiced concern over the “all-or-none” structure of the measure, others agreed that a more comprehensive measure that focuses on management of multiple risk factors is needed. The Committee agreed that the developer description of the quality construct, rationale, and aggregation and weighting approach is explicitly articulated and logical.
  
  
• Review for Scientific Acceptability: 2. Scientific Acceptability of Measure Properties: The measure meets the Scientific Acceptability criteria (2a. Reliability - precise specifications, testing; 2b. Validity - testing, threats to validity) 2a. Reliability: H-9; M-7; L-0; I-0; 2b. Validity: H-1; M-10; L-4; I-1; 2d. Composite: H-1; M-10; L-4; I-1 Rationale: • Committee members noted that the specifications of the statin component of this measure have changed since the most recent endorsement of the measure due to changes in the ACC/AHA clinical practice guidelines on cholesterol management released in November, 2013. In the earlier version of the measure, the statin component assessed reaching a target LDL threshold of < 100 mg/dL; the revised version of this component assesses statin use. • Committee members questioned whether the measure assesses if a patient is on the appropriate statin dose. Developers clarified that the measure does not consider the statin dose but assesses only whether a patient is on a statin. • Members also questioned the age range of 18-75 for the statin component of the measure. The developer clarified that for patients 21-39 years of age, this component is applicable only if the patient has ischemic vascular disease or a very high LDL level, in accordance with the ACC/AHA guidelines. • The developer clarified that the level of analysis for the measure is clinician groups (not individual clinicians), and also noted that multiple clinics may form a clinician group. They also clarified that the measure does not require having a minimum of 30 patients. • Developers presented results of signal-to-noise reliability testing of the performance measure score. They clarified that the beta-binomial method was used for the reliability testing because the composite score itself is a binary (yes/no) measure. Members noted that although the 12 reliability was quite high for most clinician groups, it was lower than 0.7 for some clinician groups. • To demonstrate validity of the performance measure score, developers examined the association between the scores for this measure with the scores from the Optimal Vascular Care measure (NQF #0076), hypothesizing that clinician groups likely provide similar quality of care to different patients who also require management of multiple risk factors. The R2 value from this analysis was 0.64. The developers also described several steps occurring during the data submission process as demonstration of empirical validity testing at the data level element. • Developers also clarified that the measure is risk-adjusted for three factors (insurance type, age group, and diabetes type) and noted that the risk-adjustment strategy was developed using data from all clinicians in Minnesota. However, one member expressed some concern that the only adjustment for sociodemograhic status is insurance type. Developers clarified that other potential risk factors that were considered were not statistically significant and thus were not included in the risk-adjustment model. • Several Committee members voiced concern about holding physicians accountable for the patient’s tobacco use, as some see actual tobacco use (as opposed to efforts for tobacco cessation) as out of the control of the clinician. However, another member referred to data showing that physicians can influence their patients to stop tobacco use. Developers also noted that statewide, they have seen an approximate 2.5% increase in tobacco-free patients in Minnesota. • One Committee member noted the need for clarity about potential adverse effects related to statin use. Another member referenced the flow diagram provided by the developer that details several contraindications for statin use, while another member echoed the importance of the potential for adverse reactions when making treatment decisions. • After developers clarified the performance rates for each of the components, Committee members questioned whether the aspirin component (performance rate =99.5% in MN) is needed in the composite. Developers noted that while this component may be "topped out" in MN, this happened over a four-year period of focus on this component. They also referenced a New England Journal of Medicine article that found a 34.8% performance rate nationally in the primary care setting. Finally, they noted that performance on this component across ACOs is, on average, 75.3%.
  
  
• Review for Feasibility: 3. Feasibility: H-7; M-4; L-4; I-0 (3a. Clinical data generated during care delivery; 3b. Electronic sources; 3c.Susceptibility to inaccuracies/ unintended consequences identified 3d. Data collection strategy can be implemented) Rationale: • The measure data can be collected through electronic clinical data and paper records. • One Committee member noted that, due to the number of components included in the composite, the data collection effort for this composite measure may be intensive. Developers stated that submission of this measure by all clinician groups in MN is mandated by the state. While they acknowledged that MN has many large practices that use EHRs, small practices— even those who still use paper medical records—are able to submit data on this measure. The developers did, however, acknowledge the data collection burden for the new statin component if a patient has not been prescribed a statin (i.e., identifying exceptions due to contraindications).
  
  
• Review for Usability: 4. Usability and Use: H-5; M-7; L-4; I-0 ((Used and useful to the intended audiences for 4a. Accountability and Transparency; 4b. Improvement; and 4c. Benefits outweigh evidence of unintended consequences) Rationale: • Committee members noted that the measure is publicly reported and is used in pay-forperformance and accreditation programs. Performance is slowly increasing across the state of Minnesota, suggesting quality of care may be improving. • Data submitted by the developer demonstrate relatively consistent improvement of performance in MN from the years 2006-2014. • Committee members agreed that this composite measure is patient-centric and acknowledged the importance of using a comprehensive measure that assesses performance of reducing multiple risk factors. • Some Committee members expressed concern that the measure could incent some providers to "cherry-pick" patients or make their practices less hospitable to certain patients or certain subgroups of patients (the tobacco-free component of the measure was a particular concern).
  
  
• Review for Related and Competing Measures: 5. Related and Competing Measures • This measure is a competing measure to the following measures o 0061: Comprehensive Diabetes Care: Blood Pressure Control (< 7% to < 8% depending on individual patient factors. o 0575: Comprehensive Diabetes Care: Hemoglobin A1c (HbA1c) control (<8%) • NQF staff asked the Committee to discuss whether there is justification for continued endorsement of the individual measures if the composite retains endorsement. The Committee discussed the pros and cons of endorsing both individual measures and the composite measure. The Committee ultimately agreed that while the composite measure is useful to assess patientcentric performance across a variety of clinical areas, endorsement of individual measures also can be beneficial, particularly for users who want to focus on certain components of the composite or those who have data collection constraints and cannot use the composite. The Committee therefore recommended continued endorsement of both the individual measures and the composite measure.
  
  
• Endorsement Public Comments: 6. Public and Member Comment Comments received: • Two commenters raised concern over the glucose control component of the composite, referencing the National Action Plan for Adverse Event Prevention, which was released in August, 2014. The National Action Plan states that the blood glucose threshold of < 7% to < 8% depending on individual patient factors. Benefits: Achieving near-normal glycemic control lowers risk of diabetes microvascular complications such as retinopathy, nephropathy and amputations. Achieving A1c of 6.9 to 7.9% may also significantly reduce macrovascular complications based on Steno-2 and UKPDS data. Quality of Evidence: High Strength of Recommendation: Strong. Measurement does not and should not preclude good clinical judgement; however the measure development work group believes that a target of < 8.0 is reasonable and supported by guidelines. Our measure does have an upper age limit cut-off of 75 years and we allow exclusions for death, permanent nursing home resident or patients who are receiving hospice or palliative care services. • Two commenters were critical of the composite measure itself, citing concern that use of the composite measure could mask the individual care processes that most need improvement. Developer response: While it is true that the measure is reported at the composite level, the individual components and the associated rates are available to the medical groups for better understanding their rates and for use in quality improvement to know which areas have opportunity for improvement. MNCM and the measure development work group firmly believe that achieving the intermediate physiological outcome targets related to blood pressure and glycemic control in addition being tobacco free and use of daily aspirin and statins where appropriate are the diabetic patient’s best mechanisms of avoiding or postponing long term complications associated with this chronic condition which affects millions of Americans. Measuring providers separately on individual targets is not as patient centric as a measure that seeks to reduce multiple risk factors for each patient. Diabetic patients are more likely to reduce their overall risk and maximize health outcomes by achieving several intermediate physiological targets. • Two commenters noted that documenting HbA1c levels >8% but less than 9% cannot be done using CPT-II coding, necessitating need for medical chart review. Developer response: A point of clarification, these measure do not rely on CPTII codes for numerator compliance, nor are they indicated anywhere in our measure specification. Measure specifications focus on the electronic health record as a source of clinical information for calculating numerator compliance; actual A1c values are utilized in the case of the A1c target. Additionally, 80 to 90% of all the clinics in MN are reporting this information from their electronic health records without the need for additional chart abstraction. • One commenter suggested a need for including sociodemographic factors in the risk-adjustment approach. Developer response: Our risk adjustment model does include insurance product, which is a proxy for socioeconomic status. During the process of measure development, the expert panel discusses potential variables for risk adjustment that are important to consider for the measured population. For this measure, variables that are available for evaluation include gender, age, zip, race/ethnicity, country of origin, primary language, insurance product, diabetes type, depression and ischemic vascular disease. The potential risk adjustment variables are then evaluated for appropriate inclusion in the model based on a t value outside the range of -2.0 and +2.0. Currently, the variables that have demonstrated acceptable properties are insurance product, age bands (18-25, 26-50, 51-65 and 65 to 75) and diabetes type (1 or 2). Race/ethnicity has been collected for this measure in MN for the past few years, but has now reached a level of 15 reliability in which it can be evaluated for its impact. MNCM continues to review variables and their impact on the measure and part of its measure risk adjustment strategy. • One commenter suggested the need for additional detail regarding moderate or high intensity in the description of statin use for the measure. Developer response: The measure development work group thoroughly discussed the pros and cons of specifying a certain dose of the statin medication and based on the following factors ultimately decided to not specify a dose of moderate or high intensity for numerator compliance: 1) data burden for practices, 2) controversy and burden surrounding the CV risk calculator, 3) ICSI 2014 Diabetes Guideline recommendations for measurement and 4) cardiology work group member’s believe that there is some benefit for some patients who can only tolerate a lower intensity dose. Committee response: • During its review of the individual measure assessing HbA1c
  
  
• Endorsement Committee Recommendation: Standing Committee Recommendation for Endorsement: Y-13; N-4
  
  

Measure Specifications

• NQF Number (if applicable): *Note- 76 (This measure is a component of the endorsed composite measure NQF #76)
  
  
• Description: The percentage of patients 18-75 years of age who had a diagnosis of ischemic vascular disease (IVD) and were on daily aspirin or anti-platelet medication, unless allowed contraindications or exceptions are present.
  
  
• Numerator: Denominator patients with documentation that the patient was on daily aspirin or anti-platelet medication during the measurement period, unless allowed contraindications or exceptions are present.
  
  
• Denominator: 18 years or older at the start of the measurement period AND less than 76 years at the end of the measurement period AND Patient had a diagnosis of ischemic vascular disease (Ischemic Vascular Disease Value Set) with any contact during the current or prior measurement period OR had ischemic vascular disease (Ischemic Vascular Disease Value Set) present on an active problem list at any time during the measurement period. AND At least one established patient office visit (Established Pt Diabetes & Vasc Value Set) for any reason during the measurement period
  
  
• Exclusions: The following exclusions are allowed to be applied to the eligible population: - Patient was a permanent nursing home resident at any time during the measurement period - Patient was in hospice or receiving palliative care at any time during the measurement period - Patient died prior to the end of the measurement period - Documentation that diagnosis was coded in error - Patient had only urgent care visits during the measurement period
  
  
• HHS NQS Priority: Effective Prevention and Treatment
  
  
• HHS Data Source: Administrative clinical data, EHR, Paper medical record, Claims, Registry
  
  
• Measure Type: Process
  
  
• Steward: MN Community Measurement
  
  
• Endorsement Status: Endorsed
  
  
• Changes to Endorsed Measure Specifications?: The MUC list indicates the measure has not been modified from its endorsed version.
  
  
• Is the measure specified as an electronic clinical quality measure? No
  
  

Summary of Workgroup Deliberations

• Workgroup Recommendation: Conditional Support for Rulemaking
  
  
• Workgroup Rationale: MAP acknowledged the importance of Use of Aspirin or Anti-platelet Medication as a critical element of high quality vascular care. While this measure is included in the Optimal Vascular Care composite measure, MAP recognized that clinicians may still report Aspirin or Anti-platelet Medication measures separately to drive quality improvement. MAP also discussed that there is a competing measure in the program, ACO #30: IVD Use of Aspirin or Another Antiplatelet. MAP Conditionally Supported this measure with the condition that there are no competing measures in the program.
  
  
• Public comments received: 1

Rationale for measure provided by HHS
Risk Factor Optimization and Guideline-Directed Medical Therapy in US Veterans With Peripheral Arterial and Ischemic Cerebrovascular Disease Compared to Veterans With Coronary Heart Disease. Hira RS et al Am J Cardiol. 2016 Oct 15;118(8):1144-1149. doi: 10.1016/j.amjcard.2016.07.027. Epub 2016 Jul 29. Age-specific risks, severity, time course and outcome of bleeding on long-term anti-platelet treatment after vascular events: a population based cohort study. Linix, L et al Published online June 13, 2017 http://dx.doi.org/10.1016/S0140-6736(17)30770-5

Summary of NQF Endorsement Review

• Year of Most Recent Endorsement Review: 2017
  
  
• Project for Most Recent Endorsement Review: Cardiovascular 2016-2017
  
  
• Review for Importance: 1. Importance to Measure and Report: The measure meets the Importance criteria (1a. Evidence, 1b. Performance Gap, 1c. Composite) 1a. Evidence: H-14; M-6; L-1; I-1; 1b. Performance Gap: H-14; M-7; L-1; I-0; Composite: H-12; M-8; L-1; I- 1 Rationale: • For the 2012 maintenance of endorsement evaluation, the developer provided the following clinical practice guidelines to support the blood pressure, statin medication, tobacco free (outcome measure), and daily aspirin or anti-platelet medication components: o Blood pressure, statin medication, tobacco free, and daily aspirin or anti-platelet medication components: The ICSI Stable Coronary Artery Disease (April 2011), Address Modifiable Risk Factors guideline recommended modifiable risk factors for coronary artery disease such as smoking, inadequate physical activity, stress, hyperlipidemia, obesity, hypertension and diabetes mellitus be evaluated. 21 o Blood pressure: The Comorbid Conditions Guideline and the ICSI Hypertension Diagnosis and Treatment Guideline (November 2010) recommended a target blood pressure of 140/90 mmHg or less. o Statin medication: The ICSI Lipid Management in Adults (October 2009) guideline recommended target goals for hyperlipidemic patients with coronary artery disease: LDL – less than 100 mg/dL; HDL – 40 mg/dL or greater; Triglycerides – less than 150 mg/dL. o Daily aspirin or anti-platelet medication: The ICSI Stable Coronary Artery Disease (April 2011), Address Modifiable Risk Factors guideline recommended the use of one aspirin tablet daily (81-162 mg) unless there are medical contraindications. • For the current maintenance of endorsement evaluation, the developer provided the following updated evidence for all four components: o Blood pressure: The 2015 AHA/ACC/ASH Scientific Statement on the Treatment of Hypertension in Patients with Coronary Artery Disease included 3 recommendations for blood pressure targets, including a blood pressure goal of 65 and Medicare. •o Statin medication: The ICSI Lipid Management in Adults (updated Nov 2013/completed prior to ACC/AHA release) recommends that clinicians initiate statin therapy regardless of LDL in patients with established atherosclerotic cardiovascular disease (ASVCD). The 2013 ACC/AHA Guideline for the Treatment of Blood Cholesterol to Reduce Atherosclerotic Cardiovascular Risk in Adults recommends high-intensity statin therapy be initiated or continued as first-line therapy in women and meno Tobacco free outcome measure: The developer provided evidence from the United States Preventive Services Task Force (USPSTF) stating that despite considerable progress in tobacco control over the past 50 years, in 2013, an estimated 17.8% of U.S. adults and 15.9% of pregnant women aged 15 to 44 years were current cigarette smokers. o Daily aspirin or anti-platelet medication: The developer provided three recommendations for antiplatelet agents/anticoagulants for patients with ischemic vascular disease from the AHA/ACCF Secondary Prevention and Risk Reduction Therapy for Patients with Coronary and Other Atherosclerotic Vascular Disease: 2011 Update. • The Standing Committee discussed the potential changes to blood pressure parameters based on the results of the Systolic Blood Pressure Intervention Trial (SPRINT), which compared the benefit of treatment of systolic blood pressure to a target of less than 120 mm Hg with treatment to a target of less than 140 mm Hg. The Committee also discussed the anticipated blood pressure guidelines to be released by AHA/ACC sometime in the future. NQF staff asked the Committee to consider the quantity, quality, and consistency of the body of evidence that was presented in the measure submission form. NQF staff reassured the Committee that the NQF process allows for a measure to be reviewed when new evidence becomes available. One of the Committee members noted that the USPSTF recommendations for daily aspirin include patients aged 50 to 70 years old, while the measure includes patients up to 75 years old. Other Committee members noted that the USPSTF recommendations are for primary prevention rather than patients with a diagnosis of ischemic vascular disease (IVD). Overall, the Standing Committee agreed that the updated evidence supports blood pressure control, statin use, daily aspirin or anti-platelet medication, and tobacco use assessment and 22 intervention(s) in patients to avoid or postpone long-term complications associated with a diagnosis with IVD. The developer provided composite performance rates from clinics in Minnesota for Report Year 2007-2016 (Dates of Service 2006-2015). o In 2007, the rate was 38.9% for 4,662 patients and 33.8% in 2010 for 63,241 patients. In 2011, the blood pressure component target changed from, and the performance rate increase to 39.7% for 66,910 patients. o In 2015, the cholesterol management component was suppressed during redesign of the measure and the performance rate increased to 69.3% for 102,654 patients. o In 2016, the cholesterol management component was changed from LDL <100 to appropriate statin use and the performance rate was 66.1% for 104,395 patients. • The developer also provided performance rates for the individual components. o The blood pressure component increased from 84.0% in 2012 to 85.0% in 2016. o Daily aspirin use or anti-platelet medication use increased from 92.5% in 2009 to 96.7% in 2016. o The number of tobacco free patients increased from 82.4% in 2009 to 83.0 in 2016. o Statin use was 95.2% in 2016 (this was the first year the new component was reported) The developer provided 2014 disparities data from the measure as specified demonstrating a performance rate of 67.2% for White patients, 47.6% for Black/African-American patients, 51.8% for American Indian/Alaska Native patients, and 53.4% for multi-racial patients. The data also showed a higher performance gap for female patients and younger patients. The Committee asked the developer if there were trend data on disparities that demonstrated a change in performance over time and by individual clinic. The developer did not have additional, specific disparities data. However, according to the developer, some clinics that care for a greater proportion of minority patients have lower performance rates but there are a couple of clinics that are excelling in minimizing disparities. • The Standing Committee agreed that the data provided demonstrated a performance gap and opportunity for improvement in optimal vascular care for patients with IVD. • This is an all-or-none composite measure that requires patients to meet all four component targets in the composite measure to be considered ‘optimally managed’; all four components are weighted equally. The developer noted that measuring providers on individual targets is not as patient-centric as this composite measure that seeks to reduce multiple risk factors in patients with IVD and maximize health outcomes. One of the members of the Standing Committee noted that the tobacco free component would be more appropriate as a process measure. The Committee member noted that smoking rates are often influenced by geographic location. Providers in areas with high rates of tobacco use will not appear as effective in increasing the number of tobacco free patients as those in areas where tobacco use is less prevalent. In the pre-evaluation comments, another Committee member noted that the absolute benefit of each component is not equal; achieving blood pressure control or smoking cessation is much more difficult than prescribing a statin or aspirin/anti-platelet medication. • The Standing Committee agreed, that overall, the quality construct and rational for the composite was clearly stated and logical.
  
  
• Review for Scientific Acceptability: 2. For the 2012 maintenance of endorsement evaluation, patient-level data element validity testing was conducted on 63,241 patients with IVD from 128 medical groups representing 573 clinics that submitted data to Minnesota Community Measurement for 2009 dates of service reported in 2010. After data submission, in-person validation audits requiring a 90% accuracy rate were conducted to compare the submission to the patient’s medical record. Of the 128 medical groups that submitted data in 2010, 17 groups initially failed the audit and remedy plans were developed. All 17 groups resubmitted and passed subsequent audit. • For the current maintenance of endorsement evaluation, the measure was tested at the measure score level using a dataset that included 104,395 patients with IVD in Minnesota and neighboring communities from 111 medical groups representing 671 clinics for dates of service from January 1, 2015 to December 31, 2015. • To test the reliability of the measure score, the developer used a beta-binomial model to assess the signal-to-noise ratio. A reliability score of 0.00 implies that all the variability in a measure is attributable to measurement error. A reliability score of 1.00 implies that all the variability is attributable to real differences in performance. The higher the reliability score, the greater is the confidence with which one can distinguish the performance of one facility from another. This is an appropriate test for measure score reliability. A reliability score of 0.70 is generally considered a minimum threshold for reliability. The overall reliability for the composite measure was 0.90 and 0.61 at the minimum number of patients per reportable clinic (=30). The distribution of reliability scores by number of eligible patients per reportable clinic (=30) ranged from 0.61 for 30 patients per clinic to 0.99 for 4,441 patients per clinic. • In the pre-evaluation comments, a member of the Standing Committee mentioned that assessing prescribing behavior of statin therapy (as noted in the specifications) is not consistent with the evidence provided to support the statin component. The Committee member noted that prescribing the lowest dose of the weakest statin would meet the intent of the measure but not generate clinically significant outcomes in the IVD population. Other Committee members questioned why ‘permanent nursing home residents’ are excluded from the denominator. The Committee discussed the fall risks associated with administering blood pressure medication to nursing home patients, excessive treatment in patients with advanced illness, and the lack of clinical trials for these types of medications in the nursing home population. • The Standing Committee did not express additional concerns with the reliability of the measure, but ultimately decided the testing results were sufficient. • For the 2012 maintenance of endorsement evaluation, content and face validity were assessed through the Measurement and Reporting Committee and a panel of experts. There was consensus among the expert workgroup that the target components reflected a quality of care that will reduce patients heart attack and stroke risk. • For the current maintenance of endorsement evaluation, empirical validity testing of the composite measure score was conducted by testing the correlation of a medical group’s performance with their performance on the Optimal Diabetes Care measure (#0729). It is expected that the quality of care provided by a medical group to a patient with ischemic vascular disease would be of similar quality as the care provided to a patient with diabetes, therefore the respective performance measure scores should be similar. This is an appropriate method for assessing conceptually and theoretically sound hypothesized relationships. The Optimal Diabetes Care measure (#0729) includes the same four components as #0074 plus a component for hemoglobin A1C; it also measures a different population. The linear regression analysis demonstrated an R2 value of 0.635, which means that 64.0% of the total variation in performance on the Optimal Vascular Care measure can be explained by variation in performance on the Optimal Diabetes Care measure. The remaining 36.0% of total variation on the Optimal Vascular Care measure remains unexplained. • This measure is risk-adjusted. The final risk factors selected for the risk model were age and insurance product (Medicare, Medicaid, MSHO, Special Needs, Self-pay, Uninsured). The developer analyzed gender and depression as well, but gender did not show sufficient variation between clinics and ‘depression’ was not selected due to the high cost of collection. The developer stated that race, ethnicity, language, and country of origin (RELO) were not considered for risk adjustment because these variables did not have a high completion rate across all clinics. The developer is continuing to work with the medical community to achieve the goal of evaluating RELO at the clinic level. The developer conducted an Analysis of Maximum Likelihood Estimates on the 2014 Dates of Service to compare the optimal rate of patients by insurance product (Commercial, MHCP, and Uninsured) to patients with Medicare and patient age (18-25; 26-50; 51-65) to patients aged 66-75. The Analysis of Maximum Likelihood Estimates demonstrated that all of the results for both variables, age and insurance product, were significant, except for ages less than 26 due to the small sample size (n = 44). The developer also found that the only two variables that were correlated were age >65 and Medicare. The Standing Committee did not express any concerns on the threats to validity and agreed that the testing results satisfied the validity criterion. • The developer conducted a Pearson Correlation Analysis of the individual components rates and the composite rates. The Pearson correlation coefficient value, r, ranges from +1.00 to -1.00. A value of 0.00 indicates that there is no association between the two variables. A value greater than 0.00 indicates a positive association; that is, as the value of one variable increases, so does the value of the other variable. A value less than 0.00 indicates a negative association; that is, as the value of one variable increases, the value of the other variable decreases. The developer conducted the following Pearson Correlation Analysis for each component: Variable Mean Pearson r coefficient Blood pressure 0.85048 0.69813 Tobacco Free 0.80901 0.71336 Daily ASA Use 0.96271 0.59223 Statin Use 0.93973 0.62327 Optimal Vascular Care Rate = 0.63919 • The developer concluded that practices in Minnesota demonstrate relatively high compliance for all of the components; however, there is still an opportunity for improvement at the clinic level. The blood pressure control and tobacco free components demonstrated the most variability, opportunity for improvement, and impact the ability to achieve all four components. Another Committee member suggested that if the two variables with the most variability were more heavily weighted than the other components, the measure would be more impactful. Another Committee member pointed out that three of the components were under the direct control of the provider, yet it was not clear how the tobacco free component captured the quality of care provided by the clinician. A member of the Standing Committee questioned whether there was evidence showing that meeting all four component targets would not 25 generate the same patient outcomes as meeting two or three of the components. The developer pointed out that various combinations of the components and the proportion of patients meeting the different combinations were provided. • The Standing Committee did not express additional concerns with the construct of the composite measure and agreed the information provided was sufficient to satisfy the criterion for composite construct.
  
  
• Review for Feasibility: 3. Feasibility: H-12; M-10; L-0; I-0 (3a. Clinical data generated during care delivery; 3b. Electronic sources; 3c. Susceptibility to inaccuracies/ unintended consequences identified 3d. Data collection strategy can be implemented) Rationale: • All of the data elements are in defined fields in electronic sources and there are no fees, licensure, or other requirements necessary to use this measure. The Standing Committee agreed this measure met the feasibility criterion.
  
  
• Review for Usability: 4. Usability and Use: H-13; M-8; L-1; I-0 (Used and useful to the intended audiences for 4a. Accountability and Transparency; 4b. Improvement; and 4c. Benefits outweigh evidence of unintended consequences) Rationale: • The measure is widely used in Minnesota for public reporting, payment, regulatory and accreditation programs, and quality improvement with external benchmarking to multiple organizations. 5. Related and Competing Measures • This measure is related to: o #0067: Chronic Stable Coronary Artery Disease: Antiplatelet Therapy (ACC) o #0068: Ischemic Vascular Disease (IVD): Use of Aspirin or Another Antiplatelet (NCQA) o #0073: Ischemic Vascular Disease (IVD): Blood Pressure Control (NCQA) • The developer stated that #0068 and #0073 focus on the inpatient setting and patients discharged with AMI, CABG, or PCI. #0067 focuses on patients with CAD.
  
  
• Review for Related and Competing Measures: 5. Related and Competing Measures • This measure is related to: o #0067: Chronic Stable Coronary Artery Disease: Antiplatelet Therapy (ACC) o #0068: Ischemic Vascular Disease (IVD): Use of Aspirin or Another Antiplatelet (NCQA) o #0073: Ischemic Vascular Disease (IVD): Blood Pressure Control (NCQA) • The developer stated that #0068 and #0073 focus on the inpatient setting and patients discharged with AMI, CABG, or PCI. #0067 focuses on patients with CAD.
  
  
• Endorsement Public Comments: 6. Public and Member Comment • One commenter did not agree with statin use as a component to address dyslipidemia and believed it would be misleading to include this as a component of “optimal care.” The commenter believed including this component would lead to the lowest level of acceptable care being considered optimal care and would do little to move the quality of care forward. • Developer Response: Thank you for your comment and suggestion for the inclusion of the dose of statin (moderate or high) in the calculation of the cholesterol component of this patient level all-or-none composite measure. While ACC/ AHA guidelines do indicate that most patients with ischemic vascular disease would benefit from high dose intensity statins, there 26 are provisions for moderate intensity statins for patients who cannot tolerate high intensity doses. The measure development work group thoroughly discussed the pros and cons of specifying a certain dose of the statin medication for numerator component compliance and determined that requiring the submission of the dose of statin would cause undue data collection burden for the practices. Additionally, the cardiologists on the workgroup strongly believe that there is some benefit for patients who can only tolerate a low dose of statin. We do not discount the role of ongoing LDL monitoring to determine effectiveness of statin therapy, but having a physiological target (e.g. LDL < 100) is no longer supported by evidence. The American College of Cardiology/ American Heart Associate guidelines for the treatment of blood cholesterol indicate the following: “Treat to target — this strategy has been the most widely used the past 15 years but there are 3 problems with this approach. First, current clinical trial data do not indicate what the target should be. Second, we do not know the magnitude of additional ASCVD risk reduction that would be achieved with one target lower than another. Third, it does not take into account potential adverse effects from multidrug therapy that might be needed to achieve a specific goal. Thus, in the absence of these data, this approach is less useful than it appears (Section 3). It is possible that future clinical trials may provide information warranting reconsideration of this strategy” (pg. 17) Yes, our component rates for prescribing statins are high in MN, which is a little bit unexpected for the newly re-designed component, however we would like to clarify the cholesterol component of statin use is not reported as a stand-alone measure. The Optimal Vascular Care measure is reported as an all-or-none composite, patients achieving multiple components of modifiable risk factors to reduce or delay long term complications. Statin use is one component, the other three are blood pressure control, tobacco-free and daily aspirin or antiplatelet medication. • Committee Response: Thank you for your comment. The Committee agrees that monitoring LDL levels remains an important part of providing care for patients with IVD. However, the statin component in this measure aligns with the 2013 ACC/AHA Guideline for the Treatment of Blood Cholesterol to Reduce Atherosclerotic Cardiovascular Risk in Adults.
  
  
• Endorsement Committee Recommendation: Standing Committee Recommendation for Endorsement: Y-19; N-3
  
  

Measure Specifications

• NQF Number (if applicable): 3188
  
  
• Description: 30-Day Unplanned Readmissions for Cancer Patients measure is a cancer-specific measure. It provides the rate at which all adult cancer patients covered as Fee-for-Service Medicare beneficiaries have an unplanned readmission within 30 days of discharge from an acute care hospital. The unplanned readmission is defined as a subsequent inpatient admission to a short-term acute care hospital, which occurs within 30 days of the discharge date of an eligible index admission and has an admission type of “emergency” or “urgent.”
  
  
• Numerator: The numerator includes readmissions of the following patients with an eligible index admission in the measure denominator: 1) Readmitted to a short-term acute care hospital (PCHs, short-term acute care PPS hospitals, and CAHs) within 30 days of the discharge date of an index admission; and, 2) Readmitted with a Claim Inpatient Admission Type Code of “Emergency” or “Urgent” (“1” or “2”). Of note, if a patient has more than one unplanned admission within 30 days of discharge from the index admission, each readmission is only counted once in the numerator.
  
  
• Denominator: The denominator includes index admissions at acute care hospitals (PCHs, short-term acute care PPS hospitals, and CAHs) for patients with a discharge date during the measurement period that meet the following criterion: 1) Primary Claim Diagnosis Code or Claim Diagnosis Code I-XXV of malignant cancer (ICD-9-CM range: 140.00-209.36, 209.70-209.79, 511.81, 789.51; ICD-10-CM range: C00 -- C96.9, J91.0, R18.0). Of note, a readmission that meets the denominator criteria is included as an index admission within this measure if it meets all other eligibility criteria.
  
  
• Exclusions: Numerator The following readmissions are excluded from the measure numerator: 1) Primary Claim Diagnosis Code of metastatic disease (ICD-9-CM range: 196-198.89, 209.70â€209.79; ICD-10-CM range: C77.0 -- C79.9, C7B.0-C7B.8). Rationale: A primary (or principal) diagnosis of metastatic disease serves as a proxy for disease progression. Readmissions for conditions or symptoms associated with disease progression are not reflective of poor clinical care but, rather, advanced disease. 2) Patients with a Primary Claim Diagnosis Code of chemotherapy or radiation encounter (ICD-9-CM range: V58.00-V58.12; ICD-10-CM range: Z51.00 -- Z51.12) as these are considered planned admissions. Rationale: Readmissions are expected and planned for some patients who require additional cancer treatment in the inpatient setting. These readmissions reflect high-quality care that is focused on patient safety and are reliably distinguishable in claims data. Denominator The following index admissions are excluded from the measure denominator: 1) Age less than 18 years of age (based on the beneficiary’s age at the end of the prior year). Rationale: Pediatric patients represent a very small and distinct Medicare population with different characteristics and outcomes.
  
  
• HHS NQS Priority: Patient and Family Engagement; Making Care Safer; Communication and Care Coordination
  
  
• HHS Data Source:
  
  
• Measure Type: Outcome
  
  
• Steward: Seattle Cancer Care Alliance
  
  
• Endorsement Status: Endorsed
  
  
• Is the measure specified as an electronic clinical quality measure? No
  
  

Summary of Workgroup Deliberations

• Workgroup Recommendation: Support for Rulemaking
  
  
• Workgroup Rationale: MAP supported MUC 17-178 for use in the PCHQR program. This measure is fully developed and tested, and has received NQF endorsement. MAP agreed that this fills a current gap in the PPS-Exempt Cancer Hospital Quality Reporting Program by addressing unplanned readmissions of cancer patients.
  
  
• Public comments received: 3

Rationale for measure provided by HHS
Cancer is the second leading cause of death in the United States, with nearly 600,000 cancer-related deaths expected this year.1 It is now the leading cause of death among adults aged 40 to 79 years as well and in 21 states.2 It is estimated roughly 1.7 million Americans will be diagnosed with cancer in 2016, and nearly 14.5 million Americans with a history of cancer were alive in 2014. Cancer disproportionately affects older Americans, with 86% of all cancers diagnosed in people 50 years of age and older.1 Oncology care contributes greatly to Medicare spending and accounted for an estimated $125 billion in healthcare spending in 2010. This figure is projected to rise to between $173 billion and $207 billion by 2020.3 Given the current and projected increases in cancer prevalence and costs of care, it is essential that healthcare providers look for opportunities to lower the costs of cancer care. Reducing readmissions after hospital discharge has been proposed as an effective means of lowering healthcare costs and improving the outcomes of care. Research suggests that between 9% and 48% of all hospital readmissions are preventable, owing to inadequate treatment during the patient’s original (index) admission or after discharge.4 Jencks, et al. estimated that unplanned readmissions cost the Medicare program $17.4 billion in 2004.5 Unnecessary hospital readmissions negatively impact cancer patients by compromising their quality of life, by placing them at risk for health-acquired infections, and by increasing the costs of their care. Furthermore, unplanned readmissions during treatment can delay treatment completion and, potentially, worsen patient prognosis. Preventing these readmissions improves the quality of care for cancer patients. Numerous studies have examined all-cause readmissions and readmissions for specific conditions, such as orthopedic surgery. Existing studies in cancer have largely focused on post-operative readmissions, reporting readmission rates between 6.5% and 25%. Patient factors, including age, comorbidities, cancer stage, and socioeconomic status, were identified as risk factors in these patients. Surgical complications, surgery duration, and hospital length of stay also increased readmission risk in these studies. Finally, hospital factors (e.g., hospital size) and practice patterns, such as inadequate discharge planning, comorbidity management, and follow-up care, were associated with preventable readmissions.6-17 Moya, et al. observed a 20% readmission rate in hematopoietic cell transplantation (HCT) recipients along with an extended length of stay during the readmission (25 ± 21 days). Infections (some associated with the graft), graft failure, coagulation disorders, and a second neoplasm were the most frequent causes of readmission.18 Bejanyan, et al. examined readmissions in patients with myeloablative allogeneic HCT and observed a 39% readmission rate in these patients. Infections, fever, gastrointestinal complications, and graft-versus-host disease (GVHD) were the most frequent reasons for readmission.19 Less is known about other readmissions in medical cancer admissions, though Ji, et al. noted that surgical patients were most often readmitted for surgical complications while medical patients were typically readmitted for the same condition treated during the index admission.6 Together, these studies suggest that certain readmissions in cancer patients are preventable and should be routinely measured for purposes of quality improvement and accountability. All-cause and disease-specific unplanned readmissions rates have been adopted by the Centers for Medicare & Medicaid Services (CMS) as key indicators of inpatient quality care. Additionally, Medicare began reducing payments to hospitals with excess readmissions in October 2012, as mandated in the Patient Protection and Affordable Care Act of 2010. Benbassat, et al. concluded that global readmission rates are not useful indicators of healthcare quality and, instead, recommended measuring readmissions at the condition level.4 Readmission rates have been developed for pneumonia, acute myocardial infarction, and heart failure. However, cancer has lagged behind these conditions in the development of validated readmission rates. In 2012, the Comprehensive Cancer Center Consortium for Quality Improvement, or C4QI (a group of eighteen academic medical centers that collaborate to measure and improve the quality of cancer in their centers), began development of a cancer-specific unplanned readmissions measure: 30-Day Unplanned Readmissions for Cancer Patients. The Alliance of Dedicated Cancer Centers, or ADCC (an organization of eleven comprehensive cancer centers that are reimbursed differently by Medicare), identified this ongoing work as a potential accountability measure for the PCHQR. Both groups recognize the importance of measuring unplanned readmissions as an indicator of the quality of hospital-based oncology care and have designed the 30-Day Unplanned Readmissions for Cancer Patients measure accordingly.5,6 This measure is intended to reflect the unique clinical aspects of oncology patients and to yield readmission rates that more accurately reflect the quality of cancer care delivery, when compared with broader readmissions measures. Likewise, this measure addresses cancer measurement gaps in existing readmissions measures, such as the Hospital-Wide All-Cause Unplanned Readmission Measure (HWR), stewarded by CMS. The 30-Day Unplanned Readmissions for Cancer Patients measure can be used by individual hospitals to inform local quality improvement efforts. Through adoption in public reporting programs (e.g., PCHQR), it can increase transparency around the quality of care delivered to patients with cancer. 1. American Cancer Society. Cancer facts and figures 2016. 2016. Available at: http://www.cancer.org/acs/groups/content/@research/documents/document/acspc-047079.pdf. 2. Siegel RL, Miller KD, Jemal A. Cancer statistics, 2016. CA Cancer J Clin. 2016;66(1):7-30. 3. Mariotto AB, Yabroff KR, Shao Y, Feuer EJ, Brown ML. Projections of the cost of cancer care in the United States: 2010-2020. J Natl Cancer Inst. 2011;103(2):117-128. 4. Benbassat J, Taragin M. Hospital readmissions as a measure of quality of health care: advantages and limitations. Arch Intern Med. 2000;160(8):1074-1081. 5. Jencks SF, Williams MV, Coleman EA. Rehospitalizations among patients in the Medicare fee-for-service program. N Engl J Med. 2009;360(14):1418-1428. 6. Ji H, Abushomar H, Chen XK, Qian C, Gerson D. All-cause readmission to acute care for cancer patients. Healthc Q. 2012;15(3):14-16. 7. Rochefort MM, Tomlinson JS. Unexpected readmissions after major cancer surgery: an evaluation of readmissions as a quality-of-care indicator. Surg Oncol Clin N Am. 2012;21(3):397-405, viii. 8. Manzano JG, Luo R, Elting LS, George M, Suarez-Almazor ME. Patterns and predictors of unplanned hospitalization in a population-based cohort of elderly patients with GI cancer. Journal of clinical oncology : official journal of the American Society of Clinical Oncology. 2014;32(31):3527-3533. 9. Dickinson H, Carico C, Nuno M, et al. Unplanned readmissions and survival following brain tumor surgery. J Neurosurg. 2015;122(1):61-68. 10. Fernandez FG, Khullar O, Force SD, et al. Hospital readmission is associated with poor survival after esophagectomy for esophageal cancer. Ann Thorac Surg. 2015;99(1):292-297. 11. Manzano JG, Gadiraju S, Hiremath A, Lin HY, Farroni J, Halm J. Unplanned 30-Day Readmissions in a General Internal Medicine Hospitalist Service at a Comprehensive Cancer Center. J Oncol Pract. 2015;11(5):410-415. 12. Saunders ND, Nichols SD, Antiporda MA, et al. Examination of unplanned 30-day readmissions to a comprehensive cancer hospital. J Oncol Pract. 2015;11(2):e177-181. 13. Shah SP, Xu T, Hooker CM, et al. Why are patients being readmitted after surgery for esophageal cancer? J Thorac Cardiovasc Surg. 2015;149(5):1384-1389; discussion 1389-1391. 14. Valero-Elizondo J, Kim Y, Prescott JD, et al. Incidence and Risk Factors Associated with Readmission After Surgical Treatment for Adrenocortical Carcinoma. J Gastrointest Surg. 2015;19(12):2154-2161. 15. Uppal S, Penn C, Del Carmen MG, Rauh-Hain JA, Reynolds RK, Rice LW. Readmissions after major gynecologic oncology surgery. Gynecol Oncol. 2016;141(2):287-292. 16. Wilbur MB, Mannschreck DB, Angarita AM, et al. Unplanned 30-day hospital readmission as a quality measure in gynecologic oncology. Gynecol Oncol. 2016;143(3):604-610. 17. Nakayama JM, Ou JP, Friedman C, Smolkin ME, Duska LR. The Risk Factors of Readmission in Postoperative Gynecologic Oncology Patients at a Single Institution. Int J Gynecol Cancer. 2015;25(9):1697-1703. 18. Moya R, Espigado I, Parody R, Carmona M, Marquez F, De Blas JM. Evaluation of readmissions in hematopoietic stem cell transplant recipients. Transplant Proc. 2006;38(8):2591-2592. 19. Bejanyan N, Bolwell BJ, Lazaryan A, et al. Risk factors for 30-day hospital readmission following myeloablative allogeneic hematopoietic cell transplantation (allo-HCT). Biol Blood Marrow Transplant. 2012;18(6):874-880.

Summary of NQF Endorsement Review

• Year of Most Recent Endorsement Review: 2017
  
  
• Project for Most Recent Endorsement Review: All-Cause Admissions and Readmissions 2017
  
  
• Review for Importance: 1. Importance to Measure and Report: The measure meets the Importance criteria (1a. Evidence, 1b. Performance Gap) 1a. Evidence: Y-23; N-0; 1b. Performance Gap: H-10; M-11; L-0 I-0 21 Rationale: • As a rationale for measuring this health outcome, the developer lists several studies from peerreviewed journals explaining that cancer is the second cause of death in the United States, with nearly 600,000 cancer-related deaths expected this year. • The developer explains that this measure intends to reflect the unique clinical aspects of oncology patients and to yield readmission rates that may be obscured by a broader readmission measure, such as the Hospital-Wide All-Cause Unplanned Readmission Measure (HWR). The developer notes that there are several clinical actions that can be taken by the accountable entity to improve the outcome of 30-day readmissions. Specifically, the logic model notes that providers can ensure that patients are clinically ready for discharge with clear and appropriate follow-up care planned. These actions will help foster improved patient care, better population health, and reduce readmission risk. • The Committee agreed that the measure was supported by the literature and reflects critical aspects of cancer care for patients. The Committee also agreed that there are numerous clinical actions that can be taken to impact the result of the measure. • The developer studied 4,975 acute care hospitals and evaluated their potential performance gap over three years. The Committee noted that differences in performance across quartiles (Average: 16.54; 25th percentile: 12.5, 50th percentile: 17.32, and 75th percentile: 20.80) demonstrated a significant opportunity for improvement on the measure. • Committee members noted that there was a disparity by race (i.e. black patients had a higher readmission rate). Committee members also supported the developers decision not to include race in the risk adjustment model due to potential concerns about masking disparities. • One committee member questioned the assumption that scheduled care is high quality by definition and questioned the evidence base for the assumption. The committee member noted that there are many readmissions that are scheduled that are not patient-centered or protocoldriven, but instead based on timing issues with specialty providers, etc.
  
  
• Review for Scientific Acceptability: 2. Scientific Acceptability of Measure Properties: The measure meets the Scientific Acceptability criteria (2a. Reliability - precise specifications, testing; 2b. Validity - testing, threats to validity) 2a. Reliability: H-0; M-17; L-5; I-0 2b. Validity: H-0; M-11; L-11; I-0 (Consensus Not Reached) Revote Post-Comment: H-1 M-14; L-3; I-1 Rationale: • This outcome measure demonstrates the rate at which adult cancer patients have unplanned readmissions within 30 days of discharge from an eligible index admission. • The numerator includes all eligible unplanned readmissions to any short-term acute care hospital—defined as admission to a PPS-Exempt Cancer Hospital (PCH), a short-term acute care Prospective Payment (PPS) hospital, or Critical Access Hospital (CAH)—within 30 days of the discharge date from an index admission that is included in the measure denominator. Readmissions with an admission type (UB-04 Uniform Bill Locator 14) of “emergency = 1” or “urgent = 2” are considered unplanned readmissions within this measure. Readmissions for patients with progression of disease (using a principal diagnosis of metastatic disease as a proxy) and for patients with planned admissions for treatment (defined as a principal diagnosis of chemotherapy or radiation therapy) are excluded from the measure numerator. • The denominator includes inpatient admissions for all adult Fee-for-Service Medicare beneficiaries where the patient is discharged from a short-term acute care hospital (PCH, short- 22 term acute care PPS hospital, or CAH) with a principal or secondary diagnosis (i.e., not admitting diagnosis) of malignant cancer within the defined measurement period. • The measure is specified for a facility level of analysis and the hospital setting. • The Committee discussed the specifications of the measure’s numerator and denominator. Committee members agreed that it was appropriate to specify the numerator using emergency and urgent codes and excluding codes that relate to planned admissions. One committee member questioned if use of emergency/urgent codes varied across hospitals based on documentation processes. • The Committee noted that there were several exclusions from the denominator—including transfer patients, the missing data patients and the patients not admitted. A Committee member expressed concerned about patient-level exclusions, and noted that up to 20% of data in the numerator would not be included due to exclusions. The developer clarified that the exclusions are important to the measure. The developer noted that planned readmissions for chemotherapy, radiation oncology and disease progression are important, otherwise the measure would just closely resemble a measure for all-cause readmission for cancer patients. • A Committee member noted that the exclusion based on progression might lead to biases by cancer type. Some cancers are more likely to be metastatic in terms of their behavior than others. Another committee member suggested that the use of metastatic codes identified through medical records might help address the issue. Committee members also noted that the distribution of metastatic patients may be variable across hospitals. The developer clarified that the measure includes risk adjustment for solid tumor without metastasis and then a separate metastasis adjuster. The developer noted that they did not exclude patients with metastatic cancer from the measure itself but are excluding patients that have a principal guidance of metastatic disease on the readmission claim—to differentiate between quality of care and disease status. • The Committee noted that the measure only looks at hospitals with more than 50 readmissions, so low-volume hospitals would not be included in the measure. Committee members commented that they would like to see sensitivity analysis for excluded data at the hospital level. The developer clarified that they were interested in including as many hospitals as possible in the measure, but noted that smaller volume hospitals would have less reliability. Their analysis found that 50 readmissions seemed to be the point where they were able to generate strong validity and reliability scores. The developer also noted that they did conduct sensitivity analysis around three cut points: 50, 75 and 100. • Reliability was tested at the measure score level. To demonstrate measure score reliability, the developer conducted a test/retest analysis to evaluate the measure’s ability to generate consistent results with randomly selected subset of patients over time. The developers calculated two metrics of agreement – the intraclass correlation coefficient (ICC) and the Spearman-Brown Prophecy Formula (S-B). The ICC is estimated from a random effects model producing risk-adjusted rates. The S-B formula projects correlation as if the full sample is used and not spilt randomly. • The reliability testing results for the three-year period (CY2013-CY2015) produced an ICC of 0.570 (95% CI: 0.567, 0.572) and 0.482 (95% CI: 0.479, 0.485), for unadjusted and risk-adjusted values, respectively. The developer notes that this result may be interpreted as “fair” reliability. The mean S-B for the same period was 0.726 (95% CI: 0.724, 0.728) for unadjusted rates and 0.650 (95% CI: 0.648, 0.653) for risk-adjusted rates. The developer notes that both of these values are significantly higher than the 0.5 that indicates a large effect size with p-values < 23 0.001. When applied to each year individually, the S-B analysis exceeded 0.50 (p-values in 2013 and 2014 but not 2015. • Committee members asked if the measure was meant to be calculated using three years of data, as that reliability testing was implemented using this timeframe. The developer clarified that the measure is intended to be an annual measure. They tested the three-year period in total but also evaluated each calendar year independently. • A Committee member suggested that the measure should consider including observation stays and emergency room visits. • The developer assessed validity at both the measure score and data element levels. • The developer conducted two analyses to test the validity of the measure score. These analyses were: • 1) evaluating the sensitivity and specificity of the UB-04 inpatient admission type code. This analysis was previously conducted using a manual chart review. 2) correlation between this measure and NQF #1789 CMS Hospital-Wide All-Cause Readmissions measure. • The results of the two analysis are as follows: o The previous data element validity testing generated a global sensitivity and specificity score of 0.879 and 0.896, respectively. o The overall correlation between NQF #1789 and NQF #3188 was 0.2769 with a p-value of. This is a statistically significant positive correlation between the two measures. • Committee members noted that the correlation with the all cause readmissions measure (NQF #1789) was on the low end, but still significant to provide sufficient evidence of validity. • A Committee member asked about the relationship of the measure with 30 day mortality rates after noting that patient populations 85 and older had the lowest readmission rates, perhaps due to out of hospital deaths. The developer noted that six percent of patients in the denominator had been excluded because they expired during the index admission. • The Committee raised several concerns around the methods for risk adjustment used. First, the Committee was concerned about collapsing multiple comorbidities into a single risk adjustment variable. Committee members were concerned that quaternary centers who serve the most clinically complex patients may not be accurately characterized using this method. Further, the Committee noted that not all comorbidities have an equal impact on readmissions. Second, the Committee was concerned with the use of age 65 and less as the reference age for the model. Third, the Committee was concerned with the use of ‘hospitalization in the prior 60 days’ as a proxy for frequent admitters. The Committee was concerned that the risk adjusting for patients who are high utilizers could possibly inadvertently adjust for the hospital’s quality, as high utilization is a poor outcome in itself. • The developers noted that there was a conceptual and empirical rationale for adjustment based on dual-eligibility status. Dual-eligibility can serve as a proxy for low-income status and other measures of social risk. Several studies were referenced that note that social risk is a risk factor for later-state cancer diagnosis, delayed health care receipt, and higher utilization of hospitalbased care. • The patient-level observed 30-Day Unplanned Readmissions for Cancer Patients rate was 22.49%, compared with an 18.32% observed rate for all other patients. “Dual-Eligible Status” was associated with a Chi-Square of 5547.9628 (p Initially, the Committee did not reach consensus on the validity sub criterion. 24 • The Committee requested feedback from the member and public comment period and discussed the measure during the post-comment call. • The developers presented additional information to address the Committee’s previous questions and support the validity of the measure. • Committee members discussed the challenges of determining an appropriate population for this measure given the heterogeneous nature of cancer. Committee members wanted to include as many patients as possible but recognized the need to ensure the measure reflects readmissions due to quality of care. • Committee members also raised concerns about the lack of granularity on the adjustment for co-morbidity. • Ultimately, the Committee determined the measure met the validity subcriterion.
  
  
• Review for Feasibility: 3. Feasibility: H-19; M-2; L-0; I-0 (3a. Clinical data generated during care delivery; 3b. Electronic sources; 3c.Susceptibility to inaccuracies/ unintended consequences identified 3d. Data collection strategy can be implemented) Rationale: • This measure is calculated using administrative claims data from established data fields. Thus, the measure’s required data elements are routinely generated as part of the facilities billing process. • Committee members believed that the feasibility is high as all data are available through the administrative claims.
  
  
• Review for Usability: 4. Usability and Use: H-4; M-15; L-3; I-0 (Used and useful to the intended audiences for 4a. Accountability and Transparency; 4b. Improvement; and 4c. Benefits outweigh evidence of unintended consequences) Rationale: • The measure is publically reported by Vizient, Inc. with external benchmarking to multiple organizations. • The developer notes that the measure is also used in quality improvement applications at the City of Hope Comprehensive Care Center, University of Miami Sylvester Comprehensive Cancer Care, Seattle Cancer Care Alliance • The measure is used in the Annual Hospital Ratings for Colon and Lunch Cancer Surgery. • The measure is used in an ACO payment program at Moffitt Cancer Center with Florida Blue. • Committee members noted that the measure is current used in both QI and accountability applications at several health centers, and would be under consideration for possible future rulemaking as early as FY 2018.
  
  
• Review for Related and Competing Measures: 5. Related and Competing Measures • No related or competing measures noted.
  
  
• Endorsement Public Comments: 6. Public and Member Comment • Public commenters expressed support for measure 3188. Commenters noted that currently endorsed readmission measures do not include cancer patients and this measure would fill a critical measurement gap. Commenters recognized the need to improve cancer care quality and believe that use of this measure could help avoid unnecessary hospitalizations. • Commenters believed the measure is valid. Commenters expressed support for the statistical model of the measure, the specified exclusions, and the risk adjustment strategy.
  
  
• Endorsement Committee Recommendation: Standing Committee Recommendation for Endorsement: Y-15; N-4 Rationale 25 • The Standing Committee did not conduct a vote for Overall Suitability for Endorsement during the February 27, 2017 webinar because Consensus was Not Reached on the Validity criterion. The Standing Committee discussed and re-voted on the Validity criterion during the PostComment Call on May 16, 2017. The Standing Committee agreed the measure meets the Validity criterion, and then also then voted Yes on Overall Suitability for Endorsement.
  
  

Measure Specifications

• NQF Number (if applicable): 2614
  
  
• Description: The measure calculates the percentage of individuals discharged in a six-month time period from a SNF, within 100 days of admission, who are satisfied. This patient reported outcome measure is based on the CoreQ: Short Stay Discharge questionnaire that utilizes four items. The following are the four items: 1. In recommending this facility to your friends and family, how would you rate it overall? (Poor, Average, Good, Very Good, or Excellent) 2. Overall, how would you rate the staff? (Poor, Average, Good, Very Good, or Excellent) 3. How would you rate the care you receive? (Poor, Average, Good, Very Good, or Excellent) 4. How would you rate how well your discharge needs were met? (Poor, Average, Good, Very Good, or Excellent)
  
  
• Numerator: The numerator is the sum of the individuals in the facility that have an average satisfaction score of greater than or equal to 3 for the four questions on the CoreQ: Short Stay Discharge questionnaire that utilizes four items. The following are the four items: 1. In recommending this facility to your friends and family, how would you rate it overall? (Poor, Average, Good, Very Good, or Excellent) 2. Overall, how would you rate the staff? (Poor, Average, Good, Very Good, or Excellent) 3. How would you rate the care you receive? (Poor, Average, Good, Very Good, or Excellent) 4. How would you rate how well your discharge needs were met? (Poor, Average, Good, Very Good, or Excellent)
  
  
• Denominator: The denominator includes all of the patients that are admitted to the SNF, regardless of payor source, for post-acute care, that are discharged within 100 days; who receive the survey (e.g. people meeting exclusions do not receive a questionnaire) and who respond to the CoreQ: Short Stay Discharge questionnaire within two months of receiving the questionnaire.
  
  
• Exclusions: Exclusions made at the time of sample selection and the following: (1) Patients who died during their SNF stay; (2) Patients discharged to a hospital, another SNF, psychiatric facility, inpatient rehabilitation facility or long term care hospital; (3) Patients with court appointed legal guardian for all decisions; (4) Patients discharged on hospice; (5) Patients who left the nursing facility against medical advice (AMA); (6) Patients who have dementia impairing their ability to answer the questionnaire defined as having a BIMS score on the MDS 3.0 as 7 or lower. [Note: we understand that some SNCCs may not have information on cognitive function available to help with sample selection. In that case, we suggest administering the survey to all residents and assume that those with cognitive impairment will not complete the survey or have someone else complete on their behalf which in either case will exclude them from the analysis.] Additionally, once the survey is administered, the following exclusions are applied: (a) Patients who responded after the two-month response period; and (b) Patients whose responses were filled out by someone else. (Note this does not include cases where the resident solely had help such as reading the questions or writing down their responses.) Surveys returned as un-deliverable are also excluded from the denominator.
  
  
• HHS NQS Priority: Patient and Family Engagement, Communication and Care Coordination
  
  
• HHS Data Source: Administrative Clinical Data, Other
  
  
• Measure Type: Patient Reported Outcome
  
  
• Steward: Centers for Medicare & Medicaid Services
  
  
• Endorsement Status: Endorsed
  
  
• Changes to Endorsed Measure Specifications?: The MUC list indicates the measure has been modified from its endorsed version.
  
  
• Is the measure specified as an electronic clinical quality measure? No
  
  

Summary of Workgroup Deliberations

• Workgroup Recommendation: Support for Rulemaking
  
  
• Workgroup Rationale: MAP supported the CoreQ: Short Stay Discharge Measure for the Skilled Nursing Facility Quality Reporting Program. MAP recognized that this measure addressed a previously identified gap in patient satisfaction and could offer an indication of quality of care from the patient's perspective. MAP noted that the current SNF QRP program measure set does not include any patient-reported outcome measures, identified as a high-priority domain by both CMS and at previous meetings of the PAC-LTC MAP Workgroup. The measure was NQF-endorsed in 2017 by the Person and Family-Centered Care Standing Committee. However, MAP noted the potential burden of collecting patient-reported data and cautioned that the implementation of a new data collection requirement should be done with the least possible burden to facilities. MAP also requested that CMS and the NQF Person and Family-Centered Care Standing Committee pay special attention to the performance gap of this measure, to ensure it continues to determine meaningful differences in quality. MAP also reiterated that CMS should implement the measure in a way that allows as many patients to be included as possible. Finally, MAP also noted the need to continue to develop measures of patient experience.
  
  
• Public comments received: 1

Rationale for measure provided by HHS
Collecting satisfaction information from skilled nursing facility (SNF) patients is more important now than ever. We have seen a philosophical change in healthcare that now includes the patient and their preferences as an integral part of the system of care. The Institute of Medicine (IOM) endorses this change by putting the patient as central to the care system (IOM, 2001). For this philosophical change to person-centered care to succeed, we have to be able to measure patient satisfaction for these three reasons: (1) Measuring satisfaction is necessary to understand patient preferences. (2) Measuring and reporting satisfaction with care helps patients and their families choose and trust a health care facility. (3) Satisfaction information can help facilities improve the quality of care they provide. The implementation of person-centered care in SNFs has already begun, but there is still room for improvement. The Centers for Medicare and Medicaid Services (CMS) demonstrated interest in consumers’ perspective on quality of care by supporting the development of the Consumer Assessment of Healthcare Providers and Systems (CAHPS) survey for patients in nursing facilities (Sangl et al., 2007). Further supporting person-centered care and resident satisfaction are ongoing organizational change initiatives. These include: the Advancing Excellence in America’s Nursing Homes campaign (2006), which lists person-centered care as one of its goals; Action Pact, Inc., which provides workshops and consultations with nursing facilities on how to be more person-centered through their physical environment and organizational structure; and Eden Alternative, which uses education, consultation, and outreach to further person-centered care in nursing facilities. All of these initiatives have identified the measurement of resident satisfaction as an essential part in making, evaluating, and sustaining effective clinical and organizational changes that ultimately result in a person-centered philosophy of care. The importance of measuring resident satisfaction as part of quality improvement cannot be stressed enough. Quality improvement initiatives, such as total quality management (TQM) and continuous quality improvement (CQI), emphasize meeting or exceeding “customer” expectations. William Deming, one of the first proponents of quality improvement, noted that “one of the five hallmarks of a quality organization is knowing your customer’s needs and expectations and working to meet or exceed them” (Deming, 1986). Measuring resident satisfaction can help organizations identify deficiencies that other quality metrics may struggle to identify, such as communication between a patient and the provider. As part of the U.S. Department of Commerce renowned Baldrige Criteria for organizational excellence, applicants are assessed on their ability to describe the links between their mission, key customers, and strategic position. Applicants are also required to show evidence of successful improvements resulting from their performance improvement system. An essential component of this process is the measurement of customer, or resident, satisfaction (Shook & Chenoweth, 2012). The CoreQ: Short Stay Discharge questionnaire can strategically help nursing facilities achieve organizational excellence and provide high quality care by being a tool that targets a unique and growing patient population. Over the past several decades, care in nursing facilities has changed substantially. Statistics show that more than half of all elders cared for in nursing homes are now discharged home (approximately 1.6 million residents; CMS, 2009). Moreover, when satisfaction information from current residents (i.e., long stay residents) is compared with those of elders discharged home, substantial differences exist (Castle, 2007). This indicates that long stay and short stay residents are different populations with different needs in the nursing facilities. Moreover, residents are more likely to follow medical advice when they rate their care as satisfactory (Hall, Milburn, Roter, & Daltroy, 1998). Thus, the CoreQ: Short Stay Discharge questionnaire measure is needed to improve the care for short stay SNF patients. Furthermore, improving the care for short stay nursing home patients is tenable. A review of the literature on satisfaction surveys in nursing facilities (Castle, 2007) concluded that substantial improvements in resident satisfaction could be made in many nursing facilities by improving care (i.e., changing either structural or process aspects of care). This was based on satisfaction scores ranging from 60 to 80% on average. It is worth noting, few other generalizations could be made because existing instruments used to collect satisfaction information are not standardized. Thus, bench-marking scores and comparison scores (i.e., best in class) were difficult to establish. The CoreQ: Short Stay Discharge measure has considerable relevance in establishing benchmarking scores and comparison scores. This measure’s relevance is furthered by recent federal legislative actions. The Affordable Care Act of 2010 requires the Secretary of Health and Human Services (HHS) to implement a Quality Assurance & Performance Improvement Program (QAPI) within nursing facilities. This means all nursing facilities have increased accountability for continuous quality improvement efforts. In CMS’s “QAPI at a Glance” document there are references to customer-satisfaction surveys and organizations utilizing them to identify opportunities for improvement. Lastly, the new “Medicare and Medicaid Programs; Reform of Requirements for Long-Term Care Facilities” proposed rule includes language purporting the importance of satisfaction and measuring satisfaction. CMS states “CMS is committed to strengthening and modernizing the nation’s health care system to provide access to high quality care and improved health at lower cost. This includes improving the patient experience of care, both quality and satisfaction, improving the health of populations, and reducing the per capita cost of health care.” There are also other references in proposed rules speaking to improving resident satisfaction and increasing person-centered care (Medicare and Medicaid Programs; Reform of Requirements for Long-Term Care Facilities, 2015). The CoreQ: Short Stay Discharge measure has considerable applicability to both of these initiatives. References: Castle, N.G. (2007). A literature review of satisfaction instruments used in long-term care settings. Journal of Aging and Social Policy, 19(2), 9-42. CMS (2009). Skilled Nursing Facilities Non Swing Bed - Medicare National Summary. http://www.cms.hhs.gov/MedicareFeeforSvcPartsAB/Downloads/NationalSum2007.pdf CMS, University of Minnesota, and Stratis Health. QAPI at a Glance: A step by step guide to implementing quality assurance and performance improvement (QAPI) in your nursing home. https://www.cms.gov/Medicare/Provider-Enrollment-and-Certification/QAPI/Downloads/QAPIAtaGlance.pdf. Deming, W.E. (1986). Out of the crisis. Cambridge, MA. Massachusetts Institute of Technology, Center for Advanced Engineering Study. Hall J, Milburn M, Roter D, Daltroy L. Why are sicker patients less satisfied with their medical care? Tests of two explanatory models. Health Psychol. 1998;17(1):70-75. Institute of Medicine (2001). Improving the Quality of Long Term Care, National Academy Press, Washington, D.C., 2001. Medicare and Medicaid Programs; Reform of Requirements for Long-Term Care Facilities; Department of Health and Human Services. 80 Fed. Reg. 136 (July 16, 2015) (to be codified at 42 CFR Parts 405, 431, 447, et al.). MedPAC. (2015). Report to the Congress: Medicare Payment Policy. http://www.medpac.gov/docs/default-source/reports/mar2015_entirereport_revised.pdf. Sangl, J., Bernard, S., Buchanan, J., Keller, S., Mitchell, N., Castle, N.G., Cosenza, C., Brown, J., Sekscenski, E., and Larwood, D. (2007). The development of a CAHPS instrument for nursing home residents. Journal of Aging and Social Policy, 19(2), 63-82. Shook, J., & Chenoweth, J. (2012, October). 100 Top Hospitals CEO Insights: Adoption Rates of Select Baldrige Award Practices and Processes. Truven Health Analytics. http://www.nist.gov/baldrige/upload/100-Top-Hosp-CEO-Insights-RB-final.pdf.

Summary of NQF Endorsement Review

• Year of Most Recent Endorsement Review: 2017
  
  
• Project for Most Recent Endorsement Review: Person and Family Centered Care Project 2016-2017
  
  
• Review for Importance: 1a. Evidence, 1b. Performance Gap)1a. Evidence: Y-17; N-1; 1b. Performance Gap: H-7; M-10; L-1; I-0Rationale: • Committee members noted that this is a very significant measure for those who go into a nursing home or a SNF who will not stay indefinitely or for a long period of time. Measuring patient satisfaction and the rate of discharges back into the community is very important to measurement as including the patient and their preferences is becoming an integral part of healthcare’s changing landscape. Additionally, measuring and reporting satisfaction with carehelps patients and their families choose and trust a healthcare facility and can help facilities improve the quality of the care they provide. • One committee member had a question about the scale being used for this measure and felt that the choice of the response scale (poor, average, good, very good, and excellent) seemed heavily weighted towards positive responses. The developer explained that they did focus groups and cognitive testing of different response scales from ten points down to four point Likert scales and found that no matter how they captured responses, they had different satisfaction scores but the relative ranking remained the same. • Overall, committee members liked that there was a conceptual framework at the beginning of the measure submission form that linked the measure with information on additional improvement programs, organizational change initiatives, and policies that are going on both atthe federal level and the facility level.
  
  
• Review for Scientific Acceptability: 2a. Reliability: H-6; M-8; L-4; I-0 2b. Validity: H-6; M-9; L-3; I-0 Rationale: • One committee member felt that the exclusions may limit the generalizability to a small proportion of facility nursing home patients. • There was additional concern around the consistency of implementation across facilities and the possibility that scores could be compromised by the low response rate. 27 • Committee members also questioned the test/retest reliability at the patient level and sample size. The developer explained that the data elements were tested using a test-retest methodology: the survey was sent out and responses received from 853 patients; 100 were resurveyed one month later. The developer responded to these concerns by saying that while morbidity does occur, and may affect the data, there is an emphasis on making sure that both the voice of the patient and the voice of the family are heard. • There was also discussion around cognitive impairment and the effect this has on the survey’s overall responses. The developer agreed that cognitive impairment does have an effect in this setting and that by having everyone use the BIMs score, which is used to get a snapshot of how well someone is functioning cognitively at a given moment, allows for a more consistent approach across all nursing home residents. A standardized approach helps reduce the incidence of gaming. • One committee member had a question on the methodology used to reduce the number of items in the tool and how they got from 22 to 4 items without losing some precision. The developer responded that the process was extremely iterative and was done hundreds of times. The purpose of this was to try and get to the items that were capturing the most satisfaction information that did not overlap with other items and if two items correlated very highly, it made sense to drop one of them. • All members agreed with the decision not to risk adjust as it is inappropriate to control out differences based on sociodemographic factors. • Cognitive testing was done with family members, residents, and with short stay residents. The developers collected more than 100 responses from each population at facilities in Pittsburgh. This testing was conducted by reading questions and having the testing groups respond back based on what they thought was being asked and if they felt it could be asked differently. The committee indicated providing the results of this testing, although supplemental, would have been useful information.
  
  
• Review for Feasibility: 3. Feasibility: H-5; M-13; L-0; I-0(3a. Clinical data generated during care delivery; 3b. Electronic sources; 3c. Susceptibility to inaccuracies/unintended consequences identified 3d. Data collection strategy can be implemented)Rationale:• The committee agreed that this tool is timely as there is currently no required experience ofcare reporting or measurement in the SNF population.• Members appreciated that this tool is brief especially since the staffing in this area tends to bevery sparse.
  
  
• Review for Usability: 4. Usability and Use: H-5; M-11; L-2; I-0(Used and useful to the intended audiences for 4a. Accountability and Transparency; 4b. Improvement;and 4c. Benefits outweigh evidence of unintended consequences)Rationale:• The committee did not have any concerns or questions about the use and usability.285. Related and Competing Measures• This measure was identified as related with #2615: CoreQ: Long-Stay Resident Measure and#2616: CoreQ: Long-Stay Family Measure, submitted by the same developer.
  
  
• Review for Related and Competing Measures: 5. Related and Competing Measures• No related or competing measures noted.
  
  
• Endorsement Public Comments: 6. Public and Member Comment• No comments were received on this measure.
  
  
• Endorsement Committee Recommendation: 7. Consensus Standards Approval Committee (CSAC) Review (October 18, 2016): Y-16; N-0Decision: Approved for endorsement
  
  

Program Index

• Merit-Based Incentive Payment System 
  
  
• Medicare Shared Savings Program 
  
  
• Ambulatory Surgical Center Quality Reporting Program 
  
  
• End-Stage Renal Disease Quality Incentive Program 
  
  
• Hospital Acquired Condition Reduction Program 
  
  
• Hospital Inpatient Quality Reporting and EHR Incentive Program 
  
  
• Hospital Outpatient Quality Reporting Program 
  
  
• Hospital Readmissions Reduction Program 
  
  
• Hospital Value-Based Purchasing Program 
  
  
• Inpatient Psychiatric Facility Quality Reporting Program 
  
  
• Prospective Payment System-Exempt Cancer Hospital Quality Reporting Program 
  
  
• Inpatient Rehabilitation Facility Quality Reporting Program 
  
  
• Skilled Nursing Facility Quality Reporting Program 
  
  
• Home Health Quality Reporting Program 
  
  
• Long-Term Care Hospital Quality Reporting Program 
  
  
• Hospice Quality Reporting Program 
  
  

Full Program Summaries

Program History and Structure: The Merit-Based Incentive Payment System (MIPS) is established by H.R. 2 Medicare Access and CHIP Reauthorization Act of 2015 (MACRA), which repeals the Medicare sustainable growth rate (SGR) and improves Medicare payment for physician services. The MACRA consolidates the current programs of the Physician Quality Reporting System (PQRS), The Value-Based Modifier (VM), and the Electronic Health Records (EHR) Incentive Program into one program (MIPS) that streamlines and improves on the three distinct incentive programs. MIPS will apply to doctors of medicine or osteopathy, doctors of dental surgery or dental medicine, doctors of podiatric medicine, doctors of optometry, chiropractors, physician assistants, nurse practitioners, clinical nurse specialists, and certified registered nurse anesthetists beginning in the 2019 payment year. Other professionals paid under the physician fee schedule may be included in the MIPS beginning in the 2021 payment year, provided there are viable performance metrics available. Positive and negative adjustments will be applied to items and services furnished beginning January 1, 2019 based on providers meeting a performance threshold for four performance categories: quality, resource use, clinical practice improvement activities, and meaningful use of certified EHR technology. Adjustments will be capped at 4 percent in 2019; 5 percent in 2020; 7 percent in 2021; and 9 percent in 2022 and future years.

High Priority Domains for Future Measure Consideration:

CMS identified the following five domains as high-priority for future measure consideration:

1. Person and caregiver-centered Experience and Outcomes: This means that the measure should address the experience of each person and their family; and the extent to which they are engaged as partners in their care. a. CMS wants to specifically focus on patient reported outcome measures (PROMs). Person or family-reported experiences of being engaged as active members of the health care team and in collaborative partnerships with providers and provider organizations.

2. Communication and Care Coordination: This means that the measure must address the promotion of effective communication and coordination of care; and coordination of care and treatment with other providers.

3. Efficiency/Cost Reduction: This means that the measure must address the affordability of health care including unnecessary health services, inefficiencies in health care delivery, high prices, or fraud. Measures should cause change in efficiency and reward value over volume.

4. Patient Safety: This means that the measure must address either an explicit structure or process intended to make care safer, or the outcome of the presence or absence of such a structure or process; and harm caused in the delivery of care. This means that the structure, process or outcome described in “a” must occur as a part of or as a result of the delivery of CMS Program Priorities and Needs: April 2017 17 care.

5. Appropriate Use: CMS wants to specifically focus on appropriate use measures. This means that the measure must address appropriate use of services, including measures of over use.

Current Measures: NQF staff have compiled the program's measures in a spreadsheet organized according to concepts.

Program History and Structure: Section 3022 of the Affordable Care Act (ACA) requires the Centers for Medicare & Medicaid Services (CMS) to establish a Shared Savings Program that promotes accountability for a patient population, coordinates items and services under Medicare Parts A and B, and encourages investment in infrastructure and redesigned care processes for high-quality and efficient service delivery. The Medicare Shared Savings Program (Shared Savings Program) was designed to facilitate coordination and cooperation among providers to improve the quality of care for Medicare Fee-For-Service (FFS) beneficiaries and reduce the rate of growth in health care costs. Eligible providers, hospitals, and suppliers may voluntarily participate in the Shared Savings Program by creating or participating in an Accountable Care Organization (ACO). If ACOs meet program requirements and the ACO quality performance standard, they are eligible to share in savings, if earned. There are three shared savings options: 1) one- sided risk model (sharing of savings only for the first two years, and sharing of savings and losses in the third year), 2) two-sided risk model (sharing of savings and losses for all three years), and 3) two-sided risk model (sharing of savings and losses for all three years) with prospective assignment

Current Measures: NQF staff have compiled the program's measures in a spreadsheet organized according to concepts.

Program History and Structure: The Ambulatory Surgical Center Quality Reporting Program (ASCQR) was established under the authority provided by Section 109(b) of the Medicare Improvements and Extension Act of 2006, Division B, Title I of the Tax Relief and Health Care Act (TRHCA) of 2006. The statute provides the authority for requiring ASCs paid under the ASC fee schedule (ASCFS) to report on process, structure, outcomes, patient experience of care, efficiency, and costs of care measures. ASCs receive a 2.0 percentage point payment penalty to their ASCFS annual payment update for not meeting program requirements. CMS implemented this program so that payment determinations were effective beginning with the Calendar Year (CY) 2014 payment update.

High Priority Domains for Future Measure Consideration:

High Priority Domains for Future Measure Consideration:

CMS identified the following categories as high-priority for future measure consideration:

1. Making Care Safer a. Measures of infection rates
2. Person and Family Engagement
   1. Measures that improve experience of care for patients, caregivers, and families.
   2. Measures to promote patient self-management.
3. Best Practice of Healthy Living
   1. Measures to increase appropriate use of screening and prevention services.
   2. Measures which will improve the quality of care for patients with multiple chronic conditions.
   3. Measures to improve behavioral health access and quality of care.
4. Effective Prevention and Treatment a. Surgical outcome measures
5. Communication/Care Coordination
   1. Measures to embed best practice to manage transitions across practice settings.
   2. Measures to enable effective health care system navigation.
   3. To reduce unexpected hospital/emergency visits and admissions

Measure Requirements:

CMS applies criteria for measures that may be considered for potential adoption in the ASCQR. At a minimum, the following requirements will be considered in selecting measures for ASCQR implementation:

1. Measure must adhere to CMS statutory requirements.
   1. Measures are required to reflect consensus among affected parties, and to the extent feasible, be endorsed by the national consensus entity with a contract under Section 1890(a) of the Social Security Act
   2. The Secretary may select a measure in an area or topic in which a feasible and practical measure has not been endorsed, by the entity with a contract under Section 1890(a) of the Social Security Act, as long as endorsed measures have been given due consideration
2. Measure must address a NQS priority/CMS strategy goal, with preference for measures addressing the high priority domains for future measure consideration.
3. Measure must address an important condition/topic for which there is analytic evidence that a performance gap exists and that measure implementation can lead to improvement in desired outcomes, costs, or resource utilization.
4. Measure must be field tested for the ASC clinical setting.
5. Measure that is clinically useful.
6. Reporting of measure limits data collection and submission burden since many ASCs are small facilities with limited staffing.
7. Measure must supply sufficient case numbers for differentiation of ASC performance.
8. Measure must promote alignment across HHS and CMS programs.
9. Measure steward will provide CMS with technical assistance and clarifications on the measure as needed.

Current Measures: NQF staff have compiled the program's measures in a presentation organized according to concepts.

Program History and Structure: For more than 30 years, monitoring the quality of care provided to end-stage renal disease (ESRD) patients by dialysis facilities has been an important component of the Medicare ESRD payment system. The ESRD quality incentive program (QIP) is the most recent step in fostering improved patient outcomes by establishing incentives for dialysis facilities to meet or exceed performance standards established by CMS. The ESRD QIP is authorized by section 1881(h) of the Social Security Act, which was added by section 153(c) of Medicare Improvements for Patients and Providers (MIPPA) Act (the Act). CMS established the ESRD QIP for Payment Year (PY) 2012, the initial year of the program in which payment reductions were applied, in two rules published in the Federal Register on August 12, 2010, and January 5, 2011 (75 FR 49030 and 76 FR 628, respectively). Subsequently, CMS published rules in the Federal Register detailing the QIP requirements for PY 2013 through FY 2016. Most recently, CMS published a rule on November 6, 2014 in the Federal Register (79 FR 66119), providing the ESRD QIP requirements for PY2017 and PY 2018, with the intention of providing an additional year between finalization of the rule and implementation in future rules. Section 1881(h) of the Act requires the Secretary to establish an ESRD QIP by (i) selecting measures; (ii) establishing the performance standards that apply to the individual measures; (iii) specifying a performance period with respect to a year; (iv) developing a methodology for assessing the total performance of each facility based on the performance standards with respect to the measures for a performance period; and (v) applying an appropriate payment reduction to facilities that do not meet or exceed the established Total Performance Score (TPS).

High Priority Domains for Future Measure Consideration:

CMS identified the following 3 domains as high-priority for future measure consideration:

1. Care Coordination: ESRD patients constitute a vulnerable population that depends on a large quantity and variety medication and frequent utilization of multiple providers, suggesting medication reconciliation is a critical issue. Dialysis facilities also play a substantial role in preparing dialysis patients for kidney transplants, and coordination of dialysis-related services among transient patients has consequences for a non-trivial proportion of the ESRD dialysis population.
2. Safety: ESRD patients are frequently immune-compromised, and experience high rates of blood stream infections, vascular access-related infections, and mortality. Additionally, some medications provided to treat ESRD patients may cause harmful side effects such as heart disease and a dynamic bone disease. Recently, oral-only medications were excluded from the bundle payment, increasing need for quality measures that protect against overutilization of oral-only medications.
3. Patient- and Caregiver-Centered Experience of Care: Sustaining and recovering patient quality of life was among the original goals of the Medicare ESRD QIP. This includes such issues as physical function, independence, and cognition. Quality of Life measures should also consider the life goals of the particular patient where feasible, to the point of including Patient-Reported Outcomes.
4. Access to Transplantation: Obtaining a transplant is an extended process for dialysis patients, including education, referral, waitlisting, transplantation, and follow-up care. The care and information available from dialysis facilities are integral to the process. Complicating the issue of attribution are the role of transplant facilities in setting criteria and making decisions about transplant candidates and the limited availability of donor organs. Measures for the ESRD QIP must balance the role of the facility and other providers with the need to make transplants accessible to as many candidate recipients as possible.

Measure Requirements:

1. Measures for anemia management reflecting FDA labeling, as well as measures for dialysis adequacy.
2. Measure(s) of patient satisfaction, to the extent feasible.
3. Measures of iron management, bone mineral metabolism, and vascular access, to the extent feasible.
4. Measures should be NQF endorsed, save where due consideration is given to endorsed measures of the same specified area or medical topic.
5. Must include measures considering unique treatment needs of children and young adults.
6. May incorporate Medicare claims and/or CROWNWeb data, alternative data sources will be considered dependent upon available infrastructure.

Current Measures: NQF staff have compiled the program's measures in a presentation organized according to concepts.

Program History and Structure: Section 3008 of the Patient Protection and Affordable Care Act of 2010 (ACA) established the HospitalAcquired Condition Reduction Program (HACRP). Created under Section 1886(p) of the Social Security Act (the Act), the HACRP provides an incentive for hospitals to reduce the number of HACs. Effective Fiscal Year (FY) 2014 and beyond, the HACRP requires the Secretary to make payment adjustments to applicable hospitals that rank in the top quartile of all subsection (d) hospitals relative to a national average of HACs acquired during an applicable hospital stay. HACs include a condition identified in subsection 1886(d)(4)(D)(iv) of the Act and any other condition determined appropriate by the Secretary. Section 1886(p)(6)(C) of the Act requires the HAC information be posted on the Hospital Compare website. CMS finalized in the FY 2014 IPPS/LTCH PPS final rule that hospitals will be scored using a Total HAC Score based on measures categorized into two (2) domains of care, each with a different set of measures. Domain 1 consists of Agency for Healthcare Research and Quality (AHRQ) Patient Safety Indicators (PSI), and Domain 2 consists of Hospital Associated Infections (HAI) as collected by the Centers for Disease Control and Prevention (CDC) National Healthcare Safety Network (NHSN). Both domains of the HAC Reduction Program are categorized under the National Quality Strategy (NQS) priority of “Making Care Safer.” The Total HAC Score is the sum of the two weighted domain scores, with Domain 1 weighted at 15% and Domain 2 weighted at 85%.

High Priority Domains for Future Measure Consideration:

For FY 2017 federal rulemaking, CMS may propose the adoption, removal, and/or suspensionof measures for fiscal years 2018 and beyond of the HACRP. CMS identified the following topics as areas within the NQS priority of “Making Care Safer” for future measure consideration:

Making Care Safer:

1. Measures that address adverse drug events during the inpatient stay
2. Measures that address ventilator-associated events
3. Additional surgical site infection locations that are not already covered within an existing measure in the program
4. Outcome risk-adjusted measures that capture outcomes from hospital-acquired conditions and are risk-adjusted to account for patient and/or facility differences (e.g., multiple comorbidities, patient care location)
5. Measures that address diagnostic errors such as harm from receiving improper tests or treatment, harm from not receiving proper tests or treatment, harm from failure to diagnose, or harm from improper diagnosis
6. Measure that address causes of hospital harm such as an all-cause harm measure or a measure that encompasses multiple harms

Measure Requirements:

CMS applies criteria for measures that may be considered for potential adoption in the HACRP. At a minimum, the following requirements must be met for consideration in the HACRP:

• Measures must be identified as a HAC under Section 1886(d)(4)(D) or be a condition identified by the Secretary.
• Measures must address high cost or high volume conditions.
• Measures must be easily preventable by using evidence-based guidelines.
• Measures must not require additional system infrastructure for date submission and collection.
• Measures must be risk adjusted.
• Measure steward will provide CMS with technical assistance and clarifications on the measure as needed.

Current Measures: NQF staff have compiled the program's measures in a presentation organized according to concepts.

Program History and Structure: The Hospital Inpatient Quality Reporting (HIQR) Program was established by Section 501(b) of the Medicare Prescription Drug, Improvement, and Modernization Act of 2003 and expanded by the Deficit Reduction Act of 2005. The program requires hospitals paid under the Inpatient Prospective Payment System (IPPS) to report on process, structure, outcomes, patient perspectives on care, efficiency, and costs of care measures. Hospitals that fail to meet the requirements of the HIQR will result in a reduction of one-fourth to their fiscal year IPPS annual payment update (the annual payment update includes inflation in costs of goods and services used by hospitals in treating Medicare patients). Hospitals that choose to not participate in the program receive a reduction by that same amount. Hospitals not included in the HIQR, such as critical access hospitals and hospitals located in Puerto Rico and the U.S. Territories, are permitted to participate in voluntary quality reporting. Performance of quality measures are publicly reported on the CMS Hospital Compare website. The American Recovery and Reinvestment Act of 2009 amended Titles XVIII and XIX of the Social Security Act to authorize incentive payments to eligible hospitals (EHs) and critical access hospitals (CAHs) that participate in the EHR Incentive Program, to promote the adoption and meaningful use of certified electronic health record (EHR) technology (CEHRT). EHs and CAHs are required to report on electronically-specified clinical quality measures (eCQMs) using CEHRT in order to qualify for incentive payments under the Medicare and Medicaid EHR Incentive Programs. All EHR Incentive Program requirements related to eCQM reporting will be addressed in IPPS rulemaking including, but not limited to, new program requirements, reporting requirements, reporting and submission periods, reporting methods, alignment efforts between the HIQR and the Medicare EHR Incentive Program for EHs and CAHs, and information regarding the eCQMs.

High Priority Domains for Future Measure Consideration:

CMS identified the following categories as high-priority for future measure consideration:

1. Patient and Family Engagement:
   1. Measures that foster the engagement of patients and families as partners in their care.
2. Best Practices of Healthy Living:
   1. Measures that promote best practices to enable healthy living.
3. Making Care Affordable:
   1. Measures that effectuate changes in efficiency and reward value over volume.

Measure Requirements:

CMS applies criteria for measures that may be considered for potential adoption in the HIQR program. At a minimum, the following criteria will be considered in selecting measures for HIQR program implementation:

1. Measure must adhere to CMS statutory requirements.
   1. Measures are required to reflect consensus among affected parties, and to the extent feasible, be endorsed by the national consensus entity with a contract underSection 1890(a) of the Social Security Act; currently the National Quality Forum(NQF)
   2. The Secretary may select a measure in an area or topic in which a feasible and practical measure has not been endorsed, by the entity with a contract under Section 1890(a)of the Social Security Act, as long as endorsed measures have been given due consideration
2. Measure must be claims-based or an electronically specified clinical quality measure(eCQM).
   1. A Measure Authoring Tool (MAT) number must be provided for all eCQMs, createdin the HQMF format
   2. eCQMs must undergo reliability and validity testing including review of the logic and value sets by the CMS partners, including, but not limited to, MITRE and the National Library of Medicine
   3. eCQMs must have successfully passed feasibility testing
3. Measure may not require reporting to a proprietary registry.
4. Measure must address an important condition/topic for which there is analytic evidence thata performance gap exists and that measure implementation can lead to improvement indesired outcomes, costs, or resource utilization.
5. Measure must be fully developed, tested, and validated in an acute inpatient setting.
6. Measure must address a NQS priority/CMS strategy goal, with preference for measures addressing the high priority domains and/or measurement gaps for future measure consideration.
7. Measure must promote alignment across HHS and CMS programs.
8. Measure steward will provide CMS with technical assistance and clarifications on the measure as needed.

Current Measures: NQF staff have compiled the program's measures in a presentation organized according to concepts.

Program History and Structure: The Hospital Outpatient Quality Reporting (OQR) Program was established by Section 109 of the Tax Relief and Health Care Act (TRHCA) of 2006. The program requires subsection (d) hospitals providing outpatient services paid under the Outpatient Prospective Payment System (OPPS) to report on process, structure, outcomes, efficiency, costs of care, and patient experience of care. Hospitals receive a 2.0 percentage point reduction of their annual payment update (APU) under the Outpatient Prospective Payment System (OPPS) for non-participation in the program. Performance on quality measures is publicly reported on the CMS Hospital Compare website.

High Priority Domains for Future Measure Consideration: CMS identified the following categories as high-priority for future measure consideration:

1. Making Care Safer:
1. Measures that address processes and outcomes designed to reduce risk in the delivery of health care, e.g., emergency department overcrowding and wait times.
2. Best Practices of Healthy Living:
1. Measures that focus on primary prevention of disease or general screening for early detection of disease unrelated to a current or prior condition.
3. Patient and Family Engagement:
1. Measures that address engaging both the person and their family in their care.
2. Measures that address cultural sensitivity, patient decision-making support or care that reflects patient preferences.
4. Communication/Care Coordination:
1. Measures to embed best practices to manage transitions across practice settings.
2. Measures to enable effective health care system navigation.
3. Measures to reduce unexpected hospital/emergency visits and admissions.

Measure Requirements: CMS applies criteria for measures that may be considered for potential adoption in the HOQR program. At a minimum, the following criteria will be considered in selecting measures for HOQR program implementation:

1. Measure must adhere to CMS statutory requirements.
1. Measures are required to reflect consensus among affected parties, and to the extent feasible, be endorsed by the national consensus entity with a contract under Section 1890(a) of the Social Security Act
2. The Secretary may select a measure in an area or topic in which a feasible and practical measure has not been endorsed, by the entity with a contract under Section 1890(a) of the Social Security Act, as long as endorsed measures have been given due consideration
2. Measure must address a NQS priority/CMS strategy goal, with preference for measures addressing the high priority domains for future measure consideration.
3. Measure must address an important condition/topic for which there is analytic evidence that a performance gap exists and that measure implementation can lead to improvement in desired outcomes, costs, or resource utilization.
4. Measure must be fully developed, tested, and validated in the hospital outpatient setting.
5. Measure must promote alignment across HHS and CMS programs.
6. Feasibility of Implementation: An evaluation of feasibility is based on factors including, but not limited to
1. The level of burden associated with validating measure data, both for CMS and for the end user.
2. Whether the identified CMS system for data collection is prepared to accommodate the proposed measure(s) and timeline for collection.
3. The availability and practicability of measure specifications, e.g., measure specifications in the public domain.
4. The level of burden the data collection system or methodology poses for an end user.
7. Measure steward will provide CMS with technical assistance and clarifications on the measure as needed.

Current Measures: NQF staff have compiled the program's measures in a presentation organized according to concepts.

Program History and Structure: Section 3025 of the Patient Protection and Affordable Care Act of 2010 (ACA) established the Hospital Readmissions Reduction Program (HRRP). Codified under Section 1886(q) of the Social Security Act (the Act), the HRRP provides an incentive for hospitals to reduce the number of excess readmissions that occur in their settings. Effective Fiscal Year (FY) 2012 and beyond, the HRRP requires the Secretaryto establish readmission measures for applicable conditions and to calculate an excess readmissionratio for each applicable condition, which will be used to determine a payment adjustment to those hospitals with excess readmissions. A readmission is defined as an admission to an acute care hospital within 30 days of a discharge from the same or another acute care hospital. A hospital’s excess readmission ratio measures a hospital’s readmission performance compared to the national average for the hospital’s set of patients with that applicable condition. Applicable conditions in the FY 2017 HRRP program currentlyinclude measures for acute myocardial infarction, heart failure, pneumonia, chronic obstructivepulmonary disease, elective total knee and total hip arthroplasty, and coronaryartery bypass graft surgery. Planned readmissions are excluded from the excess readmission calculation.

High Priority Domains for Future Measure Consideration:

For FY 2017 federal rulemaking, CMS may propose the adoption, removal, refinement, and or suspension of measures for fiscal year 2018 and subsequent years of the HRRP. CMS continuesto emphasize the importance of the NQS priority of “Communication/Care Coordination” for this program.

• Care Coordination
  • Measures that address high impact conditions identified by the Medicare Payment Advisory Commission or the Agency for Healthcare Research and Quality (AHRQ) Healthcare Cost and Utilization Project (HCUP)reports.

Measure Requirements:

CMS applies criteria for measures that may be considered for potential adoption in the HRRP. At a minimum, the following criteria and requirements must be met for consideration in the HRRP:

• CMS is statutorily required to select measures for applicable conditions, which are defined as conditions or procedures selected by the Secretary in which readmissions are high volumeor high expenditure.
• Measures selected must be endorsed by the consensus-based entity with a contract under Section 1890 of the Act. However, the Secretary can select measures which are feasibleand practical in a specified area or medical topic determined to be appropriate by the Secretary, that have not been endorsed by the entity with a contract under Section 1890 of the Act, as longas endorsed measures have been given due consideration.
• Measure methodology must be consistent with other readmissions measures currently implemented or proposed in the HRRP.
• Measure steward will provide CMS with technical assistance and clarifications on the measure as needed.

Current Measures: NQF staff have compiled the program's measures in a presentation organized according to concepts.

Program History and Structure: The Hospital Value-Based Purchasing (HVBP) Program was established by Section 3001(a) of the Affordable Care Act, under which value-based incentive payments are made each fiscal year to hospitals meeting performance standards established for a performance period for such fiscal year. The Secretary shall select measures, other than measures of readmissions, for purposes of the Program. In addition, measures of five conditions (acute myocardial infarction, pneumonia, heart failure, surgeries, and healthcare-associated infections), the Hospital Consumer Assessment of Healthcare Providers and Systems (HCAHPS) survey, and efficiency measures must be included. Measures are eligible for adoption in the HVBP Program based on the statutory requirements, including specification under the Hospital Inpatient Quality Reporting (HIQR) Program and posting dates on the Hospital Compare website.

High Priority Domains for Future Measure Consideration:

CMS identified the following categories as high-priority for future measure consideration:

1. Patient and Family Engagement:
   1. Measures that foster the engagement of patients and families as partners in their care.
2. Making Care Affordable:
   1. Measures that effectuate changes in efficiency and reward value over volume.

Measure Requirements:

CMS applies criteria for measures that may be considered for potential adoption in the HVBP Program. At a minimum, the following criteria will be considered in selecting measures for HVBP Program implementation:

1. Measure must adhere to CMS statutory requirements, including specification under the Hospital IQR Program and posting dates on the Hospital Compare website.
   • Measures are required to reflect consensus among affected parties, and to the extent feasible, be endorsed by the national consensus entity with a contract under Section 1890(a) of the Social Security Act; currently the National Quality Forum (NQF)
   • The Secretary may select a measure in an area or topic in which a feasible and practical measure has not been endorsed, by the entity with a contract under Section 1890(a) of the Social Security Act, as long as endorsed measures have been given due consideration
2. Measure may not require reporting to a proprietary registry.
3. Measure must address an important condition/topic for which there is analytic evidence that a performance gap exists and that measure implementation can lead to improvement in desired outcomes, costs, or resource utilization.
4. Measure must be fully developed, tested, and validated in the acute inpatient setting.
5. Measure must address a NQS priority/CMS strategy goal, with preference for measures addressing the high priority domains and/or measurement gaps for future measure consideration.
6. Measure must promote alignment across HHS and CMS programs.
7. Measure steward will provide CMS with technical assistance and clarifications on the measure as needed.

Current Measures: NQF staff have compiled the program's measures in a presentation organized according to concepts.

Program History and Structure: The Inpatient Psychiatric Facility Quality Reporting (IPFQR) Program was established by Section 1886(s)(4) of the Social Security Act, as added by sections 3401(f)(4) and 10322(a) of the Patient Protection and Affordable Care Act (the Affordable Care Act). Under current regulations, the program requires participating inpatient psychiatric facilities (IPFs) to report on 16 quality measures or face a 2.0 percentage point reduction to their annual update. Reporting on these measures apply to payment determinations for Fiscal Year (FY) 2017 and beyond.

High Priority Domains for Future Measure Consideration:

CMS identified the following categories as high-priority for future measure consideration:

1. Patient and Family Engagement
   1. Patient experience of care
2. Effective Prevention and Treatment
   1. Inpatient psychiatric treatment and quality of care of geriatric patients and other adults, adolescents, and children
   2. Quality of prescribing for antipsychotics and antidepressants
3. Best Practices of Healthy Living
   1. Screening and treatment for non-psychiatric comorbid conditions for which patients with mental or substance use disorders are at higher risk
   2. Access to care
4. Making Care Affordable
   1. Measures which effectuate changes in efficiency and that reward value over volume.

Measure Requirements: CMS applies criteria for measures that may be considered for potential adoption in the IPFQR. At a minimum, the following criteria will be considered in selecting measures for IPFQR implementation: Measure must adhere to CMS statutory requirements. Measures are required to reflect consensus among affected parties, and to the extent feasible, be endorsed by the national consensus entity with a contract under Section 1890(a) of the Social Security Act The Secretary may select a measure in an area or topic in which a feasible and practical measure has not been endorsed, by the entity with a contract under Section 1890(a) of the Social Security Act, as long as endorsed measures have been given due consideration Measure must address an important condition/topic for which there is analytic evidence that a performance gap exists and that measure implementation can lead to improvement in desired outcomes, costs, or resource utilization. The measure assesses meaningful performance differences between facilities. The measure addresses an aspect of care affecting a significant proportion of IPF patients. Measure must be fully developed, tested, and validated in the acute inpatient setting. Measure must address a NQS priority/CMS strategy goal, with preference for measures addressing the high priority domains for future measure consideration. Measure must promote alignment across HHS and CMS programs. Measure steward will provide CMS with technical assistance and clarifications on the measure as needed.

Current Measures: NQF staff have compiled the program's measures in a presentation organized according to concepts.

Program History and Structure: Section 3005 of the Affordable Care Act added new subsections (a)(1)(W) and (k) to section 1866 of the Social Security Act (the Act). Section 1866(k) of the Act establishes a quality reporting programfor hospitals described in section 1886(d)(1)(B)(v) of the Act (referred to as a “PPS-Exempt Cancer Hospital” or PCHQR). Section 1866(k)(1) of the Act states that, for FY 2014 and each subsequent fiscal year, a PCH shall submit data to the Secretary in accordance with section 1866(k)(2) of the Act with respect to such a fiscal year. In FY 2014 and each subsequent fiscal year, each hospital described in section 1886(d)(1)(B)(v) of the Act shall submit data to the Secretary on quality measures (QMs) specified under section 1866(k)(3) of the Act in a form and manner, and at a time, specified by the Secretary. The program requires PCHs to submit data for selected QMs to CMS. PCHQR is a voluntaryquality reporting program, in which data will be publicly reported on a CMS website. In the FY 2012 IPPSrule, five NQF endorsed measures were adopted and finalized for the FY 2014 reporting period, which was the first year of the PCHQR. In the FY 2013 IPPS rule, one additional measure wasadopted. Twelve new measures were adopted in the FY 2014 IPPS rule and one measure was adopted in theFY 2015 IPPS rule. Data collection for the FY 2017 and FY 2018 reporting periods is underway.

High Priority Domains for Future Measure Consideration:

CMS identified the following categories as high-priority for future measure consideration:

1. Communication and Care Coordination
   • Measures regarding care coordination with other facilities and outpatient settings, such as hospice care.
   • Measures of the patient’s functional status, quality of life, and end of life.
2. Making Care Affordable
   • Measures related to efficiency, appropriateness, and utilization (over/under-utilization) of cancer treatment modalities such as chemotherapy, radiation therapy, and imaging treatments.
3. Person and Family Engagement
   • Measures related to patient-centered care planning, shared decision-making, and quality of life outcomes.

Measure Requirements: The following requirements will be considered by CMS when selecting measures forprogram implementation: Measure is responsive to specific program goals and statutory requirements. Measures are required to reflect consensus among stakeholders, and to the extent feasible, be endorsed by the national consensus entity with a contract underSection 1890(a) of the Social Security Act; currently the National Quality Forum(NQF) The Secretary may select a measure in an area or topic in which a feasible and practical measure has not been endorsed, by the entity with a contract under Section 1890(a)of the Social Security Act, as long as endorsed measures have been given due consideration Measure specifications must be publicly available. Measure steward will provide CMS with technical assistance and clarifications on the measure as needed. Promote alignment with specific program attributes and across CMS and HHSprograms. Measure alignment should support the measurement across the patient’s episode of care, demonstrated by assessment of the person’s trajectory across providers and settings. Potential use of the measure in a program does not result in negative unintended consequences (e.g., inappropriate reduced lengths of stay, overuse or inappropriate use of care ortreatment, limiting access to care). Measures must be fully developed and tested, preferably in the PCHenvironment. Measures must be feasible to implement across PCHs, e.g., calculation, and reporting. Measure addresses an important condition/topic with a performance gap and has a strong scientific evidence base to demonstrate that the measure when implemented can lead to the desired outcomes and/or more appropriate costs. CMS has the resources to operationalize and maintain the measure.

Current Measures: NQF staff have compiled the program's measures in a presentation organized according to concepts.

Program History and Structure: The Quality Reporting Program (QRP) for Inpatient Rehabilitation Facilities (IRFs) was established in accordance with section 1886(j) of the Social Security Act as amended by section 3004(b) of the Affordable Care Act. The IRF QRP applies to all IRF facilities that receive the IRF PPS (e.g., IRF hospitals, IRF units that are co-located with affiliated acute care facilities, and IRF units affiliated with critical access hospitals [CAHs]). Data sources for IRF QRP measures include Medicare FFS claims, the Center for Disease Control’s National Health Safety Network (CDC NHSN) data submissions, and Inpatient Rehabilitation Facility - Patient Assessment instrument (IRF-PAI) records. The IRF QRP measure development and selection activities take into account established national priorities and input from multi-stakeholder groups. Beginning in FY 2014, IRFs that fail to submit data will be subject to a 2.0 percentage point reduction of the applicable IRF Prospective Payment System (PPS) payment update. Plans for future public reporting of IRF QRP measures are under development. Further, the Improving Medicare Post-Acute Care Transformation of 2014 (IMPACT Act) amends title XVIII (Medicare) of the Social Security Act (the Act) to direct the Secretary of the Department of Health and Human Services (HHS) to require Long-term Care Hospitals (LTCHs), Inpatient Rehabilitation Facilities (IRFs), Skilled Nursing Facilities (SNFs) and Home Health Agencies (HHAs) to report standardized patient assessment data, data on quality measures including resource use measures. The development of standardized data stems from specified assessment domains via the assessment instruments that are used to submit assessment data to CMS by these post-acute care (PAC) providers. The IMPACT Act requires CMS to develop and implement quality measures from five measure domains: functional status, cognitive function, and changes in function and cognitive function; skin integrity and changes in skin integrity; medication reconciliation; incidence of major falls; and the transfer of health information when the individual transitions from the hospital/critical access hospital to PAC provider or home, or from PAC provider to another settings. The IMPACT Act also delineates the implementation of resource use and other measures in at least these following domains: total estimated Medicare spending per beneficiary; discharge to the community; and all condition risk adjusted potentially preventable hospital readmission rates.

High Priority Domains for Future Measure Consideration:

CMS identified the following four domains as high-priority for future measure consideration:

1. Making Care Affordable: An important consideration for the IRF QRP is to better assess medical costs based on PAC episodes of care. Therefore, CMS is considering developing efficiencybased measures such as a Medicare Spending per Beneficiary measure concept.

2. Communication/Care Coordination: Assessing resident care transitions and rehospitalizations are important. Therefore, CMS is considering developing measures that assesses discharge to the community and potentially preventable readmissions.

3. Communication/Care Coordination: Infrastructure and processes for care coordination are important for the IRF QRP. The World Health Organization regards implementing medication reconciliation as a standard operating protocol necessary to reduce the potential for ADEs that cause harm to patients.  Preventing and responding to ADEs is of critical importance as ADEs account for significant increases in health services utilization and costs. Medication reconciliation conceptually highlights care transitions and resident follow-up. Therefore, a medication reconciliation quality measure for IRF patients is being considered for future quality measure development. 

4. Communication/Care Coordination: Discharge to a community setting is an important health care outcome for patients in post-acute settings, offering a multi-dimensional view of preparation for community life, including the cognitive, physical, and psychosocial elements involved in a discharge to the community. Being discharged to the community is an important outcome for many patients for whom the overall goals of care include optimizing functional improvement, returning to a previous level of independence, and avoiding institutionalization. Therefore, a discharge to community measure for IRFs is being considered for the future use in the IRF QRP.

Current Measures: NQF staff have compiled the program's measures in a spreadsheet organized according to concepts.

Program History and Structure: The Improving Medicare Post-Acute Care Transitions Act of 2014 (The IMPACT Act) added Section 1899B to the Social Security Act establishing the Skilled Nursing Facility Quality Reporting Program (SNF QRP). Facilities that submit data under the SNF PPS are required to participate in the SNF QRP, excluding units that are affiliated with critical access hospitals (CAHs). Data sources for SNF QRP measures include Medicare FFS claims as well as Minimum Data Set (MDS) assessment data. The SNF QRP measure development and selection activities take into account established national priorities and input from multi-stakeholder groups. Beginning in FY 2018, providers that fail to submit required quality data to CMS will have their annual updates reduced by 2.0 percentage points. Further, the Improving Medicare Post-Acute Care Transformation of 2014 (IMPACT Act) amends title XVIII (Medicare) of the Social Security Act (the Act) to direct the Secretary of the Department of Health and Human Services (HHS) to require Long-term Care Hospitals (LTCHs), Inpatient Rehabilitation Facilities (IRFs), Skilled Nursing Facilities (SNFs), and Home Health Agencies (HHAs) to report standardized patient assessment data, data on quality measures including resource use measures. The development of standardized data stems from specified assessment domains via the assessment instruments that are used to submit assessment data to CMS by these post-acute care (PAC) providers. The IMPACT Act requires CMS to develop and implement quality measures from five measure domains: functional status, cognitive function, and changes in function and cognitive function; skin integrity and changes in skin integrity; medication reconciliation; incidence of major falls; and the transfer of health information when the individual transitions from the hospital/critical access hospital to PAC provider or home, or from PAC provider to another settings. The IMPACT Act also delineates the implementation of resource use and other measures in at least these following domains: total estimated Medicare spending per beneficiary; discharge to the community; and all condition risk adjusted potentially preventable hospital readmission rates.

High Priority Domains for Future Measure Consideration:

Current Measures: NQF staff have compiled the program's measures in a spreadsheet organized according to concepts.

Program History and Structure: The Home Health Quality Reporting Program (HH QRP) was established in accordance with section 1895 (b)(3)(B)(v)(II) of the Social Security Act. Home Health Agencies (HHAs) are required by the Act to submit quality data for use in evaluating quality for Home Health agencies. Section 1895(b) (3)(B)(v)(I) of the Act also requires that HHAs that do not submit quality data to the Secretary be subject to a 2 percent reduction in the annual payment update, effective in calendar year 2007 and every subsequent year. Data sources for the HH QRP include the Outcome and Assessment Information Set (OASIS) and Medicare FFS claims. Data is publically reported on the Home Health Compare website. The HH QRP measure development and selection activities take into account established national priorities and input from multi-stakeholder groups. Further, the Improving Medicare Post-Acute Care Transformation of 2014 (IMPACT Act) amends title XVIII (Medicare) of the Social Security Act (the Act) to direct the Secretary of the Department of Health and Human Services (HHS) to require Long-term Care Hospitals (LTCHs), Inpatient Rehabilitation Facilities (IRFs), Skilled Nursing Facilities (SNFs) and Home Health Agencies (HHAs) to report standardized patient assessment data, data on quality measures including resource use measures. The development of standardized data stems from specified assessment domains via the assessment instruments that are used to submit assessment data to CMS by these post-acute care (PAC) providers. The IMPACT Act requires CMS to develop and implement quality measures from five measure domains: functional status, cognitive function, and changes in function and cognitive function; skin integrity and changes in skin integrity; medication reconciliation; incidence of major falls; and the transfer of health information when the individual transitions from the hospital/critical access hospital to PAC provider or home, or from PAC provider to another settings. The IMPACT Act also delineates the implementation of resource use and other measures in at least these following domains: total estimated Medicare spending per beneficiary; discharge to the community; and all condition risk adjusted potentially preventable hospital readmission rates.

High Priority Domains for Future Measure Consideration:

CMS identified the following domains as high-priority for future measure consideration:

1. Patient and Family Engagement: Functional status and functional decline are important to assess for residents in HH settings. Patients who receive care while in a HH may have functional limitations and may be at risk for further decline in function due to limited mobility and ambulation. Therefore, measures to assess functional status are in development.
2. Making Care Safer: Safety for individuals in a home-based setting is an important priority for the HH QRP as persons in home health settings are at risk for major injury due to falls, new or worsened pressure ulcers, pain, and functional decline. Therefore, these concepts will be considered for future measure development.
3. Making Care Affordable: An important consideration for the HH QRP is to better assess medical costs based on PAC episodes of care. Therefore, CMS is considering developing efficiencybased measures such as a Medicare Spending per Beneficiary measure concept.
4. Communication/Care Coordination: Assessing an individual’s care transitions and rehospitalizations is important. Discharge to a community setting is an important health care outcome for patients in post-acute settings, offering a multi-dimensional view of preparation for community life, including the cognitive, physical, and psychosocial elements involved in a discharge to the community. Being discharged to the community is an important outcome for many individuals for whom the overall goals of care include optimizing functional improvement, returning to a previous level of independence, and avoiding institutionalization. Therefore, CMS is considering developing measures that assesses discharge to the community and potentially preventable readmissions.
5. Communication/Care Coordination: Infrastructure and processes for care coordination are important for the HH QRP. The World Health Organization regards implementing medication reconciliation as a standard operating protocol necessary to reduce the potential for ADEs that cause harm to patients.  Preventing and responding to ADEs is of critical importance as ADEs account for significant increases in health services utilization and costs. Therefore, a medication reconciliation quality measure for individuals in a home health setting is being considered for future quality measure development. Medication reconciliation conceptually highlights care transitions and resident follow-up.

Current Measures: NQF staff have compiled the program's measures in a spreadsheet organized according to concepts.

Program History and Structure: The Long-Term Care Hospital (LTCH) Quality Reporting Program (QRP) was established in accordance with section 1886(m) of the Social Security Act, as amended by Section 3004(a) of the Affordable Care Act. The LTCH QRP applies to all LTCHs facilities designated as an LTCH under the Medicare program. Data sources for LTCH QRP measures include Medicare FFS claims, the Center for Disease Control and Prevention’s National Health Safety Network (CDC’s NHSN) data submissions, and the LTCH Continuity Assessment Record and Evaluation Data Sets (LCDS). The LTCH QRP measure development and selection activities take into account established national priorities and input from multi-stakeholder groups. Beginning in FY 2014, LTCHs that fail to submit data will be subject to a 2.0 percentage point reduction of the applicable Prospective Payment System (PPS) increase factor. Further, the Improving Medicare Post-Acute Care Transformation of 2014 (IMPACT Act) amends title XVIII (Medicare) of the Social Security Act (the Act) to direct the Secretary of the Department of Health and Human Services (HHS) to require Long-term Care Hospitals (LTCHs), Inpatient Rehabilitation Facilities (IRFs), Skilled Nursing Facilities (SNFs) and Home Health Agencies (HHAs) to report standardized patient assessment data, data on quality measures including resource use measures. The development of standardized data stems from specified assessment domains via the assessment instruments that are used to submit assessment data to CMS by these post-acute care (PAC) providers. The IMPACT Act requires CMS to develop and implement quality measures from five measure domains: functional status, cognitive function, and changes in function and cognitive function; skin integrity and changes in skin integrity; medication reconciliation; incidence of major falls; and the transfer of health information when the individual transitions from the hospital/critical access hospital to PAC provider or home, or from PAC provider to another settings. The IMPACT Act also delineates the implementation of resource use and other measures in at least these following domains: total estimated Medicare spending per beneficiary; discharge to the community; and all condition risk adjusted potentially preventable hospital readmission rates.

High Priority Domains for Future Measure Consideration:

CMS identified the following domains as high-priority for LTCH QRP future measure consideration:

1. Effective Prevention and Treatment: Having measures related to ventilator use, ventilator- associated event and ventilator weaning rate are a high priority for CMS as prolonged mechanical ventilator use is quite common in LTCHs and respiratory diagnosis with ventilator support for 96 or more hours is the most frequently occurring diagnosis.
2. Effective Prevention and Treatment (Aim: Healthy People/Healthy Communities): In discussions with LTCH providers, it was noted that mental health status is an important measure of care for LTCH patients. CMS is considering a Depression Assessment & Management quality measure.
3. Patient and Family Engagement: While rehabilitation and restoring functional status are not the primary goals of patient care in the LTCH setting, functional outcomes remain an important indicator of LTCH quality as well as key to LTCH care trajectories. Providers must be able to provide functional support to patients with impairments. Thus, metrics showing change in self- care and mobility function are under development.
4. atient and Family Engagement: CMS would like to explore measures that will evaluate the patient’s experiences of care as this is a high priority of providers. Therefore, the HCAHPS and Care Transition quality measure (CTM)-3 is being considered.
5. Making Care Affordable: An important consideration for the LTCH QRP is to better assess medical costs based on PAC episodes of care. Therefore, CMS is considering developing efficiency-based measures such as a Medicare Spending per Beneficiary measure concept.
6. Communication/Care Coordination: Assessing patient care transitions and rehospitalizations are important. Therefore, CMS is considering developing measures that assesses discharge to the community and potentially preventable readmissions.
7. Communication/Care Coordination: Infrastructure and processes for care coordination are important for the LTCH QRP. Therefore, a medication reconciliation quality measure for LTCH patients is being considered for future quality measure development. Medication reconciliation conceptually highlights care transitions and resident follow-up.

Current Measures: NQF staff have compiled the program's measures in a spreadsheet organized according to concepts.

Program History and Structure: The Hospice Quality Reporting Program (HQRP) was established in accordance with section 1814(i) of the Social Security Act, as amended by section 3004(c) of the Affordable Care Act. The HQRP applies to all hospices, regardless of setting. Proposed data sources for future HQRP measures include the Hospice Item Set and the Hospice Consumer Assessment of Healthcare Providers and Systems (CAHPS) survey. HQRP measure development and selection activities take into account established national priorities and input from multi-stakeholder groups. Beginning in FY 2014, Hospices that fail to submit quality data will be subject to a 2.0 percentage point reduction to their annual payment update.

High Priority Domains for Future Measure Consideration:

Current Measures: NQF staff have compiled the program's measures in a spreadsheet organized according to concepts.

Index of Measures (by Program)

General Comments

• General comments
  
  

• Hospital Visits following General Surgery Ambulatory Surgical Center Procedures (Public comments received:7; MUC ID: MUC17-233)
  
  

• Medication Reconciliation for Patients Receiving Care at Dialysis Facilities (Public comments received:2; MUC ID: MUC17-176)
  
  
• Percentage of Prevalent Patients Waitlisted (PPPW) (Public comments received:6; MUC ID: MUC17-241)
  
  
• Standardized First Kidney Transplant Waitlist Ratio for Incident Dialysis Patients (SWR) (Public comments received:5; MUC ID: MUC17-245)
  
  

• Hospital-Wide All-Cause Risk Standardized Mortality Measure (Public comments received:8; MUC ID: MUC17-195)
  
  
• Hybrid Hospital-Wide All-Cause Risk Standardized Mortality Measure (Public comments received:6; MUC ID: MUC17-196)
  
  
• Hospital Harm Performance Measure: Opioid Related Adverse Respiratory Events (Public comments received:6; MUC ID: MUC17-210)
  
  

• Lumbar Spine Imaging for Low Back Pain (Public comments received:1; MUC ID: MUC17-223)
  
  

• Continuity of Pharmacotherapy for Opioid Use Disorder (Public comments received:1; MUC ID: MUC17-139)
  
  
• Average change in functional status following lumbar spine fusion surgery (Public comments received:1; MUC ID: MUC17-168)
  
  
• Average change in functional status following total knee replacement surgery (Public comments received:2; MUC ID: MUC17-169)
  
  
• Average change in functional status following lumbar discectomy laminotomy surgery (Public comments received:1; MUC ID: MUC17-170)
  
  
• Appropriate Use of DXA Scans in Women Under 65 Years Who Do Not Meet the Risk Factor Profile for Osteoporotic Fracture (No public comments received; MUC ID: MUC17-173)
  
  
• Average change in leg pain following lumbar spine fusion surgery (Public comments received:2; MUC ID: MUC17-177)
  
  
• Optimal Diabetes Care (Public comments received:2; MUC ID: MUC17-181)
  
  
• Optimal Vascular Care (Public comments received:1; MUC ID: MUC17-194)
  
  
• Diabetes A1c Control (< 8.0) (Public comments received:1; MUC ID: MUC17-215)
  
  
• Ischemic Vascular Disease Use of Aspirin or Anti-platelet Medication (No public comments received; MUC ID: MUC17-234)
  
  
• Routine Cataract Removal with Intraocular Lens (IOL) Implantation (Public comments received:3; MUC ID: MUC17-235)
  
  
• International Prostate Symptom Score (IPSS) or American Urological Association-Symptom Index (AUA-SI) change 6-12 months after diagnosis of Benign Prostatic Hyperplasia (Public comments received:1; MUC ID: MUC17-239)
  
  
• Screening/Surveillance Colonoscopy (Public comments received:2; MUC ID: MUC17-256)
  
  
• Knee Arthroplasty (Public comments received:4; MUC ID: MUC17-261)
  
  
• ST-Elevation Myocardial Infarction (STEMI) with Percutaneous Coronary Intervention (PCI) (Public comments received:3; MUC ID: MUC17-262)
  
  
• Revascularization for Lower Extremity Chronic Limb Ischemia (Public comments received:2; MUC ID: MUC17-263)
  
  
• Zoster (Shingles) Vaccination (Public comments received:1; MUC ID: MUC17-310)
  
  
• Patient reported and clinical outcomes following ilio-femoral venous stenting (No public comments received; MUC ID: MUC17-345)
  
  
• Elective Outpatient Percutaneous Coronary Intervention (PCI) (Public comments received:3; MUC ID: MUC17-359)
  
  
• Intracranial Hemorrhage or Cerebral Infarction (Public comments received:4; MUC ID: MUC17-363)
  
  
• Simple Pneumonia with Hospitalization (Public comments received:1; MUC ID: MUC17-365)
  
  
• HIV Screening (Public comments received:1; MUC ID: MUC17-367)
  
  

• Optimal Diabetes Care (Public comments received:4; MUC ID: MUC17-181)
  
  
• Diabetes A1c Control (< 8.0) (Public comments received:6; MUC ID: MUC17-215)
  
  
• Ischemic Vascular Disease Use of Aspirin or Anti-platelet Medication (Public comments received:1; MUC ID: MUC17-234)
  
  

• 30-Day Unplanned Readmissions for Cancer Patients (Public comments received:3; MUC ID: MUC17-178)
  
  

• CoreQ: Short Stay Discharge Measure (Public comments received:1; MUC ID: MUC17-258)
  
  

Full Comments (Listed by Measure)

• We would like to note our concern over conditions that call upon NQF Committees to advise on specific measures. Specifically, the requests to have the Disparities Standing Committee provide guidance on the social risk factors in the risk adjustment approach and Attribution Committee on the attribution methodology of measures. It is our understanding that these committees do not have defined roles in reviewing measures in the Consensus Development Process at this time and, specific measures during the Socioeconomic Status Trial Period. The AMA asks that NQF clearly define how it will seek input from these committees to ensure that the conditions placed on these measures will be met. In addition, based on our experience with the MAP, we offer the following suggestions. Our suggestions would allow the workgroups to spend more time determining whether a measure is appropriate and useful for a specific program (the intended purpose of the MAP) rather than spend significant time determining whether the measure meets minimum criteria around reliability and validity. This question is the most critical one for these groups to discuss and under the current process workgroup members do not have the time or information to perform that level of analysis. • Clarifying Conditions: It would be good if staff could draft the specific conditions the workgroup is placing on a measure real-time as the discussion occurs. This information could be projected on a slide during the discussion. It will ensure that everyone is on the same page and also facilitate the discussion. • Voting Process: The voting process this year was a little clunky when dealing with measures where the workgroup did not necessarily have consensus. It seemed like they were forced into a vote and often resulting in a default to the preliminary recommendation, which was not where many (but not 60%) of the workgroup members thought the recommendation should be. I am not sure what the solution is but the process used this year did not necessarily achieve the desired goal of reflecting consensus and the true opinions/input of the workgroup. • Testing Data: If CMS is going to require testing data for a measure to make it on the MUC list then CMS must be consistent with the requirements. It is incredibly difficult for workgroup members to understand the degree to which a measure generally performs on reliability and validity unless the testing results are publicly available and/or the measure is NQF-endorsed. Many of the measures this year did not have publicly available testing results and associated measure specifications. Often the steward provided a verbal summary, which is not sufficient. It would be preferable if the MAP only weighed in on fully specified and tested measures where all of the information is available at the time of the MUC list release. If CMS chooses to bring forward a measure that is not ready for implementation (i.e., specified and tested in the setting for which it is intended), then the MAP can create a “not ready for prime time” type of category and provide input on the general concept but not assign a decision category to the measure. The expectation would then be that CMS would bring back the measure when it is ready. In an ideal world, measures that have been tested in alternative settings and not the setting proposed for use should not be considered as having testing data and allowed to be placed on the MUC list. For example, a measure that has been endorsed and tested at the health plan or state level but proposed for use at the clinician level. • Release of Information: The MUC list needs to include links or attachments to all of the measure specifications and available testing data. It is very difficult to draft comments and provide feedback without readily available access to information. It also leads to inconsistency because some of the information can be found either through NQF’s website or through an internet search and often you may not be reviewing the most up-to-date information or any information at all besides the general information published on the MUC list. The discussion guides must also be released to the public. Neither the clinician nor hospital workgroup discussion guides were released to the public. All workgroup member materials should be released to the public ahead of the meeting. (Submitted by: American Medical Association)
  
  
• It is important to note that while the MACRA statute encourages CMS to select quality measures for the MIPS program that are endorsed by a consensus-based entity, CMS is not required to include only measures set forth or endorsed by NQF or other consensus-building entities and we request the report reflect the appropriate language per the MACRA statue. CMS may select for the MIPS program, any quality measures it deems appropriate, as long as the measure has a focus that is evidence-based. While the American Medical Association (AMA) is supportive of the collaborative process CMS has used in the development of the episode cost measures, we do not believe that they were ready for MAP consideration and did not receive adequate vetting by the Clinician Workgroup. There is also a consistent problem with the timeline to provide comments back to the MAP which greatly jeopardizes the integrity of the MAP process. The AMA is troubled over the lack of transparency and inconsistent application of the Measure Selection Criteria (MSC) to these episode-based cost measures and request the report be amended to reflect the lack of information and testing data provided to the workgroup and public prior to deliberations during the public comment period, during workgroup deliberations and preliminary recommendation comment period because as the report is written appears the committee and public had access to all necessary information. Specifically, no information regarding the individual measure specifications, attribution methodology, or reliability and validity testing results were released for member and public review prior to the MAP Clinician Workgroup meeting, modifications to the measures based on preliminary feedback are still being made, and, to our knowledge, the Workgroup members did not have any detailed information in front of them at the time of the discussion. The developer only cited some limited information on how the measures were developed and tested. Given the degree of interest from numerous medical specialty societies, the AMA looked forward to a robust and detailed discussion on each of these cost measures but unfortunately, it did not occur (Submitted by: American Medical Association)
  
  
• In general, we agree with the workgroup deliberations. There are some MUC’s which had conditional support which we think should be supported, and are noted under each MUC as well as in our comments on the hospital, clinician, and PAC/LTC reports. We acknowledge that there were 9 measures under programs of end stage renal disease, prospective payment, ambulatory surgical care, and outpatient/inpatient quality. We are deeply concerned that there were no measures under consideration regarding hospital acquired infections and unnecessary readmissions as these are common indicators of increased morbidity and mortality. Under ERSD, we support MUC17-176 regarding medication reconciliation. We are concerned that MUC17-241 waitlist and MUC17-245 first transplant, were only conditionally supported as there are disparities related to race and income and we would add disability discrimination. We fail to understand how insurance status would affect these measures as once a consumer is eligible for either transplant or dialysis, they become eligible for Medicare retroactively. Under prospective payment, we support MUC17-178 regarding unnecessary readmissions. Under ambulatory surgical centers, we are concerned that MUC17-233 regarding hospital admissions was only conditionally supported as research indicates that ambulatory centers have worse outcomes including complications than hospital settings. Although we understand that these are “relatively rare events that could adversely affect low volume ACS”, even one adverse event or death is too many. Under hospital outpatient, we agree with not recommending MUC17-223 regarding lumbar spine imaging as this was also not recommended by NQF’s Musculoskeletal Standing Committee. Under hospital inpatient, we support MUC17-195 and 196 regarding mortality measures and agree with the conditional recommendation MUC170-210 regarding opioid use and respiratory events. We acknowledge that measures were reviewed under merit-based and Medicare shared savings programs. Under merit systems, we support the three recommended measures: MUC17-194 vascular care, MUC17-168 functional status post lumbar surgery and MUC17-169 functional status post knee surgery. We concur with the conditional support of the other 19 measures. Under Medicare savings, we agree with the conditional support of the three measures regarding diabetes, A1c, and vascular disease. We acknowledge that measures were considered in the settings of rehabilitation, long term care (LTC), skilled nursing (SNF), home health, and hospice care. We support the criteria in table 1, most notably falls, medication (inappropriate or adverse event), infection, transitions of care, mental health, and family/patient education. We are concerned however with the lack of consideration of shared-decision making as well as social determinants of health as these affect outcomes. We agree with the examination of measure gaps. Regarding SNFs, we support the recommended measure MUC17-258 on short discharge and consumer satisfaction. Under LTC, we would recommend a measure on the inappropriate use of the Medicare 100 day rule, including facilities attempting to get guardianship to keep patients longer. Under inpatient rehab, we would suggest a measure examining the misuse of Medicare observation status even for in-patient stays, resulting the consumers bearing the cost of rehab after hospitalization. (Submitted by: Statewide Parent Advocacy Network/Family Voices NJ)
  
  
• Quality of Care will not be gained without checks and balances. I have a son in Homecare with the Department of Disabilities under AHCCCS in the State of Az.There is no quality of care department under DDD. There is not a quality of care department for the persons in the program under AHCCCS. The program directors, clinicians, and agencies that provide the HH care all are protected and the members are left without care. We have so many families tht have no care at all. The biggest problem is that DDD conducts their own GAP reports so in essens there are no GAP reports turned into AHCCCS. This is all required but if no one is looking then why should they report their misgivings. The HHS nurses deliberately miss assess paitents and give them less care than what they should. Their reasoning is so that they can serve more poeple. This is not ok to leave incapaciatated people alone in their homes and not report this to anyone other than themselves. My only comment in the end is: No facility, government agency, insurance company, provider agency should be allowed to report without some kind of outside check to see if they are doing what they should. We have massive problems in our Home and Community Service Program due to the fact that agencies have been told by DDD's HHS nurses that they do not have to report the GAPS or any other negative thing to AHCCCS. The people can not take care of themselve let alone know how to report when they are not taken care of to begin with. I am not sure how this could be set up but i think there could be a central number that patients could call which was attatched to a federal government system that would then be logged in and returned to the provider that is being complained about. As I have stated...it has to be outside of the intities that are already taking care of that patient. AHCCCS/DES/DDD have no centeral numbers to call. DDD has a number and files grievences on the issues but not one issue is ever resolved. No one is given the AHCCCS GAP line on purpose by DDD. DDD has hiden all the reports from AHCCCS....set up a system that will not be a catch 21 for the people. It seems that we write everything in a manner that is member driven but then there is no outlet for the member. Thank you very much Darlene (Submitted by: MLHCT)
  
  
• Serious concern over whether federal government will enforce the quality measures - Agree with need to standardize quality metrics across CMS products - Measure Removal Criteria is not standard with the other 2 documents – should be standardized Also thoughtful points – need for risk adjustment, consideration for regional as opposed to national comparisons for cost metrics. - Overall, thought points raised were very thoughtful. For example, the areas in need of development for IRF, LTCH, SNF, HH, and Hospice all made sense to me at a high-level. They were also thoughtful about administrative burden that quality measures create, and that needs to be a consideration. - For Measure Removal Criteria (p. 10), I thought the criteria mentioned made sense. Could rephrase some of the existing criteria to make it clearer that measures with already high performance and limited opportunity to improve, or those with limited overall quality impact should be removed (Submitted by: AMGA)
  
  
• The AAMC appreciates the MAP Workgroups’ thoughtful review and discussion of the measures under consideration (MUC). The following are the AAMC’s high-level comments on the MAP recommendations for both hospitals and clinicians: • Regarding the clinician measures under consideration, the AAMC strongly believes that providers should not be held accountable for activities outside their control. The 8 episode level cost measures must be appropriately risk adjusted, including for social risk factors, and the attribution methodology for episodes should clearly and accurately determine the relationship between patient and clinician before such episode-level cost measures are incorporated into the Quality Payment Program. • For the hospital measures, the AAMC strongly believes that certain accountability measures must be adjusted for sociodemographic status (SDS) before being included in the Medicare quality reporting programs, be NQF-endorsed prior to MAP review, and be included in the Inpatient Quality Reporting (IQR) program for at least one year before being considered in a performance program by the Workgroup. Additionally, the AAMC recommends that the report acknowledge the challenges in voting that occurred during the MAP Hospital Workgroup meeting. • The AAMC believes that the MAP Workgroups should review measures in the Medicare programs holistically in order to ensure that new measures add value, are useful for consumers, and promote alignment, while also considering the burden to reporting these measures for providers. The AAMC notes that CMS’ Meaningful Measures framework is a starting point toward this work, and supports the MAP Hospital Workgroup’s suggestion that CMS increase harmonization of measures and evaluate similar constructs across settings and programs. The AAMC believes that increased harmonization includes harmonization of measure implementation, and that the MAP was correct to note that variation in how a measure is implemented creates challenges for patients and providers. Additionally, the AAMC is supportive of an active MAP role in examining measures used in CMS programs more broadly, due to the amplified impact of the measures as other payers and purchasers often implement measures used by CMS. • Finally, the AAMC agrees with MAP Workgroup feedback to CMS in regards to which criteria CMS should consider when removing measures from its quality reporting and value-based purchasing programs. Specifically, the AAMC is supportive of the MAP Hospital Workgroup’s additional suggested criteria and items to consider, particularly risk adjustment, provider burden, and operational issues. Address Challenges Related to MAP Voting in the Draft Report During the December MAP Hospital Workgroup there was significant discussion about the workgroup’s voting process, most notably when the chairs re-visited the morning’s voting on two of the three measures under consideration for the End-Stage Renal Disease Quality Incentive Program (ESRD QIP). This discussion around the lack of clarity around the voting process was an important part of the workgroup’s deliberation on the considerations for implementing measures in federal programs. In particular, there was concern about “what” Workgroup members were actually voting on for a given vote, and what the thresholds were for motion passage. Overall, the voting process was inconsistent and not truly reflective of Workgroup consensus. These issues must be clarified prior to any future voting by the committee. The AAMC recommends that the challenges and concerns with the voting process related to these measures be acknowledged in the report and be addressed prior to future MAP meetings. Accountability Measures Must Be Adjusted for Sociodemographic Status (SDS) The AAMC has long advocated for appropriate adjustment for sociodemographic status (SDS) factors for certain outcome measures. The AAMC agrees with the Workgroup’s preliminary recommendations that the pneumonia episode-of-care payment and excess days in acute care after hospitalization measures should undergo review in the SDS trial period to determine whether there is a conceptual and empirical relationship between outcomes and SDS factors prior to inclusion in the IQR program. The Association also strongly believes that other approved Hospital MAP Workgroup measures, including hospital visits within 7 days after hospital outpatient surgery and CABG mortality, should be submitted for review in the SDS trial period. The AAMC strongly supports a continued robust and transparent SDS trial period. The Association is very concerned that the issues and concerns regarding SDS are not being sufficiently addressed. We ask that the SDS trial period be a priority for the MAP, NQF, and CMS in 2018 and subsequent years. The AAMC also notes that there are several measures in the current performance programs which have not been SDS adjusted. We ask that MAP include a recommendation regarding the need to adjust the existing measures for SDS, and that an opportunity be provided to review all measures for appropriateness in the performance programs. All Measures Reviewed by the MAP Hospital Workgroup Should be NQF Endorsed NQF endorsement demonstrates that a measure has been tested, is reliable, and can be used in a specific setting. With the volume of measures the MAP has to review, the Workgroups and Coordinating Committee rely heavily on NQF endorsement to ensure the measure is sound. Since hospital measures are typically not re-reviewed by the Workgroup, it is essential that these measures be NQF-endorsed at the time of consideration so that members are fully informed as to the measure’s appropriateness for the Medicare reporting and performance programs. The voting on measures under consideration demonstrated that there is a narrow consensus by the MAP hospital workgroup to conditionally support measures pending NQF endorsement, and that there is a large minority with major concern about CMS pushing a measure to the workgroup before it is fully vetted and understood. The AAMC echoes the MAP members’ concerns regarding how best to provide recommendations to CMS on measures that are not fully developed and tested or measures that have not been examined for their scientific acceptability. Individual Measure Review ESRD Program: Percentage of Prevalent Patients Waitlisted (PPW) During the MAP Hospital Workgroup meeting, there was considerable discussion regarding the measure of the percentage of patients waitlisted for a kidney transplant (MUC 17-241). While the AAMC agrees with the importance of improving transplantation rates for all patients with ESRD and recognize the issues of equal access to transplantation, we do not support the attribution of this measure to dialysis facilities. Referral for transplantation is a decision made by the nephrologist and waitlisting is a decision that is made by the transplant center neither decision is under the control of a dialysis facility. There may be clinical reasons that a patient is not eligible for a transplant and those decisions should be made by a physician responsible for the patient’s care and by the patient. These are complex decisions that take into account many factors. There are demographic issues depending on the location of the facility that may make nationwide comparisons of waitlists percentages difficult to interpret. As the NQF and CMS consider measures for inclusion in programs, it is critical to ensure that these measures are meaningful to the providers and to the consumers. We are concerned about unintended consequences that may occur if this measure were implemented in the ESRD program, particularly due to the attribution concerns. Hospital-Wide All-Cause Mortality Measure The Hospital MAP Workgroup conditionally supported a new hospital-wide all-cause risk standardized mortality measure (MUC17-195) pending review and endorsement, specifically recommending that the NQF committee reviewing the measure ensure that there is appropriate, validated evidence supporting the measure and explicitly consider the importance of the measure and potential unintended consequences. It was also recommended that the measure be brought to the NQF Disparities Standing Committee as part of the evaluation for appropriateness of adjusting for social risk factors. The AAMC appreciates that the MAP’s discussion that an all-cause mortality measure is of great importance to patients and could potentially encourage facilities to work more collaboratively with other providers and improve continuity of care. However, serious concerns relating to risk adjustment, misuse of all-cause mortality metrics used in epidemiological studies, unintended consequences for end-of-life care, misinterpretation of the measure score by consumers discussed were raised, and it is essential that these concerns be vetted through the NQF endorsement process. Claims-based risk adjustment by using HCC data might not adequately account for appropriate clinical and social risk factors and does not broadly capture the patient’s health status. Hospitals that disproportionately care for vulnerable patient populations are disadvantaged when SDS factors are not considered in the risk adjustment or scoring methodology. The AAMC agrees with the MAP that appropriate risk adjustment is necessary to ensure that the measure does not disproportionately penalize facilities who see more complex patients. Appropriate exclusions should address hospice enrollment to ensure that the timing of hospice decisions by a patient’s family that results in a delay in enrollment such that the patient’s admission is inappropriately included when measuring a hospital’s mortality rate. The AAMC shares the concerns raised by the National Coalition for Hospice and Palliative Care that as proposed, this measure may have the unintended consequence of “inhibit[ing] the use of palliative services and failure to accommodate the wishes of patients who would prefer death over prolonged life-sustaining treatment.” The AAMC believes that condition-specific mortality measures already in use in the IQR may be more actionable for hospitals and may provide more detailed information to patients to support consumer decision-making. We agree with the MAP members who cautioned that performance scores on an all-cause mortality measure could be potentially misleading to consumers, as this may simply reflect a lower acuity facility and not necessarily a facility’s overall quality. For all of the reasons above, the AAMC continues to strongly believe that these concerns weigh against conditional support of the measure for the Hospital Inpatient Quality Reporting (IQR) program. Hybrid Hospital-Wide All-Cause Mortality Measure The Hospital MAP Workgroup also conditionally supported a new hybrid hospital-wide all-cause risk standardized mortality measure (MUC17-196) pending review and endorsement with enumerated recommendations for the Standing Committee in its review of the measure, including that the Standing Committee pay specific attention to the ability to consistently obtain EHR data across hospitals. The MAP also recommended that there be a voluntary reporting period for the measure before it is finalized in the IQR program in order to allow providers to test the extraction of electronic data elements. The AAMC agrees with the voluntary reporting recommendation, but continues to strongly believe that the overall concerns with hospital-wide all-cause mortality measures (as discussed above) plus the additional concerns described below related to this hybrid measure weighed against conditional support of the measure for the IQR program. The AAMC understands that CMS’ intention is to replace MUC17-195 with this hybrid all-cause mortality measure once the hybrid measure is fully developed and endorsed. The MAP noted that the concerns for the claims-based mortality measure were the same for this measure, in addition to concerns with the challenges of extracting EHR data and EHR fragmentation. The AAMC believes that integrating EHR data with claims data is a positive step, but we recommend that the focus of efforts at this stage should be on the use of EHR data to adjust condition specific mortality measures that are currently being used in the programs. Additionally, the AAMC would like to note that there was discussion around concern that this measure was developed using Kaiser Permanente Northern California data in the model, and that such data is not representative of Medicare Fee for Service more broadly. This was not included in the draft report, and the AAMC recommends that this concern be acknowledged in the final report. The draft MAP report, titled “Considerations for Implementing Measures in Federal Programs” reviews the Clinician Workgroup discussion regarding the measures under consideration for the Merit-Based Incentive Payment System (MIPS) and the Medicare Shared Savings Program. CMS identified key program needs and priorities for the MIPS program, including cost and composite measures, measures relevant to specialty providers, domains of person and caregiver experience and outcomes, and appropriate use. One copy editing note is that the draft report appears to have an inconsistency. The spreadsheet “map_2017-2018_preliminary_recommendations” posted to the MAP Coordinating Committee web indicates that the MAP Clinician Workgroup conditionally supported eighteen measures for MIPS, but in the report, atop page 6, it states that the “MAP conditionally supported nineteen measures.” Cost Measures Should be Appropriately Risk-Adjusted and the Attribution Methodology Should be Clear and Accurately Determine Patient/Clinician Relationship Cost measures have been identified as a priority, and CMS addressed this priority through the inclusion of 8 cost measures on the MUC list for discussion during the MAP Clinician Workgroup meeting. The AAMC remains concerned that none of the 8 cost measures are adjusted to account for socio-demographic status (SDS). In addition to patient clinical complexity, SDS factors can drive differences in average costs. In particular, physicians at academic medical centers (AMCs) care for vulnerable populations of patients who are sicker, poorer, and more complex than patients treated elsewhere. In addition, we echo the MAP’s cautious concern for potential care stinting as an unintended consequence of cost measures, and agree that appropriate risk adjustment may help safeguard against that practice. In regards to attribution – AAMC has previously commented that attribution methods used should be clear and transparent to clinicians and that it is critical that there be an accurate determination of the relationship between a patient and a clinician to ensure that the correct clinician is held responsible for the patient’s outcomes and costs. Attribution is complicated, given that most patients receive care from numerous clinicians across several facilities, and AAMC has urged CMS to explore better data sources and analytic techniques to support more accurate attribution. The AAMC recommends that: (1) cost measures include risk-adjustment for SDS factors, (2) the attribution methodology is transparent, and (3) the correct clinician is held responsible for the patient’s outcomes and costs. Composite Measures Should Only Be Used When Fully Developed, Technically Feasible, and Actionable The MAP expressed encouragement for additional composite measures under consideration, and acknowledged the additional technical challenges that composite measures pose during the measure development process, noting an adult vaccine composite measure might be preferred over individual measures for vaccine administration, but that such a composite measure might be challenging to develop and maintain due to changing clinical guidelines. In regards to condition specific composite measures, the MAP noted that composite measures may provide challenges at the clinician level where a particular clinician or specialist does not have complete control over the care for that particular condition. The AAMC supports composite measures, but only when the composite measure is fully developed and vetted, technically feasible, and actionable at the individual-clinician level. (Submitted by: Association of American Medical Colleges (AAMC))
  
  
• America’s Essential Hospitals appreciates the opportunity to comment on the Centers for Medicare & Medicaid Services’ Measures Under Consideration list for December 2017, and the MAP deliberation process. America’s Essential Hospitals appreciates and supports the work the National Quality Forum (NQF) and its staff have done in the past few years to facilitate member engagement and improve understanding of measures under consideration, including dissemination of materials well in advance of Measure Applications Partnership (MAP) meetings. However, we feel that process concerns remain and need further clarification to ensure meaningful discussion by the MAP and consistency in the voting process. The work of the MAP is inherently complex and challenging, but it is important. Moving forward, we believe a more standardized framework for discussion of measures would promote clarity and transparency for developers and stakeholders alike. This clarity would help to focus MAP members’ discussions. For example, there is ambiguity about the role of the measure developer during MAP discussions and what level of detail—e.g., analyses and information about scientific acceptability—is appropriate to discuss. Clarity also would improve the voting process. At the most recent meeting of the MAP Hospital Workgroup, in December 2017, NQF staff attempted to define and illustrate the voting categories. However, confusion remained, and our concern is that the overall voting process is inconsistent and therefore does not reflect a true consensus of the MAP. We look forward to working with NQF to mitigate these process issues and uphold the validity of the MAP’s work and decisions. On behalf of our 325 member hospitals, America’s Essential Hospitals appreciates the opportunity to comment on the Centers for Medicare & Medicaid Services’ Measures Under Consideration list for December 2017. We respectfully submit our comments to the Measure Applications Partnership for consideration during this pre-rulemaking process. Our members are dedicated to providing high-quality care for all, including the vulnerable, and provide a disproportionate share of the nation’s uncompensated care. Risk Adjustment Outcomes measures make up approximately 40 percent of items on the Measures Under Consideration list for December 2017. The Measure Applications Partnership (MAP) must appropriately assess whether measures are impacted by sociodemographic factors, including socioeconomic status, and risk adjust when warranted. Without proper risk adjustment, an essential hospital serving the most complex patients, who also have low incomes and other confounding sociodemographic factors, might appear through public reporting to have poorer outcomes than other hospitals. This is an inaccurate and misleading picture created by factors outside the hospital’s control. America’s Essential Hospitals urges the MAP to only recommend outcome measures that have been appropriately risk adjusted. Two measures under consideration related to hospitalwide mortality—MUC17-195 and MUC17-196—are not appropriate measures of quality and should not be included in payment programs. Hospitalwide mortality is a relatively imprecise and crude measure of quality. Condition-specific mortality measures already included in the Inpatient Quality Reporting Program could facilitate targeted improvement efforts and support consumer decision making. However, there is a disconnect as to the value a hospitalwide measure would provide and the rationale for the addition of such a measure to payment programs. This disconnect is exacerbated when certain factors are not considered, such as the lack or insufficiency of data concerning case mix, coding variation between hospitals, disease severity, referral bias, end-of-life care, and place of death. This leads to incomparability between hospitals and negatively effects essential hospitals, which provide specialized, complex care to vulnerable populations. With respect to MUC17-196, we have concerns about the use of electronic health record (EHR) data in the risk adjustment model. This measure uses a hybrid approach, drawing from Medicare claims in addition to EHR data. The usefulness of having information to support measure accuracy must be balanced with the challenges of extracting EHR data. Providers adapt their workflows to ensure meticulous entry of standardized data into EHRs, but hospitals still face obstacles to the meaningful use of health information technology, and adoption requires extensive training and resources. Further, testing of the MUC17-196 measure was limited to a group of Kaiser Permanente hospitals in northern California. It is unwise to incorporate a measure into reporting programs until it is supported by adequate evidence of its validity across the field. Hospital Harm Performance Measure: Opioid-Related Adverse Events America’s Essential Hospitals supports the recommended revision and resubmission of MUC17-210 (Hospital Harm Performance Measure: Opioid-Related Adverse Respiratory Events) before rulemaking. We agree that the opioid epidemic is an urgent health crisis, and MUC17-210 assesses a critical patient safety issue. Essential hospitals often are at the forefront of caring for communities and patients affected by this public health emergency. However, this measure has not been adequately tested and must be further developed and refined. Specific attention should be placed on the potential unintended consequences of the measure and how to balance it with existing measures that assess appropriate pain control. Before including the measure in public reporting programs, we encourage the Centers for Medicare & Medicaid Services to continue to develop and field test this measure, with input from stakeholders, to ensure collected information accurately reflects quality of care. Measure Alignment and Measure Removal Criteria America’s Essential Hospitals supports the use of a strategic, high-impact set of quality metrics in federal programs. We emphasize the importance of streamlining measures to promote greater alignment and harmonization and to reduce redundancies and inefficiencies. Measures should be used only if they reveal meaningful differences in performance across providers. The measures also should be administratively simple to collect and report. The goal should be to align measures across the care continuum—between hospitals, physicians, and others—to reduce unnecessary data collection and reporting efforts. (Submitted by: America's Essential Hospitals)
  
  
• At the December meeting of the MAP Hospital Workgroup, the AHA and several other panelists noted a handful of significant process challenges that we believe detract from the MAP’s ability to reach consistent and fair decisions. To be clear, we believe the NQF has made significant improvements to the process over the past few years, including disseminating materials well in advance of the meeting and providing overviews of the quality reporting programs prior to the introduction of the measures under consideration for discussion. However, we believe other processes must be clarified. We look forward to working with NQF to ameliorate these process issues to ensure that the legitimacy of decisions reached by the MAP is not undermined in the future. One pervasive issue that arose with nearly every measure under consideration was the lack of clarity around the voting process. While NQF staff have attempted to elucidate the voting categories, thresholds for motion passage, and abstention policies, we worry that overall the voting process is inconsistent and frequently does not truly reflect group consensus. Another concern was the role of the measure developer in the discussion. Measure developers were invited to provide comments on the measure under development, even justifying or qualifying measure specifications when questioned by MAP panelists, but without providing any supporting documentation. This addition was confusing for discussants who were accustomed to unbiased debate on the measures. Finally, there seemed to be general disagreement about what level of detail was appropriate to discuss. While the measure developers introduced each measure under consideration with a lengthy presentation on their analyses and details of measure design, discussants were discouraged from talking about these same details regarding scientific acceptability and were told that these were deliberations reserved for NQF measure endorsement standing committees. The fundamental issue around what should and should not be germane to the discussion must be resolved in order for the MAP to operate as intended. The AHA is committed to working with the NQF to help address these issues. We recognize the MAP process is complex and inherently fraught with competing priorities and related challenges. Because of the important nature of NQF and the MAP’s work, however, these issues do need to be addressed to uphold the validity of the group’s decisions. (Submitted by: American Hospital Association)
  
  
• First, the deliberation process ought to include discussion evaluating potential new measures in context of existing program measure sets, i.e., how does X new measure/s round out the existing set. There ought to be related discussion, when relevant, how new measures cross, or should cross, payment silos. E.g., why are ACO and MA (excluding MA Part D coverage) measures different or not the same? Moreover, quality performance measurement is about improving value, or outcomes achieved relative to spending. However, neither CMS nor NQF make any effort to correlate the two. Instead, Medicare rewards comparatively lower spending independent of quality. Both the MSSP and the hospital VBP programs reward comparatively lower spending providers despite their having comparatively worse quality. This, perversely, incents providers to disregard quality improvement. First, as a generic comment, we're pleased seven of the 32 MAP measures are PROMs. Again, more effort should be made to standardize measures, when appropriate across silos, e.g., OQR, IQR, ASCQR, et al. Similarly, there ought to be greater uniformity in re: criteria concerning measurement removal. Re: the MIPS and MSSP report for comment we support the development of cost measures, specifically CMS' on going effort to produce episode-based cost measures. We are however concerned these be appropriately risk adjusted, that is moreover they take into account functional status limitations (about which CMS' Drs. Yong and Long are aware). As NQF staff are likely aware beneficiaries with functional status limitations, regardless of the # of chronic conditions, disproportionately account for Medicare spending compared to beneficiaries with X number of chronic conditions and no functional status limitations. Concerning MSSP measures, as noted under my "workgroup deliberations" comments, above, we urge the MAP to emphasize the importance of quality performance, i.e., ACO providers ought to be rewarded, as MA plans are, for superior quality performance. Currently the MSSP/ACO program is "penalty only" in re: quality measurement performance scoring. Concerning MIPS measures, we fear, based on PQRS historical performance, the program will similarly become a race to the bottom, i.e., MIPS providers will largely report the same small subset of MIPS measures (all the more reason why MIPS quality measures should be increasingly claims-based). We support effort/movement toward standardizing measurement across the SNF, IRF, LTCH and HH settings. (Submitted by: AMGA)
  
  
• • PhRMA appreciates the MAP’s ongoing work to review and prioritize measures for inclusion and recommendations on removal criteria in federal health care programs. As CMS refines existing programs and implements new quality reporting requirements, the MAP process provides an important opportunity for stakeholder input into the measures under consideration as well as CMS’ programmatic goals and needs. As a multi-stakeholder body with considerable expertise in quality measurement and improvement, MAP is well positioned to evaluate and make comprehensive recommendations to CMS about the future direction of program measure sets. The holistic review of program measure sets is increasingly important as CMS works to align measures for meaningful quality improvement while balancing measurement burden for providers. Efforts like the Meaningful Measures Framework can serve as a complement to MAP’s work to inform decisions for measure prioritization, inclusion, or removal. We encourage MAP to assert a more prominent role in this process moving forward. • Support for more actionable measures: PhRMA agrees with the MAP’s approach towards measure types that are more actionable for clinicians, including cost and composite measures. We believe well-constructed episode level resource measures can provide more meaningful data for providers than total spending per beneficiary measures. It will continue to be important for episode-based cost measures to be paired with meaningful measures of care quality. PhRMA also supports the addition of composite measures that provide more holistic view of care provided for a condition, such as diabetes care. In the future, we hope to see more of these types of measures, including those that support improved population health, such as adult vaccinations. We also appreciate the MAP’s discussion on the use of composites to capture multiple appropriate use measures, such as the pairing of the appropriate use of a test with an effective screening measure, to provide greater balance and safeguard against care stinting. • Linking cost measures with quality, appropriate risk adjustment: development of sound cost measures and appropriately linking them to relevant quality measures is particularly important considering the ways that these measures shape incentives for value-based payment programs. CMS should ensure that when cost measures are used, they are appropriately balanced with robust measures of quality and patient outcomes, and accurately capture the cost of care delivered. We caution that application of raw cost measures in the absence of meaningfully linked quality data or appropriate context could result in reduced provision of needed care and decreased adoption of new medically beneficial treatments in an effort to stem costs. The MAP also noted that cost measures must apply appropriate risk adjustment, and take clinical and social risk factors under consideration if appropriate, to avoid potential care stinting. Cost measures, as with any other type of measure, should not be developed in isolation and without accountability. Therefore, we support MAP’s recommendation that the NQF Cost and Effiency Standing Committee review the measures to ensure appropriate clinical and societal risk adjustment, exclusions and attribution methodology. As the agency works to maintain and improve these measure sets, we encourage reliance on measures that are well-grounded in current best evidence and have attained stakeholder consensus endorsement. Thus, we encourage CMS to seek NQF endorsement for cost measures. • Patient-Centered Measures: PhRMA appreciates the recognition by the MAP work groups that patient-centered measures are critical to assessing quality of care, driving quality improvement and should continue to be a priority in measure development and inclusion. However, significant gaps persist in the availability and adoption of patient-centered measures in CMS quality and payment programs. A recent scan of patient-reported outcomes performance measures (PRO-PMs) conducted by Discern Health in 2017 found that the vast majority of PRO-PMs assess patient experience of care, and gaps remain in measure areas such as functional status, symptom burden, and shared decision making. Additionally, use of PRO-PMs in CMS programs is still in very early stages, with use in less than 20 percent of quality and payment programs. Identifying these gaps and working with measure developers and other stakeholders to address them must continue to be a high priority. In addition to identifying measure gaps, MAP should consider mechanisms to encourage broader adoption of valid, evidence-based measures that capture the patient’s voice. • Measures that address public health needs: PhRMA agrees with MAP’s assessment of the role of public health in addressing opioid use disorder, and that this is a priority gap area that should be addressed. We support an appropriately developed mesasure on Continutiy of Pharmacotherapy for Opioid Use Disorder that is properly tested and attributed at the clinician/clinician group level and receives endorsement by a multistakeholder consensus-based organization. (Submitted by: PhRMA)
  
  
• • Overall, BI applauds MAP’s efforts to align its recommendations with the Centers for Medicare & Medicaid Services’ (CMS) new Meaningful Measures Initiative. We support CMS’ efforts to minimize provider burden and ensure that measures included in the Agency’s programs are most vital to assessing and improving patient outcomes; however, we are concerned that critical measure gaps may continue to persist. We therefore urge MAP to take a more active and targeted approach to address these existing gaps. In our comments below, we provide comments on MAP’s draft recommendations, but also highlight the need for measures that promote quality across the chronic obstructive pulmonary disease (COPD) care continuum; address complex chronic and comorbid conditions, such as measures focused solely on patients with comorbid type 2 diabetes and cardiovascular disease, as well as primary prevention measures for stroke; and care quality and outcome measures for behavioral health. We encourage MAP to include in its recommendations to CMS guidance focused on fostering measure development in areas that address known gaps in care. • BI appreciates the opportunity to submit comments that inform the MAP Hospital Workgroup’s review of existing measure sets. We applaud MAPs support for MUC17-178: 30-Day Unplanned Readmissions for Cancer Patients for rulemaking. This NQF-endorsed measure fills a current gap in the PPS-Exempt Cancer Hospital Quality Reporting Program by addressing unplanned readmissions of cancer patients. We also agree with MAP’s conditional support for the MUC17-195: Hospital-wide All-Cause Risk Standardized Mortality Measure and MUC17-196: Hybrid Hospital-Wide All-Cause Risk Standardized Mortality Measure proposed for inclusion in the Hospital Inpatient Quality Reporting Program and Medicare and Medicaid EHR Incentive Program for Eligible Hospitals and Critical Access Hospitals. Hospital-wide mortality has been the focus of several previous quality reporting initiatives in the U.S. and other countries, [1] and while existing mortality measures may have contributed to national declines in hospital mortality rates [2], they do not currently allow for measurement of a hospital’s broader performance, nor do they meaningfully capture performance for smaller volume hospitals. BI supports inclusion of these measures in CMS programs, as we believe they can help address these identified reporting challenges; however, we also recognize MAP’s number of potential concerns, which should be vetted through the NQF endorsement process. While we agree with MAP’s conditional support of these measures, we urge MAP to further clarify its recommendations for use and program inclusion in the final report to CMS. • BI would like to call to MAP’s attention the significant gaps in outcomes and care delivery measures that exist for multiple chronic conditions, including but not limited to, mental health, COPD, and diabetes. These are particularly important gap areas, as healthcare for patients with complex chronic and comorbid conditions extends beyond one setting of care. Many patients with multiple chronic conditions see multiple providers spanning several care settings. In a 2012 analysis, CMS identified that patients with comorbidities including asthma and COPD were associated with up to 7 times higher costs than the average spending for Medicare beneficiaries. Additionally, patients with comorbid chronic conditions such as asthma, diabetes or COPD consume a significantly higher portion of healthcare resources. [3] We therefore believe there is an immediate need for MAP to focus on assessing and making recommendations for measures that address this gap area across all care settings, which can help ensure patients receive coordinated and continuous care, particularly since most Medicare patients suffer from co-occurring conditions. • BI notes a particular absence in measures for individuals with comorbid diabetes and cardiovascular disease. [4] According to the 2017 National Diabetes Statistics Report released by the Centers for Disease Control and Prevention (CDC), in 2014, 1.5 million patients with diabetes (or 70.4 per 1,000 persons with diabetes) were discharged from a hospital with major cardiovascular disease. [5] A 2014 CMS report on Medicare/Medicaid dual-eligibles, 45% of patients who had a heart condition were also diagnosed with diabetes. [6] Similarly, cardiovascular disease accounts for 28% of the costs of treating diabetes and associated complications. [7] At a national level, the American Diabetes Association reports that in 2012, the total estimated direct medical costs for diabetes care was $176 billion. At this time, no existing measure in any of the CMS quality reporting programs explicitly reports on identification or treatment of patients with these comorbid conditions. We encourage MAP to focus on addressing this important measure gap. • Along these lines, BI also notes a lack of measures focused specifically on outcomes related to cardiovascular mortality for individuals with comorbid diabetes and cardiovascular disease. Evidence shows that cardiovascular disease is highly prevalent in patients with diabetes, is associated with high rates of mortality, and is a source of high financial burden to patients, caregivers, and the health care system at large. This is especially evident in the diabetes patient population where incidence of myocardial infarction (MI) is eight times higher than the general population. Additionally, patients with diabetes are also 2.7 times more likely to experience death related to coronary heart disease with a prior MI. [8] Although this gap area was not discussed in the Hospital Workgroup draft report, it is crucial that more focus is placed on developing measures that help ensure patients receive care that comprehensively meets their needs, both during and beyond the hospital stay. • BI notes a similar gap in measures for care and outcomes for behavioral health. Behavioral health care has historically been fragmented and siloed, despite clinical guidance indicating the need for integration of behavioral health services across settings of care. [9] This is further exacerbated by the significant gaps in quality that have been observed not only in the treatment and management of behavioral health at psychiatric facilities but also outside of traditional behavioral health settings such as acute, post-acute and ambulatory care. Currently, CMS’ Inpatient Quality Reporting (IQR) Program does not include any behavioral health measures despite clear evidence, including a 2006 Institute of Medicine (IOM) report, emphasizing the high prevalence, costs, patient and system burden of mental health disorders. [10] This inability to measure the quality of behavioral health care delivery significantly hinders efforts to identify and improve care that is not aligned with clinical guidance. [11] As such, BI supports the potential future inclusion of measures addressing appropriate treatment and care coordination for behavioral health conditions in the Hospital IQR program. BI specifically recommends inclusion of measures related to serious psychiatric behavioral health disorders such as schizophrenia and bipolar disorder. • Finally, BI recommends CMS consider adopting quality measures that assess continuity of care across all care settings. Future measures should go beyond measures of readmissions and mortality and by further encouraging providers to improve quality of care and reduce costs by implementing more effective care transitions. [12] Recent studies have shown that more effective care transitions have reduced hospital readmissions. [13] [1] Health and Social Care Information Centre. Summary Hospital-Level Mortality Indicator. 2015. Accessed January 3, 2018. [2] Suter LG, Li SX, Grady JN, et al. National patterns of risk-standardized mortality and readmission after hospitalization for acute myocardial infarction, heart failure, and pneumonia: update on publicly reported outcomes measures based on the 2013 release. Journal of general internal medicine. 2014;29(10):1333-1340. [3] Centers for Medicare and Medicaid Services. Chronic Conditions among Medicare Beneficiaries, Chartbook, 2012 Edition. Baltimore, MD. 2012 [4] American Diabetes Association. Economic Costs of Diabetes in the U.S. in 2012. Diabetes Care. 2013; DC_122625. DOI:10.2337/dc12-2625. [5] Centers for Disease Control and Prevention. National Diabetes Statistics Report, 2017: Estimates of Diabetes and Its Burden in the United States. Accessed January 3, 2017 [6] Physical and Mental Health Condition Prevalence and Comorbidity among Fee-for-Service Medicare-Medicaid Enrollees. Centers for Medicare & Medicaid Services. Published September, 2014. Accessed January 3, 2018. [7] Sander S, et al. Poster presented at American Academy of Managed Care Nexus; October 3-6, 2016; National Harbor, MD. [8] Juutilainen A, Lehto S, Rönnemaa T, Pyörälä K, Laakso M. Type 2 diabetes as a "coronary heart disease equivalent": an 18-year prospective population-based study in Finnish subjects. Diabetes Care. 2005;28(12):2901-7. [9] Fontanella CA, Guada J, Phillips G. Individual and contextual-level factors associated with continuity of care for adults with schizophrenia. Adm Policy Ment Health. 2014; 41(5):572-87. [10] Institute of Medicine. Improving the quality of health care for mental and substance-use conditions. Washington (DC): National Academies Press; 2006. [11] Pincus HA, Spaeth-Rublee B, Watkins KE. The Case For Measuring Quality In Mental Health And Substance Abuse Care. Health Aff. 2011; 30:4730-736. [12] Agency for Healthcare Research and Quality. Seamless Care: Safe Patient Transitions from Hospital to Home. NTIS 200520. . Published February 2005. Accessed January 3, 2018. [13] McIlvennan CK, Eapen ZJ, Allen LA. Hospital readmissions reduction program. Circulation. 2015;131(20):1796–1803. • BI appreciates the opportunity to submit comments that inform the MAP Clinician Workgroup’s review of existing measure sets. BI commends the Clinician Workgroup in acknowledging both the need for meaningful patient reported outcome measures, composite measures that examine completeness of care, as well as outcome measures in the Merit-Based Incentive Payment System (MIPS) and Medicare Shared Savings Plan (MSSP). BI also recognizes the difficulty of developing measures that assess completeness of care, patient reported outcomes and outcome measures targeted for the clinician setting. However, BI urges the MAP Coordinating Committee to continue to address persistent gaps in quality measurement, particularly for patients with multiple chronic conditions as well as patients with behavioral health disorders, many of whom also suffer from comorbid chronic conditions. • BI supports MAP’s decision to conditionally support MUC17-215: Diabetes A1c Control (< 8.0), the inverse measure for the existing Diabetes Poor Control (>9.0) measure, as part of the MIPS and MSSP. According to the most recent standards from the American Diabetes Association (ADA), less stringent A1C goals (such as <8% [64 mmol/mol]) may be appropriate for select patients, including patients with a history of severe hypoglycemia, limited life expectancy, advanced microvascular or macrovascular complications, extensive comorbid conditions, or patients with long-standing diabetes where the goal is difficult to achieve despite diabetes self-management education, appropriate glucose monitoring, and effective doses of multiple glucose-lowering agents including insulin. [1] Given the alignment of this measure with new evidence-based recommendations, BI supports this measure. • BI also supports MAP’s decision to conditionally support composite measure MUC17-215: Optimal Diabetes Care as part of MIPS and MSSP. The decision to support this composite is an important step towards creating incentives for complete high-quality care focused on evaluating not just blood sugar level, but overall heart health. BI acknowledges MAP’s recognition of the utility of the individual subcomponents of this composite to drive quality improvement. BI also commends MAP for recognizing the value of providing clinicians with the choice to either report the MUC17-215: Diabetes A1c Control (<8.0%) measure as part of a composite to assess completeness of care, or as an individual measure, ensuring they have the option to focus on this important issue regardless of the reporting option they select. BI acknowledges MAP’s continued focus on refining and updating quality measures for patients with diabetes; however, a significant gap remains for patients with multiple chronic conditions, specifically those with comorbid diabetes and cardiovascular disease. [2] According to the 2017 National Diabetes Statistics Report released by the Centers for Disease Control and Prevention (CDC), in 2014, 1.5 million patients with diabetes (or 70.4 per 1,000 persons with diabetes) were discharged from a hospital with major cardiovascular disease. [3] According to a 2014 Centers for Medicare & Medicaid Services (CMS) report on Medicare/Medicaid dual-eligibles, 45% of patients who had a heart condition were also diagnosed with diabetes. [4] Similarly, cardiovascular disease accounts for 28% of the costs of treating diabetes and associated complications. [5] At a national level, the American Diabetes Association reports that in 2012, the total estimated direct medical costs for diabetes care was $176 billion. At this time, no existing measure in any of the CMS quality reporting programs explicitly reports on identification or treatment of patients with these comorbid conditions. We therefore encourage MAP Coordinating Committee to focus on addressing this important measure gap in their deliberations. • BI also urges MAP to address significant measure gaps for chronic obstructive pulmonary disease (COPD), which is the third leading cause of death in the United States, affecting 16 million Americans and millions more who remain undiagnosed. [6] Furthermore, the U.S. spent more than $32 billion on COPD-related patient care in 2010, and those costs are projected to increase to $49 billion by 2020. [7] BI is concerned that CMS programs are lacking appropriate measures to promote high quality COPD care across the care continuum. For example, CMS recently designated the quality measure “COPD: Long-Acting Inhaled Bronchodilator Therapy” (Quality Measure ID: 52)” as being topped-out; however, future removal of this measure will leave only one remaining COPD-related measure in MIPS (COPD: Spirometry Evaluation). [8] We therefore encourage MAP to consider and support meaningful, evidence-based measures that will fill this gap. In 2017, the National Heart, Blood, and Lung Institute (NHLBI), at the request of Congress, released the first COPD National Action Plan, which focuses on driving actionable results to prevent COPD and ease the burden for those managing this disease.[9] Under Goal 5 of this Action Plan (“Translate national policy, educational, and program recommendations into legislative, research, and public health care actions”), NHLBI explicitly notes an aim to focus on ensuring “adoption of [existing and still-developing performance-quality measures that are informed by scientific evidence and input from various COPD stakeholders] to improve COPD detection, care, and treatment in health care settings and payer programs.” The Action Plan also encourages “Building upon existing COPD guidelines, like those available from the American Thoracic Society (ATS, www.thoracic.org/statements/copd.php) and the Global Initiative for Chronic Lung Disease (GOLD, www.goldcopd.org), create clinical practice guidelines that set consistent national standards for identifying people at risk for COPD as well as diagnosing, caring for, and treating people with COPD across the care continuum.” In addition to supporting evidence-based measures that address both the management of treatment as well as outcomes for people at risk for and living with COPD, BI also encourages MAP to facilitate alignment across federal initiatives such as these. • On a broader level, BI recognizes that persistent gaps in outcomes related to general approaches to medication use for the long-term management of chronic disease across a multitude of clinical areas, including but not limited to, mental health, COPD, and diabetes. In a 2012 analysis, CMS identified that patients with comorbidities including asthma and COPD were associated with up to 7 times higher costs than the average spending for Medicare beneficiaries. Additionally, patients with comorbid chronic conditions such as asthma, diabetes or COPD consume a significantly higher portion of healthcare resources. [10] BI believes there is an immediate need to emphasize the development and testing of measures that can help ensure care for these patients is continuous and not siloed, particularly since the majority of Medicare patients suffer from co-occurring conditions. • BI notes a similar gap in measures focused on care and outcomes for behavioral health. Behavioral health care in the U.S. has historically been fragmented and siloed despite clinical guidance indicating the need for integration of behavioral health services across settings of care. [11] These challenges are further exacerbated by significant gaps in quality that have been observed in the treatment and management of behavioral health. Despite expansion of services in behavioral health by CMS under the recent Medicare Physician Fee Schedule, measure gaps in this area still exist, especially with regard to care coordination and medication management of complex treatment regimens. These issues are particularly relevant to this patient population, as many individuals with behavioral health disorders also suffer from comorbidities; it is estimated that over 90% of individuals with schizophrenia have a comorbid condition. [12] [1] American Diabetes Association. Diabetes Care 2015 Jan; 38(Supplement 1): S33-S40. https://doi.org/10.2337/dc15-S009 [2] American Diabetes Association. Economic Costs of Diabetes in the U.S. in 2012. Diabetes Care. 2013; DC_122625. DOI:10.2337/dc12-2625. [3] Centers for Disease Control and Prevention. National Diabetes Statistics Report, 2017: Estimates of Diabetes and Its Burden in the United States. Accessed January 3, 2017 [4] Physical and Mental Health Condition Prevalence and Comorbidity among Fee-for-Service Medicare-Medicaid Enrollees. Centers for Medicare & Medicaid Services. Published September, 2014. Accessed January 9, 2017. [5] Sander S, et al. Poster presented at American Academy of Managed Care Nexus; October 3-6, 2016; National Harbor, MD. [6] Centers for Disease Control and Prevention. National Center for Health Statistics. 2017. Accessed January 4, 2018 [7] Earl S. Ford, Louise B. Murphy, Olga Khavjou, Wayne H. Giles, James B. Holt, and Janet B. Croft., “Total and state specific medical and absenteeism costs of COPD among adults aged = 18 years in the United States for 2010 and projections through 2020, Chest, 147 (1), pp. 31 45 (January 2015) [8] Centers for Medicare and Medicaid Services. Quality Payment Program Year 2: Final Rule Overview. 2017. https://www.cms.gov/Medicare/Quality-Payment-Program/resource-library/QPP-Year-2-Final-Rule-Fact-Sheet.pdf. Accessed January 6, 2018 [9] National Heart, Lung, and Blood Institute. National COPD Action Plan. 2017. [10] Centers for Medicare and Medicaid Services. Chronic Conditions among Medicare Beneficiaries, Chartbook, 2012 Edition. Baltimore, MD. 2012 [11] Fontanella CA, Guada J, Phillips G. Individual and contextual-level factors associated with continuity of care for adults with schizophrenia. Adm Policy Ment Health. 2014; 41(5):572-87. [12] National Council for Community Behavioral Healthcare. Advancing Standards of Care for People with Schizophrenia. Available at: http://www.thenationalcouncil.org/wp-content/uploads/2012/11/Advancing-Care-for-Schizophrenia-Final-Report-1.pdf. • BI appreciates the opportunity to submit comments to inform the Post-Acute Care/Long-Term Care (PAC/LTC) Workgroup’s review of existing measure sets. BI commends MAP for support of patient reported outcome measures focused on driving increased patient satisfaction; however, BI urges the MAP Coordinating Committee to continue working to address persistent quality measure gaps, particularly with regard to patients with multiple chronic conditions and with regard to care delivery and patient outcomes in the behavioral health space. Specifically, older adult Medicare patients who face a higher prevalence of comorbid conditions are also the most frequent users of post-acute care services. Complex treatment protocols for this patient population are continued by post-acute care providers after discharge from the inpatient setting. • BI notes an absence in measures for individuals with comorbid diabetes and cardiovascular disease. [1] According to the 2017 National Diabetes Statistics Report released by the Centers for Disease Control and Prevention (CDC) in 2014, 1.5 million patients with diabetes (or 70.4 per 1,000 persons with diabetes) were discharged from a hospital with major cardiovascular disease [2]. According to a 2014 Centers for Medicare & Medicaid Services (CMS) report on Medicare/Medicaid dual-eligibles, 45% of patients who had a heart condition were also diagnosed with diabetes. [3] Similarly, cardiovascular disease accounts for 28% of the costs of treating diabetes and associated complications. [4] At a national level, the American Diabetes Association reports that in 2012, the total estimated direct medical costs for diabetes care was $176 billion. At this time, no existing measure in use in any of the CMS quality reporting programs explicitly reports on identification or treatment of patients with these comorbid conditions. We encourage the MAP to encourage CMS to foster measure development in this area and close this critical gap in quality of care for patients burdened with this comorbidity • On a broader level, BI recognizes that gaps continue to exist in outcomes related to general approaches to medication use for the long-term management of chronic disease across a multitude of clinical areas, including but not limited to, mental health, chronic obstructive pulmonary disease (COPD), and diabetes. This is a particularly important gap area, as healthcare for patients with complex chronic and comorbid conditions extends beyond care received in a facility and has post-discharge implications. In a 2012 analysis, CMS identified that patients with comorbidities including asthma and COPD were associated with up to 7 times higher costs than the average spending for Medicare beneficiaries. Additionally, patients with comorbid chronic conditions such as asthma, diabetes or COPD consume a significantly higher portion of healthcare resources. [5] In 2017, the National Heart, Blood, and Lung Institute (NHLBI), at the request of Congress, released the first COPD National Action Plan, which focuses on driving actionable results to prevent COPD and ease the burden for those managing this disease.[6] Under Goal 5 of this Action Plan (“Translate national policy, educational, and program recommendations into legislative, research, and public health care actions”), NHLBI explicitly notes an aim to focus on ensuring “adoption of [existing and still-developing performance-quality measures that are informed by scientific evidence and input from various COPD stakeholders] to improve COPD detection, care, and treatment in health care settings and payer programs.” The Action Plan also encourages “Building upon existing COPD guidelines, like those available from the American Thoracic Society (ATS, www.thoracic.org/statements/copd.php) and the Global Initiative for Chronic Lung Disease (GOLD, www.goldcopd.org), create clinical practice guidelines that set consistent national standards for identifying people at risk for COPD as well as diagnosing, caring for, and treating people with COPD across the care continuum.” BI therefore believes there is an immediate need to emphasize the development and testing of measures that can help ensure care for these patients is continuous and not siloed, particularly since the majority of Medicare patients suffer from co-occurring conditions. • Similarly, BI also recommends the PAC/LTC Workgroup highlight in its report the gaps in care-related care coordination and medication management of complex treatment regimens for behavioral health care. Patients with mental health conditions are more likely to also suffer from a comorbid health condition such as diabetes, stroke or lung disease. [7] Over 90% of individuals with schizophrenia are estimated to have a comorbid condition. [8] As stated in previous comments, patients with comorbidities often require complex medication regimens, which need to be monitored closely, and adherence is a continued concern in this population. As such, future measures should go beyond measures of readmissions and mortality and instead incentivize providers to improve quality of care and reduce costs by implementing more effective care transitions. [9] BI supports the adoption of such measures in not only the inpatient setting, but also in post-acute and long-term care settings, such as in the HHRP and SNF QRP. Continuing to break down the barrier between physical and mental health is an important step to creating cohesive patient care in today’s healthcare system. Therefore, as the science of measurement advances, MAP should prioritize how best to comprehensively assess performance on these important quality improvement targets. [1] American Diabetes Association. Economic Costs of Diabetes in the U.S. in 2012. Diabetes Care. 2013; DC_122625. DOI:10.2337/dc12-2625. [2] Centers for Disease Control and Prevention. National Diabetes Statistics Report, 2017: Estimates of Diabetes and Its Burden in the United States. Accessed January 3, 2017 [3] Physical and Mental Health Condition Prevalence and Comorbidity among Fee-for-Service Medicare-Medicaid Enrollees. Centers for Medicare & Medicaid Services. Published September, 2014. Accessed January 9, 2017. [4] Sander S, et al. Poster presented at American Academy of Managed Care Nexus; October 3-6, 2016; National Harbor, MD. [5] Centers for Medicare and Medicaid Services. Chronic Conditions among Medicare Beneficiaries, Chartbook, 2012 Edition. Baltimore, MD. 2012. [6] National Heart, Lung, and Blood Institute. National COPD Action Plan. 2017. https://www.nhlbi.nih.gov/health-topics/education-and-awareness/COPD-national-action-plan. Accessed January 9, 2017. [7] Physical and Mental Health Condition Prevalence and Comorbidity among Fee-for-Service Medicare-Medicaid Enrollees. Centers for Medicare & Medicaid Services. Published September, 2014. Accessed January 9, 2017 [8] National Council for Community Behavioral Healthcare. Advancing Standards of Care for People with Schizophrenia. Available at: http://www.thenationalcouncil.org/wp-content/uploads/2012/11/Advancing-Care-for-Schizophrenia-Final-Report-1.pdf. [9] Agency for Healthcare Research and Quality. Seamless Care: Safe Patient Transitions from Hospital to Home. NTIS 200520. Published February 2005. Accessed June 10, 2016. (Submitted by: Boehringer Ingelheim Pharmaceuticals, Inc. )
  
  
• MUC 17 173, I think it important to note that women who cover head to toe might need a DXA screen earlier like someone who has a fracture at 50. MUC 17 256 Cost burden is a barrier as well as crowded housing situation to having colonoscopies, this should be taken into account. MUC 17-169, 170, 177 PROs Thanx! MUC 17 367 HIV screening I think is important for everyone especially with needle use increase today, yes there still is a stigma attached to the testing in certain groups. MUC 17 194 Quality Vascular Care yes! MUC 17 258 CoreQ (where are PROs?) Important and has been recognizd as a priority and a gap & should be built to be as inclsuive to all patients as possible. MUC 17 176 Dialysys RX I consider this measure very important! Agree with comments as who might qualify for RX reconcilliation work. MUC 17 241 -245 Appears a health disparities issue might be an issue here. MUC 17 223, I do think there is over use in back imaging though do know it can be beneficial as well. **recommendations regarding specific measures on where MAP cautioned about the potential stinting of care, precision of cohort size, and too where the measure should be vetted through the endorsement process, and specifically that the measure has appropriate clinical and social risk factors in its risk adjustment model and addressing those necessary exclusions. Referrals to cost and resource committees on specific measures are important as well MUC 17 365 I am curious why "simple pneumonia" would be a case for hospitalization- I do understand hospitalization for complex care patients and or for complicated pneumonia cases. MUC 17 181 is listed 2x. MUC 17 215 mentions two different possible competing measures ACO#7 and or QPP #1 I am assuming these will be reconciled I see the diabetes measures as important for quality improvement. MUC 17 233 I think is important to know frequency of when ambulatory surgeries end up to hospitalizations MUC 17-195 -196 Seems to be needed though seems some work needs to be done, small and or safety net hospitals/trauma centers/ transitions how to measure fairly across the board? MUC 17-210 In resubmission perhaps the measure should include all Opioid MAT treatments-methadone, buprenorphine, not only Suboxone? MUC 17 178, I see this as an important measure though am hoping hospitals do not get docked for under 30 day readmission, METS can happen, unexpected side effects from cancer treatments that cause admission etc. MUC 17 363 appears to need more work, MUC 17 359 seems to be an effective measure for clinical care lower costs/ risks benefits I am curious about ,MUC 17 139 I agree is important opioid MAT or pharmacotherapy. Agree MUUC 17 310 Zoster vaccination and adult vaccinations is a moving target at the moment. (Submitted by: Hassanah Consulting)
  
  

• The American Medical Association (AMA) requests the recommendation on this measure be changed to “Refine and Resubmit.” As noted in the report and MAP rationale, this measure was recently endorsed for use at the health plan level. It is not currently developed and tested at the clinician or practice level and the AMA is troubled to see this measure receive “Conditional Support” with no information on how it would perform when assessing physician performance. As noted by the MAP Clinician Workgroup, several questions around appropriate attribution, refinement of specifications, and the reliability and validity are not yet answered and the measure specifications and results may be very different when applied at the clinician or practice level. Because of these questions, the AMA recommends that the MAP decision be changed to “Refine and Resubmit.” (Submitted by: American Medical Association)
  
  

• The MN Community Measures seem to specify improvement in ODI specifically, but we would like to leave options open for practices who would like to use other validated disability / pain measures (such as Promis, HAQ, etc). We recommend that the wording change to “improvement on a validated pain or disability patient reported outcome measure.” (Submitted by: AANS)
  
  

• We strongly support the above-referenced measure looking at functional change after total knee replacement. This will help to focus provider efforts on achieving optimal short-term outcomes for patients. The measure will encourage surgeons to select implants that best fit the capabilities of their patients, while encouraging alignment among post-acute providers to deliver rehabilitation therapy to support patients gaining optimal functionality post-surgery. Similarly, we urge CMS to consider comparable metrics for measuring patient functional status after total hip replacement. (Submitted by: Smith & Nephew)
  
  
• AdvaMed strongly supports the above-referenced measure looking at functional change after total knee arthroplasty (TKA). This will focus more attention on functional scores, and emphasize provider efforts on achieving optimal near-term outcomes for patients. The Oxford Knee Score is a strong patient-reported outcome tool. The measure will encourage surgeons to select implants that best fit the capabilities of their patients, while encouraging alignment among all post-acute providers to provide rehabilitation and therapy that helps patients gain optimal functionality post-surgery. Similarly, we would urge CMS to consider comparable metrics for measuring patient functional status after total hip replacement (THA). (Submitted by: AdvaMed)
  
  
• We support the MAP's recommendation for this measure. (Submitted by: Johns Hopkins Armstrong Institute)
  
  

• The MN Community Measures seem to specify improvement in ODI specifically, but we would like to leave options open for practices who would like to use other validated disability / pain measures (such as Promis, HAQ, etc). We recommend that the wording change to “improvement on a validated pain or disability patient reported outcome measure.” (Submitted by: AANS)
  
  

• MUC 17-176—Medication Reconciliation for Patients Receiving Care at Dialysis Facilities KCP concurs with the Workgroup’s recommendation to support MUC 17-176 (NQF 2988), which was developed by the Kidney Care Quality Alliance (KCQA) and is NQF-endorsed. (Submitted by: Kidney Care Partners)
  
  
• Support MAP recommendation. This measure has broad support among stakeholders; addresses patient safety and care coordination. (Submitted by: Children's Hospital Association)
  
  

• The MN Community Measures seem to specify improvement in ODI specifically, but we would like to leave options open for practices who would like to use other validated disability / pain measures (such as Promis, HAQ, etc). We recommend that the wording change to “improvement on a validated pain or disability patient reported outcome measure.” (Submitted by: AANS)
  
  
• The MN Community Measures seem to specify improvement in ODI specifically, but we would like to leave options open for practices who would like to use other validated disability / pain measures (such as Promis, HAQ, etc). We recommend that the wording change to “improvement on a validated pain or disability patient reported outcome measure.” (Submitted by: AANS)
  
  

• We thank CMS, NQF, and the clinicians involved for the opportunity to provide input and feedback, as well as the transparency, throughout the MAP process. We strongly support the inclusion of MUC17-178 (NQF #3188) (30-Day Unplanned Readmissions for Cancer Patients) in the PPS-Exempt Cancer Hospital Quality Reporting Program. This NQF endorsed measure has been shown to be both valid and reliable, is already in use in several PPS-Exempt Cancer Hospitals, and helps hospitals identify potential areas for improvement. This measure addresses a current performance measurement gap in the PCHQR Program. Lastly, as the measure is claims-based, it should not lead to an unwarranted data burden for the hospitals in the Program. Thank you for the opportunity to provide input during this process. None None (Submitted by: Alliance of Dedicated Cancer Centers)
  
  
• Amgen does not agree with MAP’s recommendation of “support for rulemaking” of MUC17-178 for inclusion in the PPS-Exempt Cancer Hospital Quality Reporting (PCHQR) Program. Instead, we recommend that this measure not be included in the program. While Amgen supports quality measures aimed at reducing avoidable hospital admissions, we cannot support MAP’s recommendation of “support for rulemaking” of MUC17-178 for inclusion in the PCHQR Program due to problematic issues regarding the measure exclusions. Although we agree that this measure does address a gap in the PCHQR Program, there are two specific exclusion criteria that require changes to better reflect appropriate care. First, the exclusion of primary claim diagnosis code of metastatic disease appears appropriate for the rationale provided. However, although metastatic disease may be a “proxy for disease progression” that might require readmissions, there are conditions where the best quality of care could be home care, outpatient care, or hospice care, or even a single admission (not multiple admissions). In these cases, poor clinical care would be reflected in readmissions and should be accounted for in this exclusion. A second exclusion requiring revisions is the exclusion of patients with a primary claim diagnosis code of chemotherapy or radiation encounter. We are specifically concerned about the “radiation encounter” aspect of the exclusion. Although some radiation therapy requires admission to an inpatient setting, most can be performed in the outpatient setting. Additionally, a “radiation encounter” that includes radiation to the bone might lead to a skeletal-related event (SRE) that could’ve been prevented with appropriate care such as administration of an appropriate bone-targeting agent and not require readmission. Again, poor clinical care would be reflected with a readmission and should be accounted for in this exclusion. This is an important quality measure that addresses a significant measure gap, but revisions to the exclusion criteria are needed before implementation in the PCHQR Program. (Submitted by: Amgen)
  
  
• Support MAP recommendation. (Submitted by: Children's Hospital Association)
  
  

• The American Medical Association (AMA) requests that an additional condition be placed on this measure for the MIPS and MSSP programs. Specifically, the condition would be that the measure must be risk-adjusted or stratified to enable fair and valid comparisons across physicians. As we expressed in our previous comments, the AMA is extremely concerned that comparisons of physician performance using this measure are likely to result in unfair and invalid assessments of the quality of care provided to these patients. Currently, this composite includes measures on intermediate outcomes, which assume that all patients aged 18 to 75 years can reasonable achieve these targets. In programs on which performance and points are assessed based on a physician’s score against his or her peers, it is unreasonable to assume that all patient populations across the United States are homogeneous and that all physicians reporting this measure can achieve similar scores. As a result, we believe that physicians will be unfairly penalized if they have more complex patients and patients will be misinformed about the actual quality of care provided. We urge the MAP to consider adding this condition to this measure. (Submitted by: American Medical Association)
  
  
• We appreciate the meticulous work done by NQF staff and the MAP in evaluating each of the measures under consideration, however we strongly disagree with the decision to conditionally support these cost measures. The measures might be useful if combined with measures looking at the outcomes or appropriateness of care, but without this context, they do not provide actionable information for providers and create a significant risk for unintended adverse consequences for patients. These comments also apply to MUC17-194 (Optimal Vascular Care). As noted by the MAP, these measures should be updated to reflect the new AHA/ACC Hypertension guidelines before being implemented in any CMS programs. In addition, these measures currently specify only exclusions. Given the new hypertension guideline’s emphasis on shared decision-making, and the potential for adverse consequences for patients in whom aggressive BP lowering might not be appropriate, we would strongly suggest that the measure steward also allow exceptions for medical and patient reasons. Finally, there is no evidence for limiting these measures (or MUC17-234 and -215) to patients = 75 years of age. (Submitted by: American Heart Association/American Stroke Association)
  
  

• The American Medical Association (AMA) requests that an additional condition be placed on this measure for the MIPS and MSSP programs. Specifically, the condition would be that the measure must be risk-adjusted or stratified to enable fair and valid comparisons across physicians. As we expressed in our previous comments, the AMA is extremely concerned that comparisons of physician performance using this measure are likely to result in unfair and invalid assessments of the quality of care provided to these patients. Currently, this composite includes measures on intermediate outcomes, which assume that all patients aged 18 to 75 years can reasonable achieve these targets. In programs on which performance and points are assessed based on a physician’s score against his or her peers, it is unreasonable to assume that all patient populations across the United States are homogeneous and that all physicians reporting this measure can achieve similar scores. As a result, we believe that physicians will be unfairly penalized if they have more complex patients and patients will be misinformed about the actual quality of care provided. We urge the MAP to consider adding this condition to this measure. (Submitted by: American Medical Association)
  
  
• Without knowing the trend for the previous two years, an individual test is not predictably valuable for quality assessment, unless you know the mean and standard deviation among all of an individual provider's performance. No recognition of pcp status same same (Submitted by: Family Health Care, P.C.)
  
  
• While we support the MAP’s recommendation, we would encourage the measure developers to consider adjusting the measure so that it is not an all-or-nothing measure. Meeting all of the numerator criteria is not only very difficult, but sometimes out of the physician’s control. It can also be difficult to measure. For example, it is unclear what defines the patient as “not a tobacco user” or how long must the patient be considered “not a tobacco user” for them to meet those criteria (e.g., must this cover the entire measurement period). (Submitted by: Johns Hopkins Armstrong Institute)
  
  
• NAACOS is pleased to submit our comments in response to the MAP Clinician Workgroup’s draft report, “MAP 2018 Considerations for Implementing Measures in Federal Programs: MIPS and MSSP.” NAACOS is the largest association of ACOs, representing more than 4 million beneficiary lives through 300 Medicare Shared Savings Program (MSSP) ACOs, Next Generation, and commercial ACOs. NAACOS is an ACO member-led and member-owned non-profit organization that works on behalf of ACOs across the nation to improve the quality of Medicare delivery, population health and outcomes, and health care cost efficiency. NAACOS and its members are committed to transforming the way healthcare is delivered and paid for. Our members are at the forefront of this transformation effort and have invested significant time and resources to their success, which will ultimately improve care for Medicare beneficiaries and reduce costs for CMS. The draft report notes that the MAP considered three measures for the MSSP: Optimal Diabetes Care; Diabetes A1c Control (<8.0); and Ischemic Vascular Disease Use of Aspirin or Anti-Platelet Medication. The MAP conditionally supported all three measures in its draft report. The MSSP ACO quality measures set already recognizes the clinical prevalence of diabetes by including a composite measure, composed of two individual measures, in the current measure set. While we support the draft report’s reference to conditionally support the MSSP measures outlined with the condition that there are no competing measures in the program, we point out that the current MSSP quality measure set does include competing and duplicative measures. Specifically, the MSSP measure set already includes one composite measure for diabetes (ACO-27, ACO-41) as well as an Ischemic Vascular Disease measure (ACO-30). Therefore, we do not recommend inclusion of the measures contained in this draft report. We thank the workgroup for their efforts and recognition of the importance of avoiding competing and/or duplicative measures within the same measure set. While we support the draft report’s reference to conditionally support the MSSP measures outlined with the condition that there are no competing measures in the program, we point out that the current MSSP quality measure set does include competing and duplicative measures. Specifically, the MSSP measure set already includes one composite measure for diabetes (ACO-27, ACO-41) as well as an Ischemic Vascular Disease measure (ACO-30). Therefore, we do not recommend inclusion of the measures contained in this draft report. We thank the workgroup for their efforts and recognition of the importance of avoiding competing and/or duplicative measures within the same measure set. (Submitted by: NAACOS)
  
  

• The American Medical Association (AMA) requests the recommendation on this measure be changed to “Conditional Support” and that an additional condition be placed on this measure for the MIPS program. We believe that this measure should receive a similar recommendation as MUC17-181, Optimal Diabetes Care. Both measures are currently endorsed but require an update to align the blood pressure values with the new guidelines; yet, MUC17-181 received “Conditional Support” and this measure received “Support for Rulemaking.” These recommendations should be consistent across both measures. In addition, the AMA believes that another condition should be added that the measure must be risk-adjusted or stratified to enable fair and valid comparisons across physicians. As we expressed in our previous comments, the AMA is extremely concerned that comparisons of physician performance using this measure are likely to result in unfair and invalid assessments of the quality of care provided to these patients. Currently, this composite includes measures on intermediate outcomes, which assume that all patients aged 18 to 75 years can reasonable achieve these targets. In programs on which performance and points are assessed based on a physician’s score against his or her peers, it is unreasonable to assume that all patient populations across the United States are homogeneous and that all physicians reporting this measure can achieve similar scores. As a result, we believe that physicians will be unfairly penalized if they have more complex patients and patients will be misinformed about the actual quality of care provided. We urge the MAP to consider adding this condition to this measure. (Submitted by: American Medical Association)
  
  

• The AMA supports the conditions placed on this measure and strongly encourages CMS to address each prior to implementation in any federal programs. (Submitted by: American Medical Association)
  
  
• The risk model for this measure relies on claims data alone which, as noted for the cost measures, lacks the granularity to identify important clinical factors affecting outcomes, especially all-cause mortality. While we believe that the hybrid measure (MU17-196) may perform somewhat better in this regard, given the incorporation of clinical data from EHRs in the risk adjustment model, we do not believe this measure provides actionable information and we do not support its use in rulemaking. (Submitted by: American Heart Association/American Stroke Association)
  
  
• The Hospital MAP Workgroup conditionally supported a new hospital-wide all-cause risk standardized mortality measure (MUC17-195) pending review and endorsement, specifically recommending that the NQF committee reviewing the measure ensure that there is appropriate, validated evidence supporting the measure and explicitly consider the importance of the measure and potential unintended consequences. It was also recommended that the measure be brought to the NQF Disparities Standing Committee as part of the evaluation for appropriateness of adjusting for social risk factors. The AAMC appreciates that the MAP’s discussion that an all-cause mortality measure is of great importance to patients and could potentially encourage facilities to work more collaboratively with other providers and improve continuity of care. However, serious concerns relating to risk adjustment, misuse of all-cause mortality metrics used in epidemiological studies, unintended consequences for end-of-life care, misinterpretation of the measure score by consumers discussed were raised, and it is essential that these concerns be vetted through the NQF endorsement process. Claims-based risk adjustment by using HCC data might not adequately account for appropriate clinical and social risk factors and does not broadly capture the patient’s health status. Hospitals that disproportionately care for vulnerable patient populations are disadvantaged when SDS factors are not considered in the risk adjustment or scoring methodology. The AAMC agrees with the MAP that appropriate risk adjustment is necessary to ensure that the measure does not disproportionately penalize facilities who see more complex patients. Appropriate exclusions should address hospice enrollment to ensure that the timing of hospice decisions by a patient’s family that results in a delay in enrollment such that the patient’s admission is inappropriately included when measuring a hospital’s mortality rate. The AAMC shares the concerns raised by the National Coalition for Hospice and Palliative Care that as proposed, this measure may have the unintended consequence of “inhibit[ing] the use of palliative services and failure to accommodate the wishes of patients who would prefer death over prolonged life-sustaining treatment.” The AAMC believes that condition-specific mortality measures already in use in the IQR may be more actionable for hospitals and may provide more detailed information to patients to support consumer decision-making. We agree with the MAP members who cautioned that performance scores on an all-cause mortality measure could be potentially misleading to consumers, as this may simply reflect a lower acuity facility and not necessarily a facility’s overall quality. For all of the reasons above, the AAMC continues to strongly believe that these concerns weigh against conditional support of the measure for the Hospital Inpatient Quality Reporting (IQR) program. (Submitted by: Association of American Medical Colleges (AAMC))
  
  
• Commendation to MAP Staff for a yeoman's endeavor in drafting CC comments and recommendations. Recommend that sections on the "Measures Removal Criteria" should be identical or at least very similar across the Workgroups. These Criteria represent a common template upon which MAP and CMS can use asGuidance in reducing duplicity or burden - and that intent is the same across all measures and Workgroups. Additionally, the principles/considerations regarding implication of cost with regard to measures, quality care and meaningful outcomes also cross-intersect all Workgroups and so there should be some alignment on general principles across the three Reports as well as future Reports per Rural, Adult and Pediatric Medicaid Workgroups. See < https://www.nytimes.com/2017/12/24/business/trump-administration-nursing-home-penalties.html> < https://www.nytimes.com/2018/01/02/business/nursing-homes-care-corporate.html> Alignment is our name, Consistency should be our claim. Agree with draft and recommendations. Very thoughtful dissection and discussion regarding MUC 17-195 - an example of striving for the "perfect" without damning the "good." Agree with draft and recommendations. The potential decision to sunset the MIPS program (vis MedPAC's and others' discussions) should make MAP and CMS more inclined to create generic metrics that are transmutable to other alternative kinds of payment schemes rather than creating sets and sub-sets to infinity for each scheme. Last time anyone looked, HbA1C had the same significance regardless of the payment scheme in play. MAP is supposed to lead the vanguard in alignment and harmonization, not succumb to it. Agree with draft and recommendations. Workgroup discussions spent the morning decrying the reporting burdens upon practitioners and then spent the afternoon trying to fill in "Gaps" with more reporting requirements. There should be some mention about the proposed "blending" of former Dual Eligible Workgroup members into PAC/LTC. Sunsetting the Workgroup did not sunset the needs for quality care of the needy beneficiaries. Creating 2 new Medicaid Workgroups addresses one aspect of the Duals, but otherwise is like asomatognosia. (Submitted by: AMGA)
  
  
• The AHA urges the Coordinating Committee to consider recommending this measure not be included in the IQR. The measure is at best duplicative of current mortality measures, and at worst provides no useful insight into quality of care. It could even be harmful to overarching quality improvement and transparency goals. Conceptually, this measure and the related measure, MUC17-196, are both flawed. Hospitals already report and are evaluated on mortality data for high-priority conditions (e.g., heart attack, heart failure, pneumonia).These measures would include this data, making them redundant, but it would mask any condition-specific outcomes when publicly reported, making the measures useless to consumers and providers. Furthermore, we are deeply concerned that this measure’s design will lead to inaccurate, misleading and unfair performance comparisons. Each hospital’s mix of available services and patient acuity – which greatly influence mortality rates -- is different. For example, a 100-bed community hospital is unlikely to offer the specialized tertiary and quaternary services of an academic medical center. And the patients treated in an academic medical center or other large referral center will likely have greater clinical complexity. Yet, by including all conditions, this measure assumes one can perform an “apples to apples” comparison of these types of hospitals, and render a generalized judgment of which ones provide better care. While risk adjustment can help level the playing field, we fear it is not up to the task for this type of measure. In addition, the use of an all-condition mortality rate has very limited utility for both quality improvement and transparency purposes. Condition-specific rates are more actionable because they help hospitals focus their improvement interventions on the services with the greatest opportunity for improvement. Poorly performing individual service lines may have less incentive to improve if their deficiencies are watered down by high-performing service lines. Consumers looking for the best care for their specific needs would be confused by competing mortality measures; consumers searching for general quality information, which is uncommon, may mistakenly assume this high-level measure reflects all care given in the hospital. In addition to the conceptual issues clear in these measures, there are also methodological issues that render them inappropriate for inclusion in the IQR. First, and perhaps most important, the developer’s methodological report noted that only 6 hospitals, or 0.1%, were found to be statistically worse than average in their performance on this measure; 256 hospitals, or 5.3%, were too low volume for a rate to be calculated. Collecting data and calculating this measure would require considerable resources; these costs would not be balanced with the scant benefit of finding a half-dozen low performers. Neither providers nor consumers would gain any useful information if this measure yielded information as it did when tested. Similarly concerning, these measures were developed using ICD-9 codes; thus, the predictive model is not indicative of the current and future care environment (which uses ICD-10 codes). The measure developer suggested that, if implemented, the measure would use ICD-10 codes; however, because the measure was not developed and specified using these codes, it would in effect be a different measure. For this reason, the AHA believes this measure must be re-specified and re-tested using the ICD-10 codes before it is deemed worthy of either NQF endorsement, or use in the IQR program. This particular measure would be risk-adjusted using claims data, which even the measure developer admits would lead to less accurate results. If we already know that this measure would produce less valid results than an EHR-adjusted measure, we should not support it for inclusion. The conditions under which this measure was supported by the MAP were NQF endorsement, transparency of published evidence, engagement of the disparities committee, and guidance from the Steering Committee on the worthiness of the measure overall and the likelihood of any unintended consequences. These conditions are so significant that, taken together with re-specification of the measure using ICD-10 codes, they suggest that there is still much to be determined and clarified in this measure. Thus, it is not appropriate for inclusion and will not be without considerable revision. (Submitted by: American Hospital Association)
  
  
• The Federation of American Hospitals (FAH) supports the conditions placed on this measure and strongly encourages CMS to address each prior to implementation in any federal programs. (Submitted by: Federation of American Hospitals)
  
  
• CHA respectfully requests that the Coordinating Committee revisit this measure and consider a “do not support” recommendation. After further review of the measure’s technical documentation, provided by CMS and to the MAP, it became clear that a number of issues worthy of consideration were not fully vetted by the workgroup. Notably, the results of this measure’s testing — noted on page 49 of the November 10, 2017 technical report — show that of the 4,793 hospitals included in the analysis, only 102 hospitals (2.1 percent) show up in the “better” category, and only six hospitals (0.1 percent) in the “worse” category. 92.4 percent of all hospitals are no different in their mortality under this measure. Unfortunately, these results are not part of the discussion guide provided to MAP members; instead, it notes the measure has not been tested. Additional discussion of testing and results would be an important part of the Coordinating Committee discussion. CHA believes strongly that if CMS is committed to its initiative of implementing meaningful measures that the results of this measure testing would not meet the criterion with so little differentiation. In our view, with only six hospitals nationwide performing worse than the national average, CMS should not put forth additional resources toward implementation. Measures with so little differentiation are not worthy of consideration in the quality reporting programs. In addition to the summary of the discussion noted by NQF in the recommendation, a number of important points are worth reconsideration, including: • This all-cause mortality measure is a risk-standardized measure, not a risk-adjusted measure, meaning that this measure compares a hospital to national average rather than making a meaningful comparison to another hospital. We do not believe this positively contributes to consumer understanding of differences in quality among hospitals in their communities — especially in a measure with so little differentiation. Such measures are more often used in epidemiology and not in performance improvement. This was a strong point made by experts on the panel, and additional discussion of this point is warranted. • In addition, we disagree with the assertion that the measure aligns well with the readmissions measure as discussed by CMS and the measure developer as a means for hospitals to address performance improvement. Hospitals can use the data provided by CMS to understand the clinical reasons for a readmission. Hospitals can interview patients or caregivers and work with other providers to obtain additional information that would assist in process improvement. However, when a patient dies, the hospital has far less information. While condition-specific mortality measures allow for a bit more information, a three-year all-cause mortality measure presents many challenges for any substantive root cause analysis that would contribute to meaningful performance improvement. • The measure lacks the exclusions. For example, the developer leaves in those patients that die waiting for an organ transplant. Patients with end-stage heart failure, whose lives are sustained by artificial respiration and artificial circulation until they receive a heart transplant or artificial heart assist system, are excluded from the HF mortality measure. These patients are at extreme risk of death, but they are not excluded from the all-cause measure. The rationale for inclusion is not well understood and should be revisited along with other similar exclusions. After multiple votes, this measure was decided in a 15-9 vote in favor of a “conditionally support” recommendation. CHA respectfully requests that this measure be reconsidered for a “do not support” by the Coordinating Committee. We do not believe this measure should go forward at this time, and believe that the NQF committee to review these measures would likely come to the same conclusions and not find this measure favorable for endorsement. Finally, we offer the committee additional literature that points to some of the challenges/findings with all-cause mortality measures and urge reconsideration of the current recommendation. We anticipate these findings would be part of a steering committee review process, but in light of the overwhelming literature related to the challenges of this type of measure, we do not believe it warrants going forward in the endorsement process. Therefore, we urge a “do not support” recommendation. a) Tracking hospital-wide death rates does not foster improvement — there is no direct relation between the death rates and hospital processes that could be improved. No causal inferences can be drawn from death rate differences. Shahian, D. M., Iezzoni, L. I., Meyer, G. S., Kirle, L., & Normand, S.-L. T. (2012). Hospital-wide mortality as a quality metric: conceptual and methodological challenges. American Journal of Medical Quality: The Official Journal of the American College of Medical Quality, 27(2), 112–23. http://doi.org/10.1177/1062860611412358 b) Only a small fraction (less than 4 percent) of hospital deaths are potentially avoidable, so overall death rates are poor indicators of quality. Further, the association between standardized death rates and avoidable deaths is insignificant. Hogan, H., Zipfel, R., Neuburger, J., Hutchings, A., Darzi, A., & Black, N. (2015). Avoidability of hospital deaths and association with hospital-wide mortality ratios: retrospective case record review and regression analysis. BMJ (Clinical Research Ed.), 351(February), h3239. http://doi.org/10.1136/bmj.h3239 c) The small fraction of potentially avoidable deaths means that well over 90 percent (as much as 97 percent) of the standardized mortality ratio variation between hospitals is unrelated to preventable mortality, even with theoretically perfect risk adjustment and theoretically perfect mortality measurement. Girling, A. J., Hofer, T. P., Wu, J., Chilton, P. J., Nicholl, J. P., Mohammed, M. a, & Lilford, R. J. (2012). Case-mix adjusted hospital mortality is a poor proxy for preventable mortality: a modelling study. BMJ Quality & Safety, 21(12), 1052–6. http://doi.org/10.1136/bmjqs-2012-001202 d) Variation between hospitals is not related to quality. Shojania, K. G., & Forster, A. J. (2008). Hospital mortality: when failure is not a good measure of success. CMAJ : Canadian Medical Association Journal = Journal de l’Association Medicale Canadienne, 179(2), 153–7. http://doi.org/10.1503/cmaj.080010 (Submitted by: California Hospital Association)
  
  
• Do Not Support MAP Recommendation. CHA does not support the inclusion of an all-mortality measure for this type of accountability program (such as the CMS programs). There are a multitude of challenges associated with the use of all-cause mortality measures in public reporting and value-based programs. This includes: Prematurely discharging patients who may be prematurely discharged by those who wish to avoid the negative implications of being an outlier on this measure; potential for unintended consequence of some hospitals prematurely discharging patients; there are a number of condition-specific mortality measures available and in use now; lacking evidence that this measure broadly assesses the hospital's quality of care provided to patients. There are also concerns as the adequacy of the risk adjustment and it is not currently NQF-endorsed. The measure specifications have been updated to include ICD-10 codes; however, it has not been sufficiently tested using these codes and it is critical that the measure specification using ICD-10 codes is fully tested - especially for a measure being assessed for CMS accountability programs. (Submitted by: Children's Hospital Association)
  
  

• American Medical Association (AMA) supports the conditions places on this measure (Submitted by: American Medical Association)
  
  
• The Hospital MAP Workgroup also conditionally supported a new hybrid hospital-wide all-cause risk standardized mortality measure (MUC17-196) pending review and endorsement with enumerated recommendations for the Standing Committee in its review of the measure, including that the Standing Committee pay specific attention to the ability to consistently obtain EHR data across hospitals. The MAP also recommended that there be a voluntary reporting period for the measure before it is finalized in the IQR program in order to allow providers to test the extraction of electronic data elements. The AAMC agrees with the voluntary reporting recommendation, but continues to strongly believe that the overall concerns with hospital-wide all-cause mortality measures (as discussed above) plus the additional concerns described below related to this hybrid measure weighed against conditional support of the measure for the IQR program. The AAMC understands that CMS’ intention is to replace MUC17-195 with this hybrid all-cause mortality measure once the hybrid measure is fully developed and endorsed. The MAP noted that the concerns for the claims-based mortality measure were the same for this measure, in addition to concerns with the challenges of extracting EHR data and EHR fragmentation. The AAMC believes that integrating EHR data with claims data is a positive step, but we recommend that the focus of efforts at this stage should be on the use of EHR data to adjust condition specific mortality measures that are currently being used in the programs. Additionally, the AAMC would like to note that there was discussion around concern that this measure was developed using Kaiser Permanente Northern California data in the model, and that such data is not representative of Medicare Fee for Service more broadly. This was not included in the draft report, and the AAMC recommends that this concern be acknowledged in the final report. (Submitted by: Association of American Medical Colleges (AAMC))
  
  
• The AHA does not support the inclusion of this measure in the IQR. We refer the reader to our comments on MUC17-195 that outlined our conceptual and methodological concerns about this measure. The measure is at best duplicative of current mortality measures, and at worst provides no useful insight into quality of care and could even be harmful to overarching quality improvement goals. Conceptually, this measure and the related measure, MUC17-195, are both flawed. Hospitals already report and are evaluated on mortality data for high-priority conditions; these measures would include this data, making them redundant, but mask any condition-specific outcomes when publicly reported, making them useless to consumers and providers. Hospitals provide such a wide variety of services that providing a sweeping, generalized judgment as is done in these measures is inaccurate, misleading, and unfair. Poorly performing individual service lines would not be incented to improve if their low returns were watered down by high-performing service lines. Consumers looking for the best care for their specific needs would be confused by competing mortality measures; consumers searching for general quality information, which is uncommon, would assume this high-level measure reflects all care given in the hospital. The measure would attract a great amount of attention and would likely stymie ongoing, granular quality improvement initiatives as hospitals would be encouraged to focus singularly on mortality rather than on the long list of other patient safety and quality outcomes. In addition to the conceptual issues clear in these measures, there are also logistical issues that render them inappropriate for inclusion in the IQR. First, these measures were developed using ICD-9 codes; thus, the predictive model is not indicative of the current and future care environment (which uses ICD-10 codes). The measure developer suggested that, if implemented, the measure would use ICD-10 codes; however, because the measure was not developed and specified using these codes, it would in effect be different in kind. If this measure were to move forward in the NQF endorsement process, it would have to be re-specified using the correct, updated code set in order to be deemed valid. The AHA agrees with the potential value of hybrid electronic clinical quality measures (eCQMs) such as MUC17-196. EHRs have the potential to improve the risk adjustment of claims-derived outcome measures because EHR data may include more precise clinical information than using claims alone. While this particular measure would have better face validity than the claims-only MUC17-195, it is unclear whether the results from the testing would be indicative of wider industry performance. Testing of this measure was done using Kaiser Permanente Norther California data; this provider is not comparable to the average hospital as it is an integrated health maintenance organization that uses a homegrown, internally interoperable EHR system. In other words, it is unclear whether a non-self-insured hospital with an off-the-shelf EHR would be able to receive adequately risk-adjusted outcomes based on the findings of this measure’s testing. (Submitted by: American Hospital Association)
  
  
• The Federation of American Hospitals (FAH) supports the conditions placed on this measure and strongly encourages CMS to address each prior to implementation in any federal programs. (Submitted by: Federation of American Hospitals)
  
  
• While we believe this measure may be some improvement on the claims-based measure, CHA does not support the workgroup’s recommendation on this measure for the same reasons noted in our comments on the claims-based measure. Further, we ask that the Coordinating Committee consider a “do not support” recommendation. (Submitted by: California Hospital Association)
  
  
• Do Not Support MAP Recommendation. CHA does not support the inclusion of an hybrid all-mortality measure in high-stakes accountability programs (such as the CMS programs) for similar reasons that we commented on for the MUC17-195 Hospital-Wide All-Cause Risk Standardized Mortality Measure. Also, the lack of adequate risk adjustment for social factors, this measure should be tested on existing condition-specific mortality measures. (Submitted by: Children's Hospital Association)
  
  

• American Medical Association (AMA) supports the conditions places on this measure (Submitted by: American Medical Association)
  
  
• The AHA believes that this measure provides potential value to the Hospital Inpatient Quality Reporting Program, but because it has not been fully tested, let alone evaluated and endorsed by the NQF, it is not appropriate for inclusion in the IQR at this time. In addition, we encourage the MAP to recommend including this measure only in public reporting and quality improvement programs like the IQR rather than in value-based purchasing programs due to the sensitive nature of opioid use in hospitals. The AHA is interested to see whether it is truly feasible to collect the information necessary to calculate this measure, as well as whether there is true variation in care across hospitals. In addition, we encourage the measure developers to be watchful of any unintended consequences the measure may carry. Because it uses the administration of naloxone as a trigger for identifying a potential overdose, we are concerned it might encourage the use of more invasive efforts to combat respiratory events (like intubation) over the necessary use of naloxone. We also suggest that the developers consider multiple risk adjustment approaches, including stratification rather than overall risk adjustment and testing for the appropriate population exclusions. Finally, because this measure is proposed as part of the EHR Incentive Program, we hope that the developers will provide more information about the expectations regarding notation of the medication in the record or summary of care. Because there are challenges associated with capturing a single drug in the EHR for surveillance purposes, providers will need to be prepared to accurately capture this information in order for the measure to be calculated correctly. (Submitted by: American Hospital Association)
  
  
• The Federation of American Hospitals (FAH) supports the conditions placed on this measure and strongly encourages CMS to address each prior to implementation in any federal programs. (Submitted by: Federation of American Hospitals)
  
  
• • Overall, we support the MAP’s recommendation to refine and resubmit. The measure requires more detailed descriptions in its definition to prevent issues with validity and reliability. The numerator does not account for issues that could lead to measures of false positives. For example, naloxone can be administered for purposes other than reversal of respiratory depression. Low dose infusions of naloxone can be used to treat side effects of opioids other than respiratory depression (e.g., itching). Counting cases such as these would lead to measures of false positives. Additionally, is this measure intended to be a measure solely of respiratory depression, or a measure of narcotic-induced complications? Narcotics can induce complications other than respiratory depression, such as altered/depressed mental status. We urge the developers to consider other complications in this measure. (Submitted by: Johns Hopkins Armstrong Institute)
  
  
• CHA agrees that measurement of adverse events is an important metric, and the growing abuse of opioids and the treatment of patients with dependencies is a national priority. However, CHA does not support the workgroup’s recommendation of “refine and resubmit.” Rather, CHA believes another measurement approach is likely a better option and urges the Coordinating Committee to consider a “do not support” recommendation. The workgroup discussion was a rich dialogue of the challenges with this measure. Upon reflecting on the overwhelming number of issues raised by the workgroup and absent national consensus on an adverse event measure, we think this is an opportunity for CMS to gather stakeholder input into the measure development process, rather than continue to refine this inherently flawed measure. CMS should look to the experience of its HIINs and learn from the many technical hurdles that have been encountered in trying to attain measure standardization. Further complicating the issue is the approach of an EHR measure, where we know this is still an evolving science. Challenges include respiratory monitoring inconsistencies, non-standard definitions, lack of key patient factors such as incorporating use of opioid tolerance scales that contextualize dosing requirements, patient pain/comfort scales and other factors not available via structured electronic health record coding proposed by the measure specifications. While the CMS technical report asserts that electronic health record adoption is wide and supports measure feasibility, the HEN/HIIN experience with trying to secure similar data suggests the electronic recordkeeping for this topic is still evolving and not as feasible as suggested. While we appreciate the limited testing done by CMS, we do not believe it is a reliable indicator of how this measure would perform nationally at this time. In addition, the proposed measure may oversample naloxone use, requiring the adjudication of clinical review. The focus on naloxone use only, in absence of symptoms of respiratory depression, seems to oversimplify the measure even though the authors acknowledge the difficulties in reliably documenting of signs and symptoms of respiratory depression. Including cases of naloxone use for procedures outside the operating room may also oversimplify recognition clinician use of naloxone for procedural sedation. Phase 1 feasibility testing of the measure capturing naloxone use without prior opioid administration over 24 hours found an error rate of 4.3 to 12.8 percent. We remain concerned that these results will only be magnified on a broader testing example; the high variation is very problematic. Finally, CHA is concerned that such a measure would create a chilling effect among providers in the administration of naloxone and that rethinking the approach to measurement in this area would be the prudent next step. Therefore, we urge the Coordinating Committee to consider a “do not support” recommendation to refocus this work in a more meaningful way. (Submitted by: California Hospital Association)
  
  
• Support MAP Recommendation. (Submitted by: Children's Hospital Association)
  
  

• The American Medical Association (AMA) requests that an additional condition be placed on this measure for the MIPS and MSSP programs. Specifically, the condition would be that the measure must be risk-adjusted or stratified to enable fair and valid comparisons across physicians. As we expressed in our previous comments, the AMA is extremely concerned that comparisons of physician performance using this measure are likely to result in unfair and invalid assessments of the quality of care provided to these patients. Currently, this composite assesses achievement of a specific A1c level in all patients aged 18 to 75 years and assumes that all of these patients can reasonable achieve this target. In programs on which performance and points are assessed based on a physician’s score against his or her peers, it is unreasonable to assume that all patient populations across the United States are homogeneous and that all physicians reporting this measure can achieve similar scores. As a result, we believe that physicians will be unfairly penalized if they have more complex patients and patients will be misinformed about the actual quality of care provided. We urge the MAP to consider adding this condition to this measure. (Submitted by: American Medical Association)
  
  

• I frequently am asked to find A1C values that are embedded somewhere in not text. The data in the note text and not easily accessible to providers and I would recommend the measure indicate that values need to be stored in discrete, easily found places in the EHR. Using natural language processing to find values for any of the measures should be highly discouraged. (Submitted by: Health Catalyst)
  
  
• The American Medical Association (AMA) requests that an additional condition be placed on this measure for the MIPS and MSSP programs. Specifically, the condition would be that the measure must be risk-adjusted or stratified to enable fair and valid comparisons across physicians. As we expressed in our previous comments, the AMA is extremely concerned that comparisons of physician performance using this measure are likely to result in unfair and invalid assessments of the quality of care provided to these patients. Currently, this composite includes measures on intermediate outcomes, which assume that all patients aged 18 to 75 years can reasonable achieve these targets. In programs on which performance and points are assessed based on a physician’s score against his or her peers, it is unreasonable to assume that all patient populations across the United States are homogeneous and that all physicians reporting this measure can achieve similar scores. As a result, we believe that physicians will be unfairly penalized if they have more complex patients and patients will be misinformed about the actual quality of care provided. We urge the MAP to consider adding this condition to this measure. (Submitted by: American Medical Association)
  
  
• The American Medical Association (AMA) requests that an additional condition be placed on this measure for the MIPS and MSSP programs. Specifically, the condition would be that the measure must be risk-adjusted or stratified to enable fair and valid comparisons across physicians. As we expressed in our previous comments, the AMA is extremely concerned that comparisons of physician performance using this measure are likely to result in unfair and invalid assessments of the quality of care provided to these patients. Currently, this composite assesses achievement of a specific A1c level in all patients aged 18 to 75 years and assumes that all of these patients can reasonable achieve this target. In programs on which performance and points are assessed based on a physician’s score against his or her peers, it is unreasonable to assume that all patient populations across the United States are homogeneous and that all physicians reporting this measure can achieve similar scores. As a result, we believe that physicians will be unfairly penalized if they have more complex patients and patients will be misinformed about the actual quality of care provided. We urge the MAP to consider adding this condition to this measure. (Submitted by: American Medical Association)
  
  
• no change no change no change no change (Submitted by: Family Health Care, P.C.)
  
  
• We support the MAP’s recommendation. However, we are concerned about using the most recent value in the reporting period as a measure of success, since this does not encourage ongoing monitoring. For example, if a patient meets the appropriate HbA1c levels, this measure discourages the physician from obtaining a follow-up measurement, as the patient may fail to meet the appropriate levels at the follow-up. A possible alternative may including multiple HbA1c measurements so that the physician is not discouraged to follow up with the patient. (Submitted by: Johns Hopkins Armstrong Institute)
  
  
• NAACOS is pleased to submit our comments in response to the MAP Clinician Workgroup’s draft report, “MAP 2018 Considerations for Implementing Measures in Federal Programs: MIPS and MSSP.” NAACOS is the largest association of ACOs, representing more than 4 million beneficiary lives through 300 Medicare Shared Savings Program (MSSP) ACOs, Next Generation, and commercial ACOs. NAACOS is an ACO member-led and member-owned non-profit organization that works on behalf of ACOs across the nation to improve the quality of Medicare delivery, population health and outcomes, and health care cost efficiency. NAACOS and its members are committed to transforming the way healthcare is delivered and paid for. Our members are at the forefront of this transformation effort and have invested significant time and resources to their success, which will ultimately improve care for Medicare beneficiaries and reduce costs for CMS. The draft report notes that the MAP considered three measures for the MSSP: Optimal Diabetes Care; Diabetes A1c Control (<8.0); and Ischemic Vascular Disease Use of Aspirin or Anti-Platelet Medication. The MAP conditionally supported all three measures in its draft report. The MSSP ACO quality measures set already recognizes the clinical prevalence of diabetes by including a composite measure, composed of two individual measures, in the current measure set. While we support the draft report’s reference to conditionally support the MSSP measures outlined with the condition that there are no competing measures in the program, we point out that the current MSSP quality measure set does include competing and duplicative measures. Specifically, the MSSP measure set already includes one composite measure for diabetes (ACO-27, ACO-41) as well as an Ischemic Vascular Disease measure (ACO-30). Therefore, we do not recommend inclusion of the measures contained in this draft report. We thank the workgroup for their efforts and recognition of the importance of avoiding competing and/or duplicative measures within the same measure set. While we support the draft report’s reference to conditionally support the MSSP measures outlined with the condition that there are no competing measures in the program, we point out that the current MSSP quality measure set does include competing and duplicative measures. Specifically, the MSSP measure set already includes one composite measure for diabetes (ACO-27, ACO-41) as well as an Ischemic Vascular Disease measure (ACO-30). Therefore, we do not recommend inclusion of the measures contained in this draft report. We thank the workgroup for their efforts and recognition of the importance of avoiding competing and/or duplicative measures within the same measure set. (Submitted by: NAACOS)
  
  

• Support MAP Recommendation. (Submitted by: Children's Hospital Association)
  
  

• American Medical Association (AMA) supports the conditions places on this measure (Submitted by: American Medical Association)
  
  
• The AHA questions the usefulness of this measure, and believes that the measure will need to undergo significant reassessment under the conditions added by the MAP as it goes through the NQF review process before it is implemented in the ASCQR. Conceptually, this measure fails to address a high-priority issue where significant differences in quality between providers exist. Rates for hospital visits following general surgery within 7 days of the procedure are already very low, approximately 2%. In addition, the measure developers note only 30 of 1,651 ASCs demonstrated significant outlier rates after risk adjustment. In addition, measure performance would not necessarily indicate actual variation in quality of care: the admission diagnoses included in the rate are not necessarily indicative of poor quality of care, but rather might reflect new, unrelated diagnoses. Similarly, emergency department visits do not necessarily indicate a problem related to the procedure performed at the ASC, but rather could be reflective of unrelated issues outside of the ASC’s control (for example, many patients seek care at EDs due to a lack of access to other sources of care). In addition to be conceptually flawed, the measure as specified also does not accurately reflect the care provided in general surgery at ASCs. More than half of the procedures in the measure cohort are skin procedures, which are frequently performed by practitioners other than general surgeons and are not indicative of the typical case mix at an ASC. The measure developer included these procedures in order to generate sufficient volume to calculate performance; the fact that there is not sufficient volume of general surgery procedures to calculate a rate of hospital visits following the procedure is an indication that this measure is inappropriate. This measure is certainly not appropriate for inclusion in the ASCQR as it was presented to the MAP. The AHA supports conditional endorsement of the measure only if it is endorsed by the NQF, and if the endorsement evaluation is done with consideration of sociodemographic status factors other than the very few included in the measure’s current specifications. (Submitted by: American Hospital Association)
  
  
• On behalf of the ASC Quality Collaboration (ASC QC), thank you for considering the following comments regarding the Measure Applications Partnership (MAP) draft report, “MAP 2018 Considerations for Implementing Measures in Federal Programs: Hospitals” as it pertains to MUC17-233, “Hospital Visits Following General Surgery Ambulatory Surgical Center Procedures”. The ASC QC is a non-profit organization dedicated to advancing quality measurement and public reporting in the ambulatory surgery center (ASC) industry through a collaborative effort involving a diverse group of ASC stakeholders. MAP conditionally supported MUC17-233 for the ASCQR program. However, MAP acknowledged a number of concerns raised in initial public commenting period about the measure, including the attribution model. We share these concerns, and believe the attribution model requires additional refinement. As currently specified, the measure makes no attempt to differentiate expected outcomes and appropriate post-operative care from unexpected outcomes/care. In fact it penalizes ASCs for their role in the diagnosis and treatment of conditions such as cancer. We urge the MAP to review Table 4, “Top hospital visit diagnoses for any hospital visit within 7 days of general surgery procedures (dataset: Medicare FFS CY 2015)”, of the measure documentation carefully. Several of these “top diagnoses” reflect the indication for the patient’s ASC care. For example, a new diagnosis of lymphoma following surgery on the hemic or lymphatic system is not an acute illness or complication of care. Similarly, a newly diagnosed breast neoplasm is not an illness caused by the index breast surgery or a complication of the surgery itself. ASCs should not be penalized for conditions such as the acquired absence of a breast/nipple, which are expected following a mastectomy. Only the “top hospital visit diagnoses” have been presented for public review; it is not clear how many other inappropriate outcomes are identified by the measure algorithm. In addition to this shared concern regarding attribution, we believe there are other aspects of the measure that should be remedied before it is considered for inclusion in the ASCQR program. We enumerate these below. A. The Measure Cohort The title of the measure sets the expectation that the results will be reflective of the practice of general surgery in the ASC setting. However, the majority of cases included in the measure are skin and soft tissue procedures, which have been included in order to generate sufficient volume for the cohort. Consequently, the case mix diverges significantly from the typical practice of a general surgeon in the ASC setting, since these services are routinely performed by other physician specialties. If the developer remains intent on retaining all the skin surgeries, it would be helpful to rename the measure to improve its face validity, and to better reflect what it truly assesses. A title such as “Unplanned Hospital Visits After Skin Surgery and General Surgery Procedures Performed at Ambulatory Surgical Centers” - putting skin surgery first since it is the predominant procedure type - would be reasonable. B. Low ASC Case Volumes One of the measure requirements listed for the ASCQR program in the draft report is the “measure must supply sufficient case numbers for differentiation of ASC performance”. This measure has been specified in ways that prop up the overall volume of cases. As noted above, one strategy used to inflate case volume was to include large numbers of skin surgeries. Another strategy was to include a large number of low volume ASCs. Specifically, ASCs with as few as 25 “general surgery” cases over a two-year period (just 12 cases per year) are included for analysis. Consider that, according to the measure developer, “[a]cross ASCs in the Medicare FFS CY 2015 dataset, the median volume of general surgery procedure cases in the cohort was 12 and ranged from 1 to 1,620 procedures per ASC (the 25th and 75th percentiles were 3 and 43 procedures, respectively).” This low threshold does not provide sufficient information about quality of care in all the ASCs included in the measure, making the measure scores inappropriate for public reporting and accountability purposes. C. Limited Ability to Make Distinctions Among Facilities The purpose of this measure is said to be threefold: “to illuminate variation in quality of care for general surgery procedures across ASCs, inform patient choice, and drive quality improvement.” The measure does not illuminate variation in quality of care. Of the 1,651 ASCs that qualified for the measure, the performance of 1,621 centers (about 98%) was no different than the national rate. Of the remaining 30 ASCs, 14 performed better than the national rate, and 16 performed worse than the national rate. The overwhelming majority (about 99%) of facilities would receive a measure score indicating their performance to be either no different from or better than the national rate. The number of underperforming facilities is very small. The measure is already “topped out”. One of the principal program goals for the ASCQR is to “provid[e] consumers with quality information that will allow them compare the quality of care given at ASCs and help them make informed decisions about where they receive care.” When 98% of ASCs’ performance is no different than the national rate, the consumer is very unlikely to be able to discern differences in quality. Another measure requirement for the ASCQR program is that measures be “clinically useful”. When only 16 facilities would “underperform”, the measure is unlikely to drive performance improvement. Even this “underperformance” is questionable given the issues with the measure’s misclassification of outcomes, such new cancer diagnoses, as discussed above. Experience with a similar measure, the ASCQR Program’s ASC-12: Facility Seven-Day Risk-Standardized Hospital Visit Rate after Outpatient Colonoscopy, is worth considering. Only four facilities performed worse than the national rate. *** We believe the MAP should issue a “Do Not Support for Rulemaking” recommendation for this measure. “Refine and Resubmit” might seem logical, however experience indicates when the MAP issues this recommendation for ASCQR program measures under consideration CMS does not refine and resubmit. There is little point in selecting this option when measures might have merit, but need additional work. (Submitted by: ASC Quality Collaboration)
  
  
• The Federation of American Hospitals (FAH) believes that an additional condition is not reflected in the draft rationale for this measure. During the Hospital Workgroup’s discussions, several members requested that during the endorsement review, the NQF Standing Committee complete a thorough assessment of the underlying evidence for this measure since several of the studies on which this measure is based are more than 10 years old and may not reflect current ambulatory surgery center care. FAH requests that this condition be reflected in the final rationale and strongly encourages CMS to address each prior to implementation in any federal programs. (Submitted by: Federation of American Hospitals)
  
  
• While we support this measure, we recommend that the measure developers add visits to urgent care to the list of hospital visits. Additionally, a measure of care quality would measure from time of discharge from ASC to seven days thereafter, as any transfers prior to discharge would be a sign of higher care quality. (Submitted by: Johns Hopkins Armstrong Institute)
  
  
• CHA supports the workgroup’s recommendation of “do not support” for all the reasons noted by the workgroup. (Submitted by: California Hospital Association)
  
  
• Support MAP Recommendation. (Submitted by: Children's Hospital Association)
  
  

• NAACOS is pleased to submit our comments in response to the MAP Clinician Workgroup’s draft report, “MAP 2018 Considerations for Implementing Measures in Federal Programs: MIPS and MSSP.” NAACOS is the largest association of ACOs, representing more than 4 million beneficiary lives through 300 Medicare Shared Savings Program (MSSP) ACOs, Next Generation, and commercial ACOs. NAACOS is an ACO member-led and member-owned non-profit organization that works on behalf of ACOs across the nation to improve the quality of Medicare delivery, population health and outcomes, and health care cost efficiency. NAACOS and its members are committed to transforming the way healthcare is delivered and paid for. Our members are at the forefront of this transformation effort and have invested significant time and resources to their success, which will ultimately improve care for Medicare beneficiaries and reduce costs for CMS. The draft report notes that the MAP considered three measures for the MSSP: Optimal Diabetes Care; Diabetes A1c Control (<8.0); and Ischemic Vascular Disease Use of Aspirin or Anti-Platelet Medication. The MAP conditionally supported all three measures in its draft report. The MSSP ACO quality measures set already recognizes the clinical prevalence of diabetes by including a composite measure, composed of two individual measures, in the current measure set. While we support the draft report’s reference to conditionally support the MSSP measures outlined with the condition that there are no competing measures in the program, we point out that the current MSSP quality measure set does include competing and duplicative measures. Specifically, the MSSP measure set already includes one composite measure for diabetes (ACO-27, ACO-41) as well as an Ischemic Vascular Disease measure (ACO-30). Therefore, we do not recommend inclusion of the measures contained in this draft report. We thank the workgroup for their efforts and recognition of the importance of avoiding competing and/or duplicative measures within the same measure set. While we support the draft report’s reference to conditionally support the MSSP measures outlined with the condition that there are no competing measures in the program, we point out that the current MSSP quality measure set does include competing and duplicative measures. Specifically, the MSSP measure set already includes one composite measure for diabetes (ACO-27, ACO-41) as well as an Ischemic Vascular Disease measure (ACO-30). Therefore, we do not recommend inclusion of the measures contained in this draft report. We thank the workgroup for their efforts and recognition of the importance of avoiding competing and/or duplicative measures within the same measure set. (Submitted by: NAACOS)
  
  

• While the American Medical Association (AMA) is supportive of the collaborative process CMS has used in the development of these measures, we do not believe that they were ready for MAP consideration and did not receive adequate vetting by the Clinician Workgroup. There is also a consistent problem with the timeline to provide comments back to the MAP which greatly jeopardizes the integrity of the MAP process. The AMA is troubled over the lack of transparency and inconsistent application of the Measure Selection Criteria (MSC) to these episode-based cost measures. Specifically, no information regarding the individual measure specifications, attribution methodology, or reliability and validity testing results were released for member and public review prior to the MAP Clinician Workgroup meeting, modifications to the measures based on preliminary feedback are still being made, and, to our knowledge, the Workgroup members did not have any detailed information in front of them at the time of the discussion. The developer only cited some limited information on how the measures were developed and tested. Given the degree of interest from numerous medical specialty societies, the AMA looked forward to a robust and detailed discussion on each of these cost measures but unfortunately, it did not occur. (Submitted by: American Medical Association)
  
  
• ASCRS thanks the MAP for its comments in support of appropriate risk adjustment and the need for an alternative metric than a national average to compare a physician’s cost. We believe the exclusions submitted in the cataract episode methodology are key factors in assuring physicians are not held accountable for the cost of treating patients with co-morbidities, which is out of their control. In addition, we believe an appropriate alternative to a national average cost would be to compare the physician’s expected cost versus the physician’s observed cost. Given the wide range in average costs for each of the episode sub-groups noted during the recent field test of this measure, the national average cost of cataract surgery identified, $2,676, is a misleading number to ophthalmologists. We suggest the measure be based on comparing physicians to how far they diverge from the expected cost, based on their mix of episode sub-groups and risk percentile. (Submitted by: American Society of Cataract and Refractive Surgery)
  
  
• • Overall, we support the MAP’s recommendation. We urge the measure developers to address fees that are out of the physician’s control. Physicians who perform their procedures at free standing surgical centers would incur regulated fees that differ from the regulated fees in hospitals. The variation in these fees is not affected by the physician’s performance. (Submitted by: Johns Hopkins Armstrong Institute)
  
  

• • We agree that this measure should undergo NQF endorsement. As part of that review, we would urge the measure developers to place an emphasis on feasibility of collecting the data required for this measure. It is unclear to us what the 3-point improvement represents in terms of clinical efficacy. It is unclear how clinicians would use this measure for gauging their success. (Submitted by: Johns Hopkins Armstrong Institute)
  
  

• The American Medical Association supports the conditions placed on this measure and strongly encourages CMS to address each prior to implementation in any federal programs. We would note out concern over conidtions that call upon NQF Committees to advise on specific measures. Specifically, the requests to have the Disparities Committees provide guidance on the social risk factors in the risk-adjustment approach and Attribution Committee on the attribution methdology. It is our understanding that these Committees do not have defined roles in reviewing measures in the Consensus Development Process at this time and, in fact, the Disparities Standing Committee was not asked to provide input on specifics measures duirng the Socioeconomic Status Trial Period. The AMA would ask that NQF clearly define how it will seek input from these committees to ensure that the conditions placed on these measures will be met. (Submitted by: American Medical Association)
  
  
• KCP does not support MUC 17-241 or MUC 17-245 and disagrees with the MAP Hospital Workgroup recommendation of “Conditional Support” (pending NQF review and endorsement) of the Transplant Waitlist measures. KCP recognizes the tremendous importance of improving transplantation rates for patients with ESRD, but does not support the attribution to dialysis facilities of successful/unsuccessful waitlisting. KCP believes that while a referral to a transplant center, initiation of the waitlist evaluation process, or completion of the waitlist evaluation process may be appropriate facility-level measures that could be used in ESRD quality programs, the Percentage of Prevalent Patients Waitlisted (PPPW) and Standardized First Kidney Transplant Waitlist Ratio for Incident Dialysis Patients (SWR) are not. Waitlisting per se is a decision made by the transplant center and is beyond a dialysis facility’s locus of control. In reviewing these measures, we offer the following comments: Comments Relevant to both the PPPW and SWR Measures Several of KCP’s concerns apply to both the PPPW and SWR measures: • NQF endorsement. KCP appreciates the Workgroup recognizes the importance of NQF endorsement. We note NQF-endorsement is a general pre-requisite for KCP to support inclusion of a measure in any accountability program. • Facility attribution. KCP appreciates the Workgroup’s recommendation that the Waitlist measures also be reviewed by NQF’s Attribution Expert Panel to assess KCP’s and other stakeholders’ concerns about the measures’ attribution models. However, we strongly object to attributing successful/unsuccessful placement on a transplant waitlist to dialysis facilities and believe this is a fatal structural flaw. The transplant center decides whether a patient is placed on a waitlist, not the dialysis facility. One KCP member who is a transplant recipient noted there were many obstacles and delays in the evaluation process with multiple parties that had nothing to do with the dialysis facility—e.g., his private pay insurance changed the locations where he could be evaluated for transplant eligibility on multiple occasions, repeatedly interrupting the process mid-stream. Penalizing a facility each month through the PPPW and SWR for these or other delays is inappropriate; such misattribution is fundamentally misaligned with NQF’s first “Attribution Model Guiding Principle”, which states that measures’ attribution models should fairly and accurately assign accountability. KCP emphasizes our commitment to improving transplantation access, but we believe other measures with an appropriate sphere of control should be pursued. • Age as the only sociodemographic risk variable. KCP appreciates the Workgroup’s recommendation that the Waitlist measures also be reviewed by NQF’s Disparities Standing Committee to assess KCP’s and other stakeholders’ concerns about the measures’ risk of potentiating existing health inequities. KCP strongly believes age as the only sociodemographic risk variable is insufficient. We believe other biological and demographic variables are important, and not accounting for them is a significant threat to the validity of both measures. Transplant centers assess a myriad of demographic factors—e.g., family support, ability to adhere to medication regimens, capacity for follow-up, insurance-related issues, etc. Given transplant centers consider these types of sociodemographic factors, any waitlisting measure risk model should adjust for them. Of note, like the Access to Kidney Transplantation TEP, KCP does not support adjustment for waitlisting based on economic factors or by race or ethnicity. Geography, for instance, should be examined, since regional variation in transplantation access is significant. Waitlist times differ regionally, which will ultimately change the percentage of patients on the waitlist and impact performance measure scores. That is, facilities in a region with long wait times will “look” better than those in a region with shorter wait times where patients come off the list more rapidly—even if both are referring at the same rate. Additionally, criteria indicating a patient is “not eligible” for transplantation can differ by location—one center might require evidence of an absence of chronic osteomyelitis, infection, heart failure, etc., while another may apply them differently or have additional/ different criteria. The degree to which these biological factors influence waitlist placement must be accounted for in any model for the measure to be a valid representation of waitlisting. • Hospice exclusion. We note that an exclusion for patients admitted to hospice during the month of evaluation has been incorporated into both measures. KCP agrees that the transplantation access measures should not apply to persons with a limited life expectancy and so is pleased to see this revision. • Risk model fit. KCP appreciates the Workgroup’s recommendation that the Waitlist measures also be reviewed by NQF’s Scientific Methods Panel to assess KCP’s and other stakeholders’ concerns about the measures’ risk models. We note that risk model testing yielded an overall C-statistic of 0.72 for the PPPW and 0.67 for the SWR, raising concerns that the models will not adequately discriminate performance. Smaller units, in particular, might look worse than their actual performance. We reiterate our long-held position that a minimum C-statistic of 0.8 is a more appropriate indicator of a model’s goodness of fit, predictive ability, and validity to represent meaningful differences among facilities. • Stratification of reliability results by facility size. CMS has provided no stratification of reliability scores by facility size for either measure; we are thus unable to discern how widely reliability varies across the spectrum of facility sizes. We are concerned that the reliability for small facilities might be substantially lower than the overall IURs, as has been the case, for instance, with other CMS standardized ratio measures. This is of particular concern with the SWR, for which empiric testing has yielded an overall IUR of only 0.6—interpreted as “moderate” reliability by statistical convention. To illustrate our concern, the Standardized Transfusion Ratio for Dialysis Facilities (STrR) measure (NQF 2979) was also found to have an overall IUR of 0.60; however, the IUR was only 0.3 (“poor” reliability) for small facilities (defined by CMS as <=46 patients for the STrR). Without evidence to the contrary, KCP is thus concerned that SWR reliability is similarly lower for small facilities, effectively rendering the metric meaningless for use in performance measurement in this group of providers. KCP believes it is incumbent on CMS to demonstrate reliability for all facilities by providing data by facility size. • Meaningful differences in performance. We note that with large sample sizes, as here, even statistically significant differences in performance may not be clinically meaningful. A detailed description of measure scores, such as distribution by quartile, mean, median, standard deviation, outliers, should be provided to allow stakeholders to assess the measure and allow for a thorough review of the measures’ performance. Comment Relevant to PPPW Only • Process vs. intermediate outcome measure. The CMS Measure Information Form identified the PPPW as a process measure. KCP believes the PPPW is an intermediate outcome measure and recommends it be indicated as such. In sum and for the reasons stated above, KCP does not believe that the PPPW and SWR measures are appropriate for use in the ESRD QIP. Thank you for the opportunity to comment on the Measure Applications Partnership’s (MAP) Hospital Workgroup draft report and preliminary recommendations for the 2017-2018 cycle Measures Under Consideration (MUCs) for use in Federal programs. Kidney Care Partners (KCP) is a coalition of members of the kidney care community that includes the full spectrum of stakeholders related to dialysis care—patient advocates, health care professionals, dialysis providers, researchers, and manufacturers and suppliers—organized to advance policies that improve the quality of care for individuals with chronic kidney disease and end stage renal disease (ESRD). We greatly appreciate the MAP undertaking this important work. Three MUCs submitted to the MAP by the Centers for Medicare and Medicaid (CMS) (dated December 1, 2017) are proposed for use in the ESRD Quality Incentive Program (QIP), and consequently are of particular interest to KCP. In reviewing these measures, we offer the following comments. KCP again thanks you for the opportunity to comment on this important work. If you have any questions, please do not hesitate to contact Lisa McGonigal, MD, MPH (lmcgon@msn.com or 203.530.9524). (Submitted by: Kidney Care Partners)
  
  
• During the MAP Hospital Workgroup meeting, there was considerable discussion regarding the measure of the percentage of patients waitlisted for a kidney transplant (MUC 17-241). While the AAMC agrees with the importance of improving transplantation rates for all patients with ESRD and recognize the issues of equal access to transplantation, we do not support the attribution of this measure to dialysis facilities. Referral for transplantation is a decision made by the nephrologist and waitlisting is a decision that is made by the transplant center neither decision is under the control of a dialysis facility. There may be clinical reasons that a patient is not eligible for a transplant and those decisions should be made by a physician responsible for the patient’s care and by the patient. These are complex decisions that take into account many factors. There are demographic issues depending on the location of the facility that may make nationwide comparisons of waitlists percentages difficult to interpret. As the NQF and CMS consider measures for inclusion in programs, it is critical to ensure that these measures are meaningful to the providers and to the consumers. We are concerned about unintended consequences that may occur if this measure were implemented in the ESRD program, particularly due to the attribution concerns. (Submitted by: Association of American Medical Colleges (AAMC))
  
  
• The AHA does not believe that this measure accurately gauges quality of care in the ESRD provider setting and thus does not support it for inclusion in the ESRD QIP. Wait-listing is generally a decision made by the transplant center, and the dialysis facility does not and cannot influence patient inclusion on waitlists enough for this measure to provide any insight. The measure developer suggests that there are several factors that are under the dialysis facility’s control that are related to whether a patient is on a transplant waitlist, including health maintenance and patient education. We agree that these are important responsibilities for which dialysis facilities should be held accountable; however, this measure does not assess these activities. In addition, the measure does not take important patient-level factors into account when calculating this rate. Characteristics including insurance status, sociodemographic factors, and patient choice—all of which are outside the dialysis facility’s control—will have a significant influence on whether a patient is waitlisted. The measure does not provide any benchmarks that suggest what desirable performance looks like. The AHA believes that there is no “right” percentage of patients to have on a waitlist; in fact, this percentage might vary based on the size or location of the dialysis facility. While we understand the need to hold ESRD facilities accountable and are certainly not in support of inappropriately prolonging dialysis for financial incentives, this measure would not provide actionable or meaningful information in evaluating ESRD treatment facilities. (Submitted by: American Hospital Association)
  
  
• The Federation of American Hospitals (FAH) supports the conditions placed on this measure and strongly encourages CMS to address each prior to implementation in any federal programs. FAH notes that there are explicit conditions that call upon specific NQF Committees to provide guidance, including requests for the Disparities Standing Committee to advise on the social risk factors in the risk adjustment approach and the Attribution Committee on the attribution methodology. It is FAH’s understanding that these committees do not have defined roles in reviewing measures in the Consensus Development Process at this time so it is unclear to what extent each will be able to provide input. For example, the Disparities Standing Committee was not asked to provide input on specific measures during the Socioeconomic Status Trial Period and it is not clear that their role is now expanded to advise on specific measures. The FAH would ask that NQF clearly define how it will seek input from these committees to ensure that the conditions placed on these measures will be met. (Submitted by: Federation of American Hospitals)
  
  
• Support MAP Recommendation. (Submitted by: Children's Hospital Association)
  
  

• will seek input from these committees to ensure that the conditions placed on these measures will be met. (Submitted by: American Medical Association)
  
  
• MUC 17-245—Standardized First Kidney Transplant Waitlist Ratio for Incident Dialysis Patients (SWR) KCP does not support MUC 17-241 or MUC 17-245 and disagrees with the MAP Hospital Workgroup recommendation of “Conditional Support” (pending NQF review and endorsement) of the Transplant Waitlist measures. KCP recognizes the tremendous importance of improving transplantation rates for patients with ESRD, but does not support the attribution to dialysis facilities of successful/unsuccessful waitlisting. KCP believes that while a referral to a transplant center, initiation of the waitlist evaluation process, or completion of the waitlist evaluation process may be appropriate facility-level measures that could be used in ESRD quality programs, the Percentage of Prevalent Patients Waitlisted (PPPW) and Standardized First Kidney Transplant Waitlist Ratio for Incident Dialysis Patients (SWR) are not. Waitlisting per se is a decision made by the transplant center and is beyond a dialysis facility’s locus of control. In reviewing these measures, we offer the following comments: Comments Relevant to both the PPPW and SWR Measures Several of KCP’s concerns apply to both the PPPW and SWR measures: • NQF endorsement. KCP appreciates the Workgroup recognizes the importance of NQF endorsement. We note NQF-endorsement is a general pre-requisite for KCP to support inclusion of a measure in any accountability program. • Facility attribution. KCP appreciates the Workgroup’s recommendation that the Waitlist measures also be reviewed by NQF’s Attribution Expert Panel to assess KCP’s and other stakeholders’ concerns about the measures’ attribution models. However, we strongly object to attributing successful/unsuccessful placement on a transplant waitlist to dialysis facilities and believe this is a fatal structural flaw. The transplant center decides whether a patient is placed on a waitlist, not the dialysis facility. One KCP member who is a transplant recipient noted there were many obstacles and delays in the evaluation process with multiple parties that had nothing to do with the dialysis facility—e.g., his private pay insurance changed the locations where he could be evaluated for transplant eligibility on multiple occasions, repeatedly interrupting the process mid-stream. Penalizing a facility each month through the PPPW and SWR for these or other delays is inappropriate; such misattribution is fundamentally misaligned with NQF’s first “Attribution Model Guiding Principle”, which states that measures’ attribution models should fairly and accurately assign accountability. KCP emphasizes our commitment to improving transplantation access, but we believe other measures with an appropriate sphere of control should be pursued. • Age as the only sociodemographic risk variable. KCP appreciates the Workgroup’s recommendation that the Waitlist measures also be reviewed by NQF’s Disparities Standing Committee to assess KCP’s and other stakeholders’ concerns about the measures’ risk of potentiating existing health inequities. KCP strongly believes age as the only sociodemographic risk variable is insufficient. We believe other biological and demographic variables are important, and not accounting for them is a significant threat to the validity of both measures. Transplant centers assess a myriad of demographic factors—e.g., family support, ability to adhere to medication regimens, capacity for follow-up, insurance-related issues, etc. Given transplant centers consider these types of sociodemographic factors, any waitlisting measure risk model should adjust for them. Of note, like the Access to Kidney Transplantation TEP, KCP does not support adjustment for waitlisting based on economic factors or by race or ethnicity. Geography, for instance, should be examined, since regional variation in transplantation access is significant. Waitlist times differ regionally, which will ultimately change the percentage of patients on the waitlist and impact performance measure scores. That is, facilities in a region with long wait times will “look” better than those in a region with shorter wait times where patients come off the list more rapidly—even if both are referring at the same rate. Additionally, criteria indicating a patient is “not eligible” for transplantation can differ by location—one center might require evidence of an absence of chronic osteomyelitis, infection, heart failure, etc., while another may apply them differently or have additional/ different criteria. The degree to which these biological factors influence waitlist placement must be accounted for in any model for the measure to be a valid representation of waitlisting. • Hospice exclusion. We note that an exclusion for patients admitted to hospice during the month of evaluation has been incorporated into both measures. KCP agrees that the transplantation access measures should not apply to persons with a limited life expectancy and so is pleased to see this revision. • Risk model fit. KCP appreciates the Workgroup’s recommendation that the Waitlist measures also be reviewed by NQF’s Scientific Methods Panel to assess KCP’s and other stakeholders’ concerns about the measures’ risk models. We note that risk model testing yielded an overall C-statistic of 0.72 for the PPPW and 0.67 for the SWR, raising concerns that the models will not adequately discriminate performance. Smaller units, in particular, might look worse than their actual performance. We reiterate our long-held position that a minimum C-statistic of 0.8 is a more appropriate indicator of a model’s goodness of fit, predictive ability, and validity to represent meaningful differences among facilities. • Stratification of reliability results by facility size. CMS has provided no stratification of reliability scores by facility size for either measure; we are thus unable to discern how widely reliability varies across the spectrum of facility sizes. We are concerned that the reliability for small facilities might be substantially lower than the overall IURs, as has been the case, for instance, with other CMS standardized ratio measures. This is of particular concern with the SWR, for which empiric testing has yielded an overall IUR of only 0.6—interpreted as “moderate” reliability by statistical convention. To illustrate our concern, the Standardized Transfusion Ratio for Dialysis Facilities (STrR) measure (NQF 2979) was also found to have an overall IUR of 0.60; however, the IUR was only 0.3 (“poor” reliability) for small facilities (defined by CMS as <=46 patients for the STrR). Without evidence to the contrary, KCP is thus concerned that SWR reliability is similarly lower for small facilities, effectively rendering the metric meaningless for use in performance measurement in this group of providers. KCP believes it is incumbent on CMS to demonstrate reliability for all facilities by providing data by facility size. • Meaningful differences in performance. We note that with large sample sizes, as here, even statistically significant differences in performance may not be clinically meaningful. A detailed description of measure scores, such as distribution by quartile, mean, median, standard deviation, outliers, should be provided to allow stakeholders to assess the measure and allow for a thorough review of the measures’ performance. Comments Relevant to SWR Only • Incident comorbidities incorporated into risk model. We note that eleven incident comorbidities—heart disease, inability to ambulate, inability to transfer, COPD, malignant neoplasm/cancer, PVD, CVD, alcohol dependence, drug dependence, amputation, and needs assistance with daily activities—have been incorporated into the SWR risk model. All are collected through the CMS Form 2728. As we have noted before, we continue to be concerned about the validity of the 2728 as a data source and urge CMS to work with the community to assess this matter. • Rate vs. ratio. Notwithstanding our many concerns regarding attribution and risk adjustment of this measure, consistent with our comments on other standardized ratio measures (e.g., SHR, SMR), KCP prefers normalized rates or year-over-year improvement in rates instead of a standardized ratio. We believe comprehension, transparency, and utility to all stakeholders is superior with a scientifically valid rate methodology. In sum and for the reasons stated above, KCP does not believe that the PPPW and SWR measures are appropriate for use in the ESRD QIP. (Submitted by: Kidney Care Partners)
  
  
• The AHA does not believe that this measure accurately gauges quality of care in the ESRD provider setting and thus does not support it for inclusion in the ESRD QIP. Wait-listing and transplantation are generally decisions made by the transplant center, and the dialysis facility does not and cannot influence patient inclusion on waitlists or transplants enough for this measure to provide any insight. The AHA has similar concerns about patient-level factors affecting this measure as we had for the related ESRD QIP measure, MUC17-241, Percentage of Prevalent Patients Waitlisted (PPPW). However, the SWR measure presents additional concerns that weaken its appeal. For example, this measure is specified such that it focuses on the first year of dialysis. While transplant is certainly an important goal for ESRD patients, “the decision of when to be placed on the dialysis list is driven by a number of factors, including clinical prognosis and patient preferences. For many patients, the first year on dialysis may be more appropriately focused on maintaining or improving function rather than getting ready for dialysis right away.” In addition to the conceptual issues presented here, the AHA is also concerned about the apparent statistical weakness of this measure. As explained by the developers, this measure’s c-statistic is 0.67. While the NQF does not have a floor that a c-statistic must meet, statisticians generally agree that a “good” predictive model has a c-statistic above 0.7, and a “strong” model above 0.8. A 0.67 c-statistic suggests that this model is only marginally better at predicting an outcome than random chance. Because of the conceptual and logistical weaknesses of this measure, we would not recommend it for inclusion in the ESRD QIP. (Submitted by: American Hospital Association)
  
  
• The Federation of American Hospitals (FAH) supports the conditions placed on this measure and strongly encourages CMS to address each prior to implementation in any federal programs. FAH notes that there are explicit conditions that call upon specific NQF Committees to provide guidance, including requests for the Disparities Standing Committee to advise on the social risk factors in the risk adjustment approach and the Attribution Committee on the attribution methodology. It is FAH’s understanding that these committees do not have defined roles in reviewing measures in the Consensus Development Process at this time so it is unclear to what extent each will be able to provide input. For example, the Disparities Standing Committee was not asked to provide input on specific measures during the Socioeconomic Status Trial Period and it is not clear that their role is now expanded to advise on specific measures. The FAH would ask that NQF clearly define how it will seek input from these committees to ensure that the conditions placed on these measures will be met. (Submitted by: Federation of American Hospitals)
  
  
• Support MAP Recommendation. (Submitted by: Children's Hospital Association)
  
  

• While the American Medical Association (AMA) is supportive of the collaborative process CMS has used in the development of these measures, we do not believe that they were ready for MAP consideration and did not receive adequate vetting by the Clinician Workgroup. There is also a consistent problem with the timeline to provide comments back to the MAP which greatly jeopardizes the integrity of the MAP process. The AMA is troubled over the lack of transparency and inconsistent application of the Measure Selection Criteria (MSC) to these episode-based cost measures. Specifically, no information regarding the individual measure specifications, attribution methodology, or reliability and validity testing results were released for member and public review prior to the MAP Clinician Workgroup meeting, modifications to the measures based on preliminary feedback are still being made, and, to our knowledge, the Workgroup members did not have any detailed information in front of them at the time of the discussion. The developer only cited some limited information on how the measures were developed and tested. Given the degree of interest from numerous medical specialty societies, the AMA looked forward to a robust and detailed discussion on each of these cost measures but unfortunately, it did not occur. (Submitted by: American Medical Association)
  
  
• Amgen does not agree with MAP’s recommendation of “conditional support” of MUC17-256 for inclusion in the Merit-based Incentive Payment System (MIPS). Instead, we recommend that this measure, in its current form, not be included in the program. While Amgen understands the need for additional resource use quality measures, we cannot support MAP’s recommendation of “conditional support” of MUC17-256 for inclusion in MIPS due to the same concerns raised by MAP and additionally because there is a lack of clarity regarding the true goal of the measure. Amgen agrees with MAP that there are several issues with the methodology of the measure, particularly with its risk adjustment. The risk adjustment model lacks completeness and requires additional work to guarantee all relevant risk factors are included. Its unclear methodology could lead to under-screening (“potential stinting of care” as noted in the MAP preliminary recommendations) in vulnerable populations because of clinician lack of clarity on attribution and adjustments. Additionally, there is confusion regarding the actual goal of the quality measure. The rationale description implies a desire for fewer numbers of colonoscopies; however, the measure description itself includes episode costs, which could lead to cheaper colonoscopies, but not necessarily effect their total number. The Episode-Based Cost Measure Field Test Report developed by CMS seems to confirm the nature of the measure as cheaper colonoscopies, not less. A clearer explanation of the purpose of the measure and additional details of the measure specifications would convey the exact purpose of the quality measure. Until these major issues are addressed, we recommend that this measure not be included in MIPS. (Submitted by: Amgen)
  
  

• The post-acute care prospective payment systems are undergoing significant revisions, with more planned in the future. At the same time that we are seeing changes in the payment systems themselves, in particular in a likely proposed rebasing of the SNF PPS (which CHA generally supports), post-acute care providers are implementing significant changes to the patient assessment tools. It is our understanding that CMS is also seeking to test patient-reported outcome measures as part of the patient assessment tool beta test. The timing of the implementation of this measure is of concern to CHA in light of the multiple priorities that CMS has placed on post-acute providers, including SNFs. We remain concerned about the timing of the adoption of this measure into a SNF QRP, likely around the same time as CMS will be implementing significant changes to the MDS. We caution CMS in moving forward with this measure prior to other changes planned over the next two years. Patient-reported measures are the most costly and burdensome to implement. The use of a proprietary tool, CoreQ, to collect the information is also of concern. Alternatives should be considered to lower the costs. Patient-reported data is too important to not track correctly, so the timing of implementation is very important. CHA believes adoption for the FFY 2019 rulemaking, for likely a 2021 payment determination, is premature knowing that the beta test is not yet complete and the total burden of measurement is unknown at this time. While we do not oppose this measure, our concerns relate to timing and, therefore, we caution CMS in its timeline for adoption and ask NQF to consider more clearly articulating the concerns in its final report. (Submitted by: California Hospital Association)
  
  

• While the American Medical Association (AMA) is supportive of the collaborative process CMS has used in the development of these measures, we do not believe that they were ready for MAP consideration and did not receive adequate vetting by the Clinician Workgroup. There is also a consistent problem with the timeline to provide comments back to the MAP which greatly jeopardizes the integrity of the MAP process. The AMA is troubled over the lack of transparency and inconsistent application of the Measure Selection Criteria (MSC) to these episode-based cost measures. Specifically, no information regarding the individual measure specifications, attribution methodology, or reliability and validity testing results were released for member and public review prior to the MAP Clinician Workgroup meeting, modifications to the measures based on preliminary feedback are still being made, and, to our knowledge, the Workgroup members did not have any detailed information in front of them at the time of the discussion. The developer only cited some limited information on how the measures were developed and tested. Given the degree of interest from numerous medical specialty societies, the AMA looked forward to a robust and detailed discussion on each of these cost measures but unfortunately, it did not occur. (Submitted by: American Medical Association)
  
  
• We strongly support the above-referenced measure, but urge CMS to strengthen this measure further by extending the period for this episode to one or two years beyond the total knee replacement discharge date. This would enable capturing key short-term episode costs while also making a small step toward capturing quality beyond 90-day episode periods. Specifically, it would capture short-term revisions, which at one to two years are about 2 to 3% for Medicare patients. This would allow the measure to capture both short-term quality and some measurement of costs associated with short-term revisions. The need to capture TKA quality beyond 90 days of the bundle was highlighted by a recent report from Discern Health, who reported on measure gaps for many technology spaces late last year. In their White Paper, “Medical Technology in the Value-Based Environment: An Assessment of Quality Measure Gaps ” Discern identified a key gap, namely the need for a Risk-Adjusted Multi-Year Revision Rate Outcome measure. In the absence of a measure that specifically targets revision rates, we believe that extending the time for this knee replacement cost measure capture differences in costs associated with higher or lower short-term revision rates. We ask that CMS consider extending this measure, in modified form, to total hip replacement as well. (Submitted by: Smith & Nephew )
  
  
• AdvaMed strongly supports the above-referenced measure, but urges CMS to consider strengthen this measure further by extending the period for this episode to one or two years beyond the total knee arthroplasty (TKA) discharge date. This would enable capturing key short-term episode costs that are the focus of CMS bundled payment programs, while also making a small step toward capturing quality beyond 90-day episode periods. Specifically, it would capture short-term revisions, which at one to two years are about 2 to 3% for Medicare patients. This would allow the measure to capture both short-term quality and some measurement of costs associated with short-term revisions. The need to capture TKA quality beyond 90 days of the bundle was highlighted by a recent report from Discern Health, who reported on measure gaps for many technology spaces late last year, including total joint replacement measures. In their White Paper, “Medical Technology in the Value-Based Environment: An Assessment of Quality Measure Gaps ” Discern identified a key gap, namely the need for a Risk-Adjusted Multi-Year Revision Rate Outcome measure. In the absence of a measure that specifically targets TKA revision rates, we believe that extending the time for this Knee Arthroplasty MUC would at least capture differences in costs associated with higher or lower short-term revision rates. We also ask that CMS consider extending this measure, in modified form, to total hip arthroplasty (THA). (Submitted by: AdvaMed)
  
  
• We support the MAP’s recommendation for this measure. We also urge the Cost and Resource Use Standing Committee to specifically consider the need for adjusting for societal risk factors. (Submitted by: Johns Hopkins Armstrong Institute)
  
  

• While the American Medical Association (AMA) is supportive of the collaborative process CMS has used in the development of these measures, we do not believe that they were ready for MAP consideration and did not receive adequate vetting by the Clinician Workgroup. There is also a consistent problem with the timeline to provide comments back to the MAP which greatly jeopardizes the integrity of the MAP process. The AMA is troubled over the lack of transparency and inconsistent application of the Measure Selection Criteria (MSC) to these episode-based cost measures. Specifically, no information regarding the individual measure specifications, attribution methodology, or reliability and validity testing results were released for member and public review prior to the MAP Clinician Workgroup meeting, modifications to the measures based on preliminary feedback are still being made, and, to our knowledge, the Workgroup members did not have any detailed information in front of them at the time of the discussion. The developer only cited some limited information on how the measures were developed and tested. Given the degree of interest from numerous medical specialty societies, the AMA looked forward to a robust and detailed discussion on each of these cost measures but unfortunately, it did not occur. (Submitted by: American Medical Association)
  
  
• Again, we question whether measuring cost alone will provide actionable data for individual providers or provider groups. Much of the cost data from Medicare claims will not reflect system drivers of increased cost such as labor utilization. The risk adjustment model used in this measure addresses many of the most serious comorbidities and clinical factors, however, the size of the patient’s infarct, degree of any valvular insufficiency, LVEF measurement and Killip Class are not included. We assume this is because these clinical factors are not available in claims data, however, these are important determinants of severity that could be associated with the need for higher resource utilization. We also share the concern expressed by the MAP regarding the potential stinting of care and undertreatment, especially for the sickest patients. For example, providers in tertiary/academic centers could be incentivized to limit revascularization of STEMI patients with multi-vessel CAD in order to minimize costs. There is growing supporting evidence for multi-vessel PCI in selected STEMI patients without shock, to reduce future revascularization events. Such cutting-edge approaches may be more widely adopted in academic centers currently. Given that this is a dynamic and growing practice pending definitive confirmation by studies such as the ongoing COMPLETE clinical trial, we are concerned that the cost modeling in the measure will quickly become outdated and may disadvantage such early adopters and restrict patients’ access to the best possible care. Delivering the best possible care to patients with STEMI relies on effective systems of care. We would suggest that, instead of attributing costs for an episode of care to individual clinicians, CMS take a more regional approach to STEMI care and cost. Given the many factors (geography, resources, legislation, regulation, etc.) that affect overall system performance, we believe that greater strides in improving the quality and cost effectiveness of care can be achieved if attribution of care and costs is at the city, county, state or regional level. The AHA has worked for many years through its Mission: Lifeline® program to promote improved systems of regional and national STEMI care by helping communities and regions form effective coalitions of hospitals, ambulance services, non-transport medical first response agencies, emergency communications centers, emergency medical service regulatory/ medical direction agencies, local government, local media, and payers. With this broader focus, CMS could recognize and reward regions of the country for their excellence, more effectively promoting well-coordinated systems that provide rapid, high-quality, cost-effective STEMI care. (Submitted by: American Heart Association/American Stroke Association)
  
  
• Submitted on behalf of Richard Kovacs, MD, FACC: As Chair of the American College of Cardiology’s Science and Quality Committee, I agree that risk adjustment of performance measures creates an equitable environment for clinicians and health systems in comparing health outcomes. While the collection of socioeconomic data has its challenges, I recommend that the American College of Cardiology Foundation’s (ACCF) National Cardiovascular Data Registry (NCDR™) risk adjustment methodology be considered for purposes of risk adjustment in this measure. The methodology utilized in these registries integrate non-billed clinical data as well as transactional data, as opposed to claims data which may not capture all risk factors. Specifically, the NCDR™ uses statistical models to account for patient risk factors that are present prior to PCI, which would allow a higher degree of accuracy in predicting risk for these patient groups. Additional benefits of the resulting analyses include the ability to benchmark performance and enhance quality improvement efforts at participating hospitals. The methodologies are publicly available in the literature but are also available through NCDR™. (Submitted by: American College of Cardiology)
  
  

• While the American Medical Association (AMA) is supportive of the collaborative process CMS has used in the development of these measures, we do not believe that they were ready for MAP consideration and did not receive adequate vetting by the Clinician Workgroup. There is also a consistent problem with the timeline to provide comments back to the MAP which greatly jeopardizes the integrity of the MAP process. The AMA is troubled over the lack of transparency and inconsistent application of the Measure Selection Criteria (MSC) to these episode-based cost measures. Specifically, no information regarding the individual measure specifications, attribution methodology, or reliability and validity testing results were released for member and public review prior to the MAP Clinician Workgroup meeting, modifications to the measures based on preliminary feedback are still being made, and, to our knowledge, the Workgroup members did not have any detailed information in front of them at the time of the discussion. The developer only cited some limited information on how the measures were developed and tested. Given the degree of interest from numerous medical specialty societies, the AMA looked forward to a robust and detailed discussion on each of these cost measures but unfortunately, it did not occur. (Submitted by: American Medical Association)
  
  
• In addition to the overall concerns expressed above regarding these cost measures, we believe that this measure should have a longer measurement period to encompass, or somehow consider, amputation prevention. Increased efforts to revascularize more extensively with atherectomy may generate more up-front costs but yield longer term improved outcomes, including amputation prevention, and cost savings. (Submitted by: American Heart Association/American Stroke Association)
  
  

• It does not specify vaccine. The original vaccine was barely 60% effective for only 5 years and no provision for a booster. This means that if given at age 60 you may be inadvertently increasing a person's Shingles episode to an age 10-20 years later. An episode at age 90 is often lethal. The new vaccine although rate more effective will not show its true value until after 2 years of side-spread use. ? PCP status ? PCP Status ? PCP Status (Submitted by: Family Health Care, P.C.)
  
  

• While the American Medical Association (AMA) is supportive of the collaborative process CMS has used in the development of these measures, we do not believe that they were ready for MAP consideration and did not receive adequate vetting by the Clinician Workgroup. There is also a consistent problem with the timeline to provide comments back to the MAP which greatly jeopardizes the integrity of the MAP process. The AMA is troubled over the lack of transparency and inconsistent application of the Measure Selection Criteria (MSC) to these episode-based cost measures. Specifically, no information regarding the individual measure specifications, attribution methodology, or reliability and validity testing results were released for member and public review prior to the MAP Clinician Workgroup meeting, modifications to the measures based on preliminary feedback are still being made, and, to our knowledge, the Workgroup members did not have any detailed information in front of them at the time of the discussion. The developer only cited some limited information on how the measures were developed and tested. Given the degree of interest from numerous medical specialty societies, the AMA looked forward to a robust and detailed discussion on each of these cost measures but unfortunately, it did not occur. (Submitted by: American Medical Association)
  
  
• As noted for the other cost measures, measuring cost alone, without considering patient outcomes or the appropriateness of the care provided, may produce misleading results that do not contribute to better quality or value. Performing inappropriate elective PCI may be relatively low cost per patient, but increases overall costs to the health care system without improving patient outcomes. (Submitted by: American Heart Association/American Stroke Association)
  
  
• Submitted on behalf of Richard Kovacs, MD, FACC: As Chair of the American College of Cardiology’s Science and Quality Committee, I agree that risk adjustment of performance measures creates an equitable environment for clinicians and health systems in comparing health outcomes. While the collection of socioeconomic data has its challenges, I recommend that the American College of Cardiology Foundation’s (ACCF) National Cardiovascular Data Registry (NCDR™) risk adjustment methodology be considered for purposes of risk adjustment in this measure. The methodology utilized in these registries integrate non-billed clinical data as well as transactional data, as opposed to claims data which may not capture all risk factors. Specifically, the NCDR™ uses statistical models to account for patient risk factors that are present prior to PCI, which would allow a higher degree of accuracy in predicting risk for these patient groups. Additional benefits of the resulting analyses include the ability to benchmark performance and enhance quality improvement efforts at participating hospitals. The methodologies are publicly available in the literature but are also available through NCDR™. (Submitted by: American College of Cardiology)
  
  

• While the American Medical Association (AMA) is supportive of the collaborative process CMS has used in the development of these measures, we do not believe that they were ready for MAP consideration and did not receive adequate vetting by the Clinician Workgroup. There is also a consistent problem with the timeline to provide comments back to the MAP which greatly jeopardizes the integrity of the MAP process. The AMA is troubled over the lack of transparency and inconsistent application of the Measure Selection Criteria (MSC) to these episode-based cost measures. Specifically, no information regarding the individual measure specifications, attribution methodology, or reliability and validity testing results were released for member and public review prior to the MAP Clinician Workgroup meeting, modifications to the measures based on preliminary feedback are still being made, and, to our knowledge, the Workgroup members did not have any detailed information in front of them at the time of the discussion. The developer only cited some limited information on how the measures were developed and tested. Given the degree of interest from numerous medical specialty societies, the AMA looked forward to a robust and detailed discussion on each of these cost measures but unfortunately, it did not occur. (Submitted by: American Medical Association)
  
  
• The AHA and ASA fully support the NQS priority of making care more affordable; we also strongly believe that affordability cannot be divorced from effectiveness. We are very skeptical that measuring cost alone, without placing it in the context of the outcomes achieved or the appropriateness of the care provided, will yield actionable data for providers or improve the quality of care. Perhaps more importantly, a measure of cost alone, even if adequately risk adjusted, may also lead to serious unintended consequences. Putting costs ahead of patient safety and quality creates the potential for great harm to vulnerable patients. In addition, the risk adjustment models for these measures rely on claims data alone, which lacks the granularity to identify important clinical factors affecting costs and outcomes. The AHA and ASA strongly oppose this measure for use in any accountability or reporting application, including cost profiling. The most fundamental problem with the measure is one that the MAP itself acknowledged in their comments. The measure mixes conditions that are largely medically treated (intracerebral hemorrhage and ischemic stroke) with conditions that are neurosurgical (subarachnoid hemorrhage and subdural hematoma). Given the variation in these conditions and the types of clinicians that care for them, we are very concerned that combining these conditions in one measure is likely to produce severe distortions in the modeling. Neurologists that care for mostly ischemic stroke patients will have lower than average costs, while intensivists and neurosurgeons that care for patients with intracerebral and subarachnoid hemorrhage will have higher than average costs. In addition, while ischemic strokes are widely distributed across US hospitals, hemorrhagic strokes tend to get transferred into a much smaller group of centers with regional concentration at advanced stroke centers. Within hemorrhagic stroke, patients with subarachnoid hemorrhage will have much higher costs due to procedural interventions compared to most patients with intracerebral hemorrhage. We are also very concerned that the risk model utilized in this measure lacks sufficient discrimination to accurately classify provider performance. Although the list of variables included in the risk model is extensive, it does not consider some key clinical factors. It is also unclear if the factors included must be present on arrival vs. occur during the episode of care. Most notably, the model does not include a measure of stroke severity. If CMS intends to proceed with this measure, the risk model should include a validated measure of stroke severity, such as the NIH Stroke Score (NIHSS), which can now be captured in claims data using ICD-10-CM codes. A measure of stroke severity is essential for optimal discrimination of risk, given the great variability in stroke severity and outcomes. As noted above, we believe there is a distinct risk of unintended consequences if cost alone is measured. Providers may be incentivized to avoid higher cost, but appropriate, interventions or to discharge patients to lower cost settings of care for post-stroke rehabilitation, which may be counterproductive to achieving the best clinical outcomes. (Submitted by: American Heart Association/American Stroke Association)
  
  
• While we support the MAP’s recommendation for this measure, we would urge the measure developers to consider whether or not this measure accounts for recent developments in stroke and cerebral hemorrhage care. The cost of care at comprehensive stroke centers is higher than that of primary centers since the comprehensive stroke centers have access to far more interventions that are not available elsewhere. Measures should not create disincentives for innovation in care. (Submitted by: Johns Hopkins Armstrong Institute)
  
  
• We agree with the MAP's concerns, but do not believe this measure in its current form is ready for implementation. We agree with the MAP concern that the method by which these two different populations are risk-adjusted remains opaque. The data is clear that ICH patients do worse. More work is needed prior to using this measure for accountability. (Submitted by: AANS)
  
  

• While the American Medical Association (AMA) is supportive of the collaborative process CMS has used in the development of these measures, we do not believe that they were ready for MAP consideration and did not receive adequate vetting by the Clinician Workgroup. There is also a consistent problem with the timeline to provide comments back to the MAP which greatly jeopardizes the integrity of the MAP process. The AMA is troubled over the lack of transparency and inconsistent application of the Measure Selection Criteria (MSC) to these episode-based cost measures. Specifically, no information regarding the individual measure specifications, attribution methodology, or reliability and validity testing results were released for member and public review prior to the MAP Clinician Workgroup meeting, modifications to the measures based on preliminary feedback are still being made, and, to our knowledge, the Workgroup members did not have any detailed information in front of them at the time of the discussion. The developer only cited some limited information on how the measures were developed and tested. Given the degree of interest from numerous medical specialty societies, the AMA looked forward to a robust and detailed discussion on each of these cost measures but unfortunately, it did not occur. (Submitted by: American Medical Association)
  
  

• It seems, as a PCP for 40 years, that the HIV test always requires informed consent. Whether or not a person needs a possible repeat test just for the physician to augment their quality performance status would not be a good quality measure to GAME the system. For a given encounter, this measure without knowing a person's history would be inaccurate. (Submitted by: Family Health Care, P.C.)
  
  

Navigating the Discussion Guide

• How do I get back to the section I was just looking at?
  The easiest way is to use the back button on your browser. Other options are using your backspace button (which works for many browsers on laptops), or using the permanent links at the upper right hand corner of the discussion guide. But the back button is the best choice in most situations.
  
  
• Can I print the discussion guide out?
  You can, but we don't recommend it. Besides using a lot of paper (probably a couple hundred pages at least), you'll lose all the links that allow you to move around the document. For instance, if you're scrolling through the agenda and want to see more information about a particular measure, the electronic format will allow you to click a link, read more, and then bo back. If you're on paper, there will be a lot of flipping through paper.
  
  
• If I can't print this out, how can I read it on the plane?
  Although the Discussion Guide opens in a web browser, it does not require an internet connection if you have downloaded and saved the HTML file to your hard drive.
  
  
• How do I know that I'm looking at the most recent version?
  At the top left corner of the discussion guide is a version number. At the beginning of the in person meetings, the NQF staff will ask everyone to load the most recent discussion guide version and will check that everyone has the same version loaded.
  
  
• What electronic devices can I use to view the discussion guide?
  We tried to make this as universal as possible, so it should work on your laptop (PC, Mac, Linux), your tablet (iPad, Android), or your phone (iPhone, Android). It should also work on many types of browsers (IE, Firefox, Chrome, Safari, Opera, Dolphin,....). Please let us know if you have any problems, and we'll troubleshoot with you (and improve the discussion guide for the next go around).
  
  
• Why do I see weird characters in some places?
  Because we're joining data from many different sources, we do find some technical challenges. This generally shows up as strange characters--extra question marks, accented characters, or otherwise unusual items. We've been able to fix many of these problems, but not all. We ask that you bear with us as we improve this over time!
  
  

Content

• What is included in the discussion guide?
  There are four sections within this document:
  • Agenda, with summaries of each measure under consideration
  • Full information about each measure, including its specifications, preliminary analysis of how this measure can advance the program's goals, and the rationale by HHS for being included in the list
  • Summaries for each federal health program being considered
  • Public comments that have been received to date (Note that the discussion guide may be released before the public comment period is finished, in which case there will just be a placeholder for where comments will go)
  
  
• How are the meeting discussions organized?
  The meeting sessions are organized around consent calendars, which are groups of measures being considered for a particular program or groups of measures for a particular condition or topic area. For each measure being discussed, this document will show you the description, the public comments (if any), the summary of the preliminary analysis, and the result of the preliminary analysis algorithm.

Remote Participation Instructions:

• Direct your web browser to: http://nqf.commpartners.com/.
• Under “Enter a Meeting” type in the meeting number for Day 1: 942049
• In the “Display Name” field, type in your first and last names and click “Enter Meeting.”

• Dial (877) 793-5566 for public participants to access the audio platform.