NQF

Agenda Synopsis

Full Agenda

Measure Index

• Hospital Visits following General Surgery Ambulatory Surgical Center Procedures (Public comments received:10; MUC ID: MUC17-233)
  
  

• Medication Reconciliation for Patients Receiving Care at Dialysis Facilities (Public comments received:3; MUC ID: MUC17-176)
  
  
• Percentage of Prevalent Patients Waitlisted (PPPW) (Public comments received:3; MUC ID: MUC17-241)
  
  
• Standardized First Kidney Transplant Waitlist Ratio for Incident Dialysis Patients (SWR) (Public comments received:3; MUC ID: MUC17-245)
  
  

• Hospital-Wide All-Cause Risk Standardized Mortality Measure (Public comments received:10; MUC ID: MUC17-195)
  
  
• Hybrid Hospital-Wide All-Cause Risk Standardized Mortality Measure (Public comments received:9; MUC ID: MUC17-196)
  
  
• Hospital Harm Performance Measure: Opioid Related Adverse Respiratory Events (Public comments received:18; MUC ID: MUC17-210)
  
  

• Lumbar Spine Imaging for Low Back Pain (Public comments received:2; MUC ID: MUC17-223)
  
  

• 30-Day Unplanned Readmissions for Cancer Patients (Public comments received:7; MUC ID: MUC17-178)
  
  

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
  
  

Preliminary Analysis of Measure

• Preliminary analysis result: Conditional Support for Rule Making
  
  
• Preliminary analysis summary
  • Contribution to program measure set:This is a fully developed measure that addresses an important health outcome for patients receiving care at ASC general surgery centers. This measure should be sumbitted and reviewed for NQF endorsement.
  • Impact on quality of care for patients:This measure would encourage ASCs to implement strategies to reduce unplanned hospital visits following general surgery procedures.
  
  
  
• Does the measure address a critical quality objective not adequately addressed by the measures in the program set? Yes. This measure addresses NQS priorities of making care safer by reducing harm caused in the delivery of care and promoting effective communication and coordination of care. High priority domains for the ASCQR program include the goal “To reduce unexpected hospital/emergency visits and admissions.”
  
  
• Is the measure evidence-based and is either strongly linked to outcomes or an outcome measure? Yes. This is an outcome measure.
  
  
• Does the measure address a quality challenge? Yes. Several factors make unanticipated hospital visits a priority quality indicator. 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.
  
  
• Does the measure contribute to efficient use of resources and/or support alignment of measurement across programs? Yes. This measure contributes to alignment between care settings. ASC providers may not currently be aware of all post-surgical hospital visits that occur among their patients. The general surgery ASC measure cohort includes surgical procedures that are not covered by the urology ASC and orthopedic ASC measures. Specifically, the general surgery cohort includes the following types of procedures: abdominal, alimentary tract, breast, skin/soft tissue, wound, and varicose vein. This measure would be complementary to current measures in the program measure set, such as NQF # 0265: All-Cause Hospital Transfer/ Admission and NQF #2539: Facility 7-Day Risk-Standardized Hospital Visit Rate after Outpatient Colonoscopy. The measure is also related to two measures proposed for incorporation into the program in 2022, Hospital visits after urology ASC procedures (Not yet submitted to NQF) and Hospital visits after orthopedic ASC procedures (Not yet submitted to NQF).
  
  
• Can the measure can be feasibly reported? Yes. This measure is fully specified and the data source is Medicare administrative claims and enrollment data.
  
  
• Is the measure reliable and valid for the level of analysis, program, and/or setting(s) for which it is being considered? Yes. This measure is fully developed and specifications are provided. Testing results and risk adjustment model performance should be assessed
  
  
• Measure development status: Fully Developed
  
  
• If the measure is in current use, do the benefits of the measure outweigh any unreasonable implementation issues that have been identified? N/A. This is a new measure, not currently in program.
  
  
• Is the measure NQF endorsed for the program's setting and level of analysis? Never Submitted
  
  
• Does the measure address a high-priority quality issue in the dual eligible beneficiary population? Yes.
  
  

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
  
  

Preliminary Analysis of Measure

• Preliminary analysis result: Support for Rulemaking
  
  
• Preliminary analysis summary
  • Contribution to program measure set:This NQF endorsed measure addresses two priority areas for the ESRD-QIP program, safety and care coordination. The measure addresses a safety issue (medication reconciliation) that is not currently included in the program measure set.
  • Impact on quality of care for patients:This measure encourages dialysis facilities to perform and document medication reconciliation for their patients. Medication reconciliation is a critical safety issue for all patients, but particularly patients with end-stage renal disease (ESRD). Individuals with ESRD frequently require 10 or more medications and take an average of 17-25 doses per day.
  
  
  
• Does the measure address a critical quality objective not adequately addressed by the measures in the program set? Yes. This measure addresses NQS priorities of making care safer by reducing harm caused in the delivery of care and promoting effective communication and coordination of care. The ESRD-QIP program identified care coordination and safety as high-priority domains for future measure consideration.
  
  
• Is the measure evidence-based and is either strongly linked to outcomes or an outcome measure? Yes. This is a process measure that assesses whether dialysis facilities are performing and documenting medication reconciliation for their patients. Medication management is a critical safety issue for all patients, but especially so for patients with ESRD.
  
  
• Does the measure address a quality challenge? Yes. The measure addresses a preformance gap identified during measure testing. Findings from three KCQA member dialysis organizations found that mean performance score was found to be 52.62%, with a standard deviation of 32.83.
  
  
• Does the measure contribute to efficient use of resources and/or support alignment of measurement across programs? Yes. This measure would not be duplicative of other measures in the program as there are not currently any measures relating to medication management in the ESRD QIP program.
  
  
• Can the measure can be feasibly reported? Yes. This measure is fully specified, all data elements are defined in fields in electronic health records, and the measure is generated or collected by and used by healthcare personnel during the provision of care.
  
  
• Is the measure reliable and valid for the level of analysis, program, and/or setting(s) for which it is being considered? Yes. This measure was reviewed in NQFs 2016 Patient Safety Project. Reliability of the measure was tested at the score level using beta-binomial testing. The mean reliability score is 0.9935. Further, there was a systematic assessment of face validity by experts. 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.
  
  
• Measure development status: Fully Developed
  
  
• If the measure is in current use, do the benefits of the measure outweigh any unreasonable implementation issues that have been identified? N/A. No unintended consequences were identified during testing.
  
  
• Is the measure NQF endorsed for the program's setting and level of analysis? Endorsed
  
  

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
  
  

Preliminary Analysis of Measure

• Preliminary analysis result: Conditional Support for Rule Making
  
  
• Preliminary analysis summary
  • Contribution to program measure set:This fully developed measure addresses an important quality gap for dialysis facilities. This measure should be submitted for NQF endorsement and review.
  • Impact on quality of care for patients:This measure would encourage dialysis facilities to ensure that patients remain healthy, and complete any ongoing testing activities required to remain on the waitlist.
  
  
  
• Does the measure address a critical quality objective not adequately addressed by the measures in the program set? Yes. This measure addresses the NQS priority of promoting the most effective prevention and treatment practices for the leading causes of mortality. The ESRD QIP has identified “Access to Transplantation’ as a high priority domain for future measure consideration. Obtaining a transplant is an extended process for dialysis patients, including education, referral, wait listing, transplantation, and follow-up care. The care and information available from dialysis facilities are integral to the process.
  
  
• Is the measure evidence-based and is either strongly linked to outcomes or an outcome measure? Yes. This is a process measure that tracks the percentage of prevalent patients at each dialysis facility who were on the kidney or kidney-pancreas transplant waiting list. Waitlisting is a necessary step prior to potential receipt of a deceased donor kidney. 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.
  
  
• Does the measure address a quality challenge? Yes. This measure addresses a topic with a preformance gap.Wide regional variations in waitlisting rates highlight substantial room for improvement for this process measure. Studies have found that adjusted wait-list rates ranged from 37% lower to 64% 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.
  
  
• Does the measure contribute to efficient use of resources and/or support alignment of measurement across programs? Yes. This measure is not duplicative of existing measures in the programs and is of high value to patients/consumers. It is complementary to MUC17-245 Standardized First Kidney Transplant Waitlist Ratio for Incident Dialysis Patients (SWR).
  
  
• Can the measure can be feasibly reported? Yes. The measure is fully developed and tested. The measure is calcuated using administrative claims data.
  
  
• Is the measure reliable and valid for the level of analysis, program, and/or setting(s) for which it is being considered? Yes. This measure is fully developed and specificed. CMS is currently awaiting an opportunity to submit the measure the NQF for consideration of endorsement.
  
  
• Measure development status: Fully Developed
  
  
• If the measure is in current use, do the benefits of the measure outweigh any unreasonable implementation issues that have been identified? N/A. This is a new measure never used in a program.
  
  
• Is the measure NQF endorsed for the program's setting and level of analysis? Never Submitted
  
  
• Does the measure address a high-priority quality issue in the dual eligible beneficiary population? Yes; Dually eligible Medicare beneficiaries can have a Medicare status of elderly, disabled or End Stage Renal Disease (ESRD)..
  
  

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
  
  

Preliminary Analysis of Measure

• Preliminary analysis result: Conditional Support for Rulemaking
  
  
• Preliminary analysis summary
  • Contribution to program measure set:This fully developed measure addresses an important quality gap for dialysis facilities. This measure should be submitted for NQF endorsement and review.
  • Impact on quality of care for patients:This measure would encoruage dialysis facilities to ensure that patients receive support for transplant waitlisting process and to optimize the health and functional status of patients in order to increase their candidacy for transplant waitlisting.
  
  
  
• Does the measure address a critical quality objective not adequately addressed by the measures in the program set? Yes. This measure addresses the NQS priority of promoting the most effective prevention and treatment practices for the leading causes of mortality. The ESRD QIP has identified “Access to Transplantation’ as a high priority domain for future measure consideration. Obtaining a transplant is an extended process for dialysis patients, including education, referral, wait listing, transplantation, and follow-up care. The care and information available from dialysis facilities are integral to the process.
  
  
• Is the measure evidence-based and is either strongly linked to outcomes or an outcome measure? Yes . This is a process measure that 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. Waitlisting is a necessary step prior to potential receipt of a deceased donor kidney. 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.
  
  
• Does the measure address a quality challenge? Yes. This measure addresses a topic with a preformance gap.
  
  
• Does the measure contribute to efficient use of resources and/or support alignment of measurement across programs? Yes. This measure is not duplicative of existing measures in the programs and is of high value to patients/consumers. It is complementary to MUC17-241 Percentage of Prevalent Patients Waitlisted (PPPW).
  
  
• Can the measure can be feasibly reported? Yes. The measure is calcuated using administrative claims data.
  
  
• Is the measure reliable and valid for the level of analysis, program, and/or setting(s) for which it is being considered? Yes. This measure is fully developed and specified. CMS is currently awaiting an opportunity to submit the measure the NQF for consideration of endorsement.
  
  
• Measure development status: Fully Developed
  
  
• If the measure is in current use, do the benefits of the measure outweigh any unreasonable implementation issues that have been identified? N/A. This is a new measure never used in a program.
  
  
• Is the measure NQF endorsed for the program's setting and level of analysis? Never Submitted
  
  
• Does the measure address a high-priority quality issue in the dual eligible beneficiary population? Yes; Dually eligible Medicare beneficiaries can have a Medicare status of elderly, disabled or End Stage Renal Disease (ESRD)..
  
  

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
  
  

Preliminary Analysis of Measure

• Preliminary analysis result: Conditional Support for Rulemaking
  
  
• Preliminary analysis summary
  • Contribution to program measure set:This measure is fully developed and specified and compliments the existing CMS Hospital-Wide All-Cause Risk-Standardized Readmission Measure (NQF #1789). Testing results should demonstrate reliability and validity at the facility level in the acute care setting. This measure should be submitted to NQF for review and endorsement.
  • Impact on quality of care for patients:This measure encourages hospitals to reduce the number of patient deaths (from any cause) within 30 days after admission, including in-hospital deaths.
  
  
  
• Does the measure address a critical quality objective not adequately addressed by the measures in the program set? Yes. The measure promotes patient and family engagement, one of the high priority domains for future measure consideration identified by CMS. It also promotes making care safer, effective communication/care coordination, and effective prevention and treatment practices.
  
  
• Is the measure evidence-based and is either strongly linked to outcomes or an outcome measure? Yes. A hospital-level, 30-day all-cause mortality measure will provide hospitals with an incentive to reduce mortality through encouraging a culture of safety, improved coordination of care, and strengthen incentives for quality of care improvement. A hospital-wide mortality measure allows for a broader statement about a hospital’s performance, capturing cross-cutting hospital-wide characteristics that also contribute to quality of care, and can meaningfully capture performance for small-volume hospitals. By measuring hospital-wide mortality, CMS can ensure that efforts to reduce other outcomes, such as readmissions and resource utilization, are not resulting in unintended consequences.
  
  
• Does the measure address a quality challenge? Yes. Mortality is the focus of existing CMS condition- and procedure-specific quality measures. The existing condition- and procedure-specific mortality measures provide specificity for targeted quality improvement work and may have contributed to national declines in hospital mortality rates for measured conditions.1 They do not, however, allow broader statements about a hospital’s performance, nor do they meaningfully capture performance for small-volume hospitals. Current mortality measures may not capture cross-cutting hospital-wide characteristics that also contribute to quality of care but may be difficult to measure. Some of these factors include a global culture of safety, good communication across teams, multidisciplinary care teams, coordination with community services and efforts, and effective care transitions.
  
  
• Does the measure contribute to efficient use of resources and/or support alignment of measurement across programs? Yes. The measure is intended to complement the existing CMS Hospital-Wide All-Cause Risk-Standardized Readmission Measure (NQF #1789) to allow assessment of trends in hospital performance for both readmission and mortality outcomes, similar to other complementary pairs of readmission and mortality measures for specific conditions and procedures.
  
  
• Can the measure can be feasibly reported? Yes. The measure uses Medicare administrative claims data.
  
  
• Is the measure reliable and valid for the level of analysis, program, and/or setting(s) for which it is being considered? No. The measure is fully developed but testing has not been completed .
  
  
• Measure development status: Fully Developed
  
  
• If the measure is in current use, do the benefits of the measure outweigh any unreasonable implementation issues that have been identified? N/A. The measure is not currently in use.
  
  
• Is the measure NQF endorsed for the program's setting and level of analysis? Never Submitted
  
  

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
  
  

Preliminary Analysis of Measure

• Preliminary analysis result: Conditional Support for Rulemaking
  
  
• Preliminary analysis summary
  • Contribution to program measure set:This measure is fully developed and specified and compliments the existing CMS Hospital-Wide All-Cause Risk-Standardized Readmission Measure (NQF #1789). Testing results should demonstrate reliability and validity at the facility level in the acute care setting. This measure should be submitted to NQF for review and endorsement.
  • Impact on quality of care for patients:This measure encourages hospitals to reduce the number of patient deaths (from any cause) within 30 days after admission, including in-hospital deaths.
  
  
  
• Does the measure address a critical quality objective not adequately addressed by the measures in the program set? Yes. The measure promotes patient and family engagement, one of the high priority domains for future measure consideration identified by CMS. It also promotes making care safer, effective communication/care coordination, and effective prevention and treatment practices.
  
  
• Is the measure evidence-based and is either strongly linked to outcomes or an outcome measure? Yes. A hospital-level, 30-day all-cause mortality measure will provide hospitals with an incentive to reduce mortality through encouraging a culture of safety, improved coordination of care, and strengthen incentives for quality of care improvement. A hospital-wide mortality measure allows for a broader statement about a hospital’s performance, capturing cross-cutting hospital-wide characteristics that also contribute to quality of care, and can meaningfully capture performance for small-volume hospitals. By measuring hospital-wide mortality, CMS can ensure that efforts to reduce other outcomes, such as readmissions and resource utilization, are not resulting in unintended consequences.
  
  
• Does the measure address a quality challenge? Yes. Mortality is the focus of existing CMS condition- and procedure-specific quality measures. The existing condition- and procedure-specific mortality measures provide specificity for targeted quality improvement work and may have contributed to national declines in hospital mortality rates for measured conditions.1 They do not, however, allow broader statements about a hospital’s performance, nor do they meaningfully capture performance for small-volume hospitals. Current mortality measures may not capture cross-cutting hospital-wide characteristics that also contribute to quality of care but may be difficult to measure. Some of these factors include a global culture of safety, good communication across teams, multidisciplinary care teams, coordination with community services and efforts, and effective care transitions.
  
  
• Does the measure contribute to efficient use of resources and/or support alignment of measurement across programs? Yes. The measure is intended to complement the existing CMS Hospital-Wide All-Cause Risk-Standardized Readmission Measure (NQF #1789) to allow assessment of trends in hospital performance for both readmission and mortality outcomes, similar to other complementary pairs of readmission and mortality measures for specific conditions and procedures.
  
  
• Can the measure can be feasibly reported? Yes. The measure uses Medicare administrative claims data and clinical data from the electronic health record.
  
  
• Is the measure reliable and valid for the level of analysis, program, and/or setting(s) for which it is being considered? No. The measure is fully developed but testing has not been completed.
  
  
• Measure development status: Fully Developed
  
  
• If the measure is in current use, do the benefits of the measure outweigh any unreasonable implementation issues that have been identified? N/A. The measure is not currently in use.
  
  
• Is the measure NQF endorsed for the program's setting and level of analysis? Never Submitted
  
  

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
  
  

Preliminary Analysis of Measure

• Preliminary analysis result: Revise and Resubmit
  
  
• Preliminary analysis summary
  • Contribution to program measure set:Currently, this measure has not been tested in enough hospitals to assess measure reliability across hospitals; however, testing results should demonstrate reliability and validity at the facility level in the hospital setting. The developer has indicated that this measure is anticipated for November 2018 submission to NQF for review and endorsement.
  • Impact on quality of care for patients:This measure encourages hospitals to reduce opioid-related adverse respiratory events, a servious reportable event.
  
  
  
• Does the measure address a critical quality objective not adequately addressed by the measures in the program set? Yes. This measure addresses the NQS priority of making care safer by reducing harm caused in the delivery of care. In particular, HACRP aims to incorporate measures that will make care safer, including “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).”
  
  
• Is the measure evidence-based and is either strongly linked to outcomes or an outcome measure? Yes. This is an outcome measure that has a scientific evidence base and that can be influenced by healthcare processes. 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. Opioid-related adverse respiratory events include respiratory depression, respiratory arrest, and cardiopulmonary arrest.
  
  
• Does the measure address a quality challenge? Yes. This measure adresses a servious reportable event. Use of opiates can also lead to serious adverse events, from constipation to death. Of the adverse drug events reported to the Joint Commission’s Sentinel Event database, 47% were due to a wrong medication dose, 29% to improper monitoring, and 11% to other causes (e.g., medication interactions, drug reactions). Additionally, in a closed-claims analysis, 97% of adverse events were judged preventable with better monitoring and response.
  
  
• Does the measure contribute to efficient use of resources and/or support alignment of measurement across programs? Yes. The Hospital Inpatient Quality Reporting program does not currently include any measures specifically related to opioid related adverse respiratory events.
  
  
• Can the measure can be feasibly reported? No. This is a new measure that has never been used in a program. Feasibility testing confirming this is ongoing.The measure is being tested in multiple hospitals with varied EHR systems to confirm feasibility.
  
  
• Is the measure reliable and valid for the level of analysis, program, and/or setting(s) for which it is being considered? No. This measure is not fully developed and testing is not complete. This is a new measure that has not been submitted for NQF endorsement.**The developer has indicated that they anticipate submitting the measure for review in the November 2018 NQF endorsement process.
  
  
• Measure development status: Field Testing
  
  
• If the measure is in current use, do the benefits of the measure outweigh any unreasonable implementation issues that have been identified? No. This is a new measure that has not been previously implemented.
  
  
• Is the measure NQF endorsed for the program's setting and level of analysis? Never Submitted
  
  

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
  
  

Preliminary Analysis of Measure

• Preliminary analysis result: Do Not Support for Rulemaking
  
  
• Preliminary analysis summary
  • Contribution to program measure set:The measure is currently in the Hospital Outpatient Quality Reporting (HOQR) program. The NQF Musculoskeletal Standing Committee agreed that the measure does not meet the validity subcriterion. The measure lost endorsement in 2017.
  • Impact on quality of care for patients:Inappropriate use of imaging is problematic because it subjects patients to unnecessary harms such as radiation exposure and unnecessary treatment, yet it is not associated with improved outcomes. The intent of this measure is to reduce inappropriate imaging for LBP in the absence of “red flags” that can indicate that back pain is caused by a serious, underlying pathology.
  
  
  
• Does the measure address a critical quality objective not adequately addressed by the measures in the program set? Yes. The measure promotes effective prevention and treatment.
  
  
• Is the measure evidence-based and is either strongly linked to outcomes or an outcome measure? Yes. This process measure is based on the 2015 American College of Radiology (ACR) Appropriateness Criteria: Low Back Pain. The ACR 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.
  
  
• Does the measure address a quality challenge? Yes. At the most recent NQF evaluation for endorsement, the developer provided an analysis of Medicare fee-for-service (FFS) claims data that indicated 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).
  
  
• Does the measure contribute to efficient use of resources and/or support alignment of measurement across programs? Yes. The measure is currently in the hospital outpatient program but is undergoing substantial changes. CMS is proposing to add CT lumbar spine studies to the imaging procedures included in the measure’s denominator.
  
  
• Can the measure can be feasibly reported? Yes. The measure is based on Medicare administrative claims.
  
  
• Is the measure reliable and valid for the level of analysis, program, and/or setting(s) for which it is being considered? No. The measure failed maintenance endorsement and is no longer NQF endorsed as of July 2017. The Musculoskeletal Standing Committee (SC) agreed that the measure did not pass the validity sub-criterion and did not recommend the measure for endorsement. One of the SC’s main concerns was that 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.
  
  
• Measure development status: Fully Developed
  
  
• If the measure is in current use, do the benefits of the measure outweigh any unreasonable implementation issues that have been identified? No. CMS states that no issues have been identified.
  
  
• Is the measure NQF endorsed for the program's setting and level of analysis? Failed Endorsement
  
  

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): 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
  
  

Preliminary Analysis of Measure

• Preliminary analysis result: Support for Rulemaking
  
  
• Preliminary analysis summary
  • Contribution to program measure set:This measure is fully developed and tested, and has received endorsement from NQF. It fills a current gap in the PPS-Exempt Cancer Hospital Quality Reporting Program by addressing unplanned readmissions of cancer patients.
  • Impact on quality of care for patients:Preventing cancer patient readmissions will improve the quality of care for cancer patients, improve healthoutcome, and reduce unessisary utlization of healthcare resources.
  
  
  
• Does the measure address a critical quality objective not adequately addressed by the measures in the program set? Yes. This measure addresses the National Quality Strategy Priority of making care safer by reducing harm caused in the delivery of care. The PCHQR Program does not currently include a measure relating to readmissions. It does include on measure for admissions and ED visits (NQF#2936: Admissions and Emergency Department (ED) Visits for Patients Receiving Outpatient Chemotherapy).
  
  
• Is the measure evidence-based and is either strongly linked to outcomes or an outcome measure? Yes. This outcome measure that has a scientific evidence base and a rationale for how the outcome can be influenced by health care processes. The measure provides the rate at which all adult Medicare Fee-for-Service cancer patients have an unplanned readmission within 30 days of discharge from an acute care hospital.
  
  
• Does the measure address a quality challenge? Yes. This measure addresses a topic with a preformance gap. Existing studies in cancer have largely focused on post-operative readmissions, reporting readmission rates between 6.5% and 25%. All-cause and disease-specific unplanned readmissions rates have been adopted by CMS as key indicators of inpatient quality care. This measure addresses cancer measurement gaps in existing readmissions measures.
  
  
• Does the measure contribute to efficient use of resources and/or support alignment of measurement across programs? Yes. This measure is not duplicative of an existing measure in the program. In addition, the measure povides a significant value to patients. 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.
  
  
• Can the measure can be feasibly reported? Yes. This measure is intended for reporting by 11 PCHs using Medicare claims data. These data are accessible by CMS; therefore, no operational concerns are anticipated. In addition, the measure is already used by the Moffit Cancer Center, US News&WorldReport, Vizient, and other cancer centers for the purposes of accountable care programs, reporting, benchmarking and quality impovement.
  
  
• Is the measure reliable and valid for the level of analysis, program, and/or setting(s) for which it is being considered? Yes. This measure was reviewed by NQF’s All-Cause Admissions and Readmissions Project in July 2017. The Standing Committee did not reach consensus on Validity during the initial meeting. The Committee considered public comments as well as additional input from the developer during the post-comment call. Committee members continued to express concerns about the population included in the measure and the lack of granularity in the approach used to risk adjust for comorbidities. However, the Committee ultimately determined that the measure met the Validity criterion.
  
  
• Measure development status: Fully Developed
  
  
• If the measure is in current use, do the benefits of the measure outweigh any unreasonable implementation issues that have been identified? No. This measure is intended for reporting by 11 PCHs using available Medicare claims data. No implementation issues are anticipated. These data are accessible by CMS; therefore, no concerns are anticipated.
  
  
• Is the measure NQF endorsed for the program's setting and level of analysis? Endorsed
  
  

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.
  
  

Program Index

• 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 
  
  

Full Program Summaries

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.

Index of Measures (by Program)

• Hospital Visits following General Surgery Ambulatory Surgical Center Procedures (Public comments received:10; MUC ID: MUC17-233)
  
  

• Medication Reconciliation for Patients Receiving Care at Dialysis Facilities (Public comments received:3; MUC ID: MUC17-176)
  
  
• Percentage of Prevalent Patients Waitlisted (PPPW) (Public comments received:3; MUC ID: MUC17-241)
  
  
• Standardized First Kidney Transplant Waitlist Ratio for Incident Dialysis Patients (SWR) (Public comments received:3; MUC ID: MUC17-245)
  
  

• Hospital-Wide All-Cause Risk Standardized Mortality Measure (Public comments received:10; MUC ID: MUC17-195)
  
  
• Hybrid Hospital-Wide All-Cause Risk Standardized Mortality Measure (Public comments received:9; MUC ID: MUC17-196)
  
  
• Hospital Harm Performance Measure: Opioid Related Adverse Respiratory Events (Public comments received:18; MUC ID: MUC17-210)
  
  

• Lumbar Spine Imaging for Low Back Pain (Public comments received:2; MUC ID: MUC17-223)
  
  

• 30-Day Unplanned Readmissions for Cancer Patients (Public comments received:7; MUC ID: MUC17-178)
  
  

Full Comments (Listed by Measure)

• KCP supports MUC 17-176 (NQF 2988), which was developed by the Kidney Care Quality Alliance (KCQA) and is NQF-endorsed. (Submitted by: Kidney Care Partners)
  
  
• The National Kidney Foundation supports including this measure in the ESRD QIP. Ensuring that the dialysis facility has the most accurate record of all medications including prescription and over the counter medications and herbal supplements is of critical importance to patient safety and outcomes. (Submitted by: National Kidney Foundation)
  
  
• We support this measure as it would likely improve the focus on treatment of the underlying condition. (Submitted by: Ascension)
  
  

• By Electronic Submission Submitted via comment link at www.qualityforum.org/map Measures Application Partnership National Quality Forum 1030 15th Street NW Suite 800 Washington DC 20005 RE: Support for Inclusion of Measure MUC17-178, 30-Day Unplanned Readmissions for Cancer Patients) in PPS-Exempt Cancer Hospital Quality Reporting (PCHQR) Program To the Measures Application Partnership: I am writing to express support for the inclusion of measure MUC17-178 (30-Day Unplanned Readmissions for Cancer Patients) in the PPS-Exempt Cancer Hospital Quality Reporting (PCHQR) Program, as proposed in the 2017-18 Measures Under Consideration. 30-Day Unplanned Readmissions for Cancer Patients is designed to enable oncologists in both hospital and physician-practice settings to gather meaningful data to target areas for quality improvement in the care and treatment of cancer patients. As you know, readmissions measures that currently exist in federal reporting programs may inadvertently capture planned readmissions for cancer patients. This is problematic for two reasons. First, from a quality improvement standpoint, gathering such data does not present a complete picture to help physicians identify and understand circumstances when patients should have been kept out of the hospital—which is the primary goal of reporting on readmissions measures. Second, as a result, current measures may also unfairly penalize oncologists for whom reporting is tied to payment. Applying a cancer-specific readmissions measure to oncology providers would resolve these issues. 30-Day Unplanned Readmissions for Cancer Patients has been proven to work for cancer patients. In fact, as you know, U.S. News & World Report is already applying the measure in its hospital rankings system and has demonstrated the value of this measure. Many renowned cancer centers are also already utilizing this measure—for quality improvement, benchmarking, and value-based contracts. We believe that inclusion of this measure in the PCHQR Program will help advance the quality of care received by cancer patients and will serve as an excellent measure for the Program. Thank you for your consideration of our comments. (Submitted by: H Lee Moffitt Cancer Center and Research Institute)
  
  
• The Alliance of Dedicated Cancer Centers (ADCC) is writing to express support for the inclusion of measure MUC17-178 (30-Day Unplanned Readmissions for Cancer Patients) in the PPS-Exempt Cancer Hospital Quality Reporting (PCHQR) Program, as proposed in the 2017-18 Measures Under Consideration. The ADCC is comprised of 11 cancer hospitals, all of whom participate in the PPS-Exempt Cancer Hospital Quality Reporting (PCHQR) Program. Unlike other hospitals that care for patients suffering from any condition, the Dedicated Cancer Centers treat cancer patients exclusively. Much of the progress in understanding cancer’s biology and successful treatment methods is directly attributable to the work of ADCC members. Our institutions are at the forefront of innovative treatment options in precision medicine, immunotherapies, and other state of the art diagnostic and patient care technologies. The Dedicated Cancer Centers are committed to delivering the highest standard of cancer care and share the Centers for Medicare & Medicaid Services’ (CMS) focus on cancer care delivery that is safe, effective, high-quality, and patient-centered. We are committed to achieving the best outcomes for our patients through novel therapies and excellent care delivery. Our members serve as regional, national, and international resources in developing the most effective and efficient ways to treat cancer patients. The 30-Day Unplanned Readmissions for Cancer Patients is designed to enable oncologists in both hospital and physician-practice settings to gather meaningful data to target areas for quality improvement in the care and treatment of cancer patients by excluding patients with planned readmissions as well as patients admitted for treatment of progression of disease. Readmissions measures currently included in federal reporting programs are not designed to take such circumstances into account. Inclusion of planned visits or visits for progression of disease hampers quality improvement efforts by limiting the ability of providers to identify and address truly preventable readmissions. In addition, oncologists for whom reporting is tied to payment may be penalized unfairly. Applying a cancer-specific readmissions measure to oncology providers would address these issues. The 30-Day Unplanned Readmissions for Cancer Patients measure has proven to be useful in assessing quality of cancer care. The U.S. News & World Report is already applying the measure in its hospital rankings system and has demonstrated the value of this measure. Several renowned cancer centers are also using this measure—for quality improvement, benchmarking, and value-based contracts. We believe that inclusion of this measure in the PCHQR Program will help advance the quality of care received by cancer patients and will serve as an excellent measure for the Program. As this is a claims-based measure, the data and reporting burden to participants should be insignificant. Thank you for your consideration of our comments. (Submitted by: Alliance of Dedicated Cancer Centers)
  
  
• Including 30-day unplanned readmission reporting for cancer patients will strengthen provider attention on careful and proactive care and discharge planning, as it does for other non-cancer conditions, thus potentially enhancing quality of care and care experience for this population. (Submitted by: Coalition to Transform Advanced Care (C-TAC))
  
  
• The measure should be submitted for endorsement. Endorsement review should consider if the measure should be more aligned with other readmission measures used in hospital programs, the need to risk adjust the measure for socio-demographic factors and if additional exclusions are needed (e.g. hospice). (Submitted by: Premier, Inc.)
  
  
• We are concerned that this measure could create unintended consequences such as less aggressive treatment due to fear of readmission. We would recommend the development of more specific definitions regarding what qualifies as "unplanned", especially given that this might change by treatment type. We recommend that CMS consider testing this measure before implementation. (Submitted by: Ascension)
  
  
• This measure adds value to the PCHQR program in that it fills a gap in the need for cancer-specific measures. The cancer specific readmission measure aligns with similarly reported readmission measures across various federally reported programs. However, this measure more accurately reflects the quality of cancer care delivered when compared to the broader readmission measures and better enables hospitals to gather more meaningful data to target areas for improvement. This measure improves outcomes by identifying potential unplanned readmissions which are costly and impact patient quality of life. As a claims-based measure, one limitation in the measure is that we cannot identify our patients who are readmitted to an outside center. Overall, we recommend this measure as a measure under consideration for PCHQR under certain conditions and recommend this measure be limited to solid tumor cancers in order to exclude disease progression for hematologic diseases. (Submitted by: The Ohio State University Comprehensive Cancer Center – Arthur G. James Cancer Hospital and Richard J. Solove Research Institute)
  
  
• GNYHA supports including 30-Day Unplanned Readmissions for Cancer Patients (MUC17-178) in PCHQR. We believe it’s a major step forward because it is a cancer-specific readmissions measure. The measure includes exclusions based on planned readmissions for radiation therapy or surgery, which is an important consideration in measuring readmissions for this patient population. It also addresses excluding patients who are readmitted for progression of disease as care is warranted under those circumstances. Because of these exclusions, the measure is better able to identify truly preventable readmissions compared to other existing measures. (Submitted by: Greater New York Hospital Association)
  
  

• Despite some pushback from various sources, this may be a useful measure if appropriately applied. The concern relates to prematurely discharging patients who may be prematurely discharged by those who wish to avoid the negative implications of being an outlier on this measure. This could be especially apparent for those who care for indigent or low-income populations, for more remote rural facilities, and for those who care for the sickest and most vulnerable populations (safety net hospitals, urban academic centers, rural remote facilities, etc.). Supporting evidence that such hospitals will not be inappropriately called out would be appropriate before applying this measure broadly (Submitted by: Coalition to Transform Advanced Care (C-TAC))
  
  
• The American Medical Association (AMA) questions the need for an all-cause risk-standardized mortality measure given the number of condition-specific mortality measures available and in use now. In addition, we do not believe that reporting on the 30-day mortality rate for each hospital is not truly reflective of the quality of care provided to their patients, particularly given the lack of adequate risk adjustment for social risk factors. Developers must continue to be responsive to emerging measure methodologies and the traditional approach of risk adjusting at the patient level may not be appropriate for those measures where the measurement period includes care that is outside of the control of the hospital. The AMA believes that there may be community-level variables that could impact the risk model of this measure that are not addressed. Measures that extend beyond the hospital stay or outside the locus of control of the measured entity should continue to have adjustment for social risk factors addressed and new variables analyzed and at different levels (e.g., patient, hospital, and community). In addition, the developer should continue to explore new variables that are directly related to the community in which a patient resides, particularly given the recent report from the Office of the Assistant Secretary for Planning and Evaluation (ASPE). Until these questions and potential risk factors are examined further, we do not believe that this measure is ready for implementation in HIQR. (Submitted by: American Medical Association)
  
  
• The American Medical Association (AMA) questions the need for an all-cause risk-standardized mortality measure given the number of condition-specific mortality measures available and in use now. In addition, we do not believe that reporting on the 30-day mortality rate for each hospital is not truly reflective of the quality of care provided to their patients, particularly given the lack of adequate risk adjustment for social risk factors. Developers must continue to be responsive to emerging measure methodologies and the traditional approach of risk adjusting at the patient level may not be appropriate for those measures where the measurement period includes care that is outside of the control of the hospital. The AMA believes that there may be community-level variables that could impact the risk model of this measure that are not addressed. Measures that extend beyond the hospital stay or outside the locus of control of the measured entity should continue to have adjustment for social risk factors addressed and new variables analyzed and at different levels (e.g., patient, hospital, and community). In addition, the developer should continue to explore new variables that are directly related to the community in which a patient resides, particularly given the recent report from the Office of the Assistant Secretary for Planning and Evaluation (ASPE). Until these questions and potential risk factors are examined further, we do not believe that this measure is ready for implementation in HIQR. (Submitted by: American Medical Association)
  
  
• Condition-specific mortality measures, which are more actionable and amenable to intervention and improvement, are already included in the program. (Submitted by: Premier, Inc.)
  
  
• We would not support the use of this measure on that basis that it assumes death is always and necessarily a bad outcome rather than an inevitability of the human condition. We are concerned that this assumption and the corresponding measure could ultimately lead to increased use of medically inappropriate services at the end of life, including some that may be excessively burdensome as well as some that may be medically non-indicated. (Submitted by: Ascension)
  
  
• MUC17-195 Hospital-Wide All-Cause Risk Standardized Mortality Measure The National Coalition for Hospice and Palliative Care (www.nationalcoalitionhpc.org) NCHPC does not recommend the use of measure MUC17-195 for inclusion in the HIQR program. Although we acknowledge that the measure denominator excludes hospice and metastatic cancer, Schwarze et al. (2014) noted that the use of 30-day mortality measures can inhibit the use of palliative services and fail to accommodate the wishes of patients who would prefer death over prolonged life-sustaining treatment. As proposed, this measure does not provide for consideration of a patient’s goals of care – including the decision to withdraw life-sustaining treatment. Inclusion in the HIQR program could create perverse incentives to keep patients alive despite their wishes and potentially inhibit palliative care referral, as some providers may fear the consequences of patients choosing to pursue comfort-related goals. Therefore, due to these unintended consequenes, we urge the MAP to not recommend this measure for the HIQR program. (Submitted by: National Coalition for Hospice and Palliative Care)
  
  
• - The AAMC has concerns with the use of this hospital-wide-all cause risk standardized mortality measure in the program. We believe that condition-specific mortality measures more accurately capture a hospital’s overall quality in regards to mortality and enable providers to implement targeted strategies to improve performance. In addition, The AAMC has concerns with the risk adjustment for this measure. It is critical for this measure to be appropriately risk adjusted to account for patient complexity and for sociodemographic factors. Hospitals that disproportionately care for vulnerable patient populations are disadvantaged when SDS factors are not considered in the risk adjustment or scoring methodology. This measure is solely claim based so may also present issues for risk adjustment as it will not include broader EHR clinical data in regard to patient’s health status. - The AAMC is also unclear on how the measure includes/excludes hospice enrollment for index admissions. We are concerned that the timing of hospice decisions within a patient’s family might result in a delay in hospice enrollment such that a hospice-enrolled patient’s admission might be included inappropriately when measuring the hospital’s mortality rate. - The AAMC believes that this measure should be NQF endorsed before being proposed for the IQR program. (Submitted by: Association of American Medical Colleges)
  
  
• While the Federation of American Hospitals (FAH) does not disagree with the importance of assessing the morality rates of those patients who had a hospital admission, the rationale for this measure does not provide sufficient evidence that a death in the 30 days following an inpatient admission is a predictor of the quality of care provided by a hospital and may well be due to other factors outside of the control of a hospital. During the FAH’s review of the preliminary measure specifications during the public comment period, we noted that the articles and research cited to demonstrate the importance and underlying evidence to support the measure were solely focused on inpatient mortality. The FAH does not believe that these studies provide adequate justification for developing and proposing this measure at this time. Prior to implementation of this measure in the HIQR program, linking a hospital’s quality and a patient’s chance of survival within 30 days of a hospitalization must be demonstrated and the risk adjustment approach must adequately address the social risk factors within a given community that may influence a 30-day mortality rate. To date, hospitals and others have not been provided information on the testing results of this measure, and as such the FAH is unable to fully assess this measure’s appropriateness for use in federal programs. This measure should submitted to and reviewed by NQF for endorsement prior to support from the MAP and subsequent implementation in the HIQR. (Submitted by: Federation of American Hospital (FAH))
  
  
• - The Society of Hospital Medicine (SHM) has concerns about the duplication of this measure with the hybrid hospital-wide all-cause risk standardized mortality measure (MUC17-196). We strongly discourage duplicate measures in federal programs as this creates confusion and potential opportunities for performance assessment on identical patient pools. We urge clarification on the differences between these measures, and encourage CMS to adopt a single measure in this area. (Submitted by: Society of Hospital Medicine)
  
  
• GYNHA opposes the Hospital-Wide Risk Standardized Mortality (MUC16-195) in HIQR. This measure has not been submitted for endorsement to NQF and there is no clear timeline for endorsement. Measures that have not been evaluated and endorsed by NQF for feasibility, validity and applicability to quality improvement should not be included in CMS programs. In addition to not being fully vetted, the measure is not appropriate for measuring hospital quality in a meaningful way. Hospital-wide mortality does not identify preventable and avoidable deaths that could be a result of hospital care and thus is not a measure of hospital quality. The proportion of preventable deaths vary by condition and this issue is compounded in a broad measure like hospital-wide mortality. Firstly, a hospital cannot identify clinical mechanisms to improve outcomes when provided with hospital-wide mortality rates. Secondly, any efforts to improve the outcome would be masked by deaths which could not be avoided through clinical care. While this is also true in condition specific mortality measures, the issue is magnified with a hospital-wide measure as the total proportion of unavoidable deaths is unknown across all conditions for a given hospital’s population. HIQR already includes a series of condition specific mortality measures endorsed by NQF which are more suitable for quality improvement. These condition specific measures can be regarded as a suite of measures which encompass the most significant clinical conditions for mortality in place of a broader hospital-wide mortality measure. (Submitted by: Greater New York Hospital Association)
  
  

• The hybrid approach adds burden for providers but likely adds valuable supplemental information that helps adjust for confounding factors, such as multiple chronic conditions or similar that could influence overall hospital mortality. Thus it would be preferred to MUC17-195, at least under some circumstances. (Submitted by: Coalition to Transform Advanced Care (C-TAC))
  
  
• The American Medical Association (AMA) questions the need for an all-cause risk-standardized mortality measure given the number of condition-specific mortality measures available and in use now. In addition, we do not believe that reporting on the 30-day mortality rate for each hospital is not truly reflective of the quality of care provided to their patients, particularly given the lack of adequate risk adjustment for social risk factors. Developers must continue to be responsive to emerging measure methodologies and the traditional approach of risk adjusting at the patient level may not be appropriate for those measures where the measurement period includes care that is outside of the control of the hospital. The AMA believes that there may be community-level variables that could impact the risk model of this measure that are not addressed. Measures that extend beyond the hospital stay or outside the locus of control of the measured entity should continue to have adjustment for social risk factors addressed and new variables analyzed and at different levels (e.g., patient, hospital, and community). In addition, the developer should continue to explore new variables that are directly related to the community in which a patient resides, particularly given the recent report from the Office of the Assistant Secretary for Planning and Evaluation (ASPE). Until these questions and potential risk factors are examined further, we do not believe that this measure is ready for implementation in HIQR. (Submitted by: American Medical Association)
  
  
• Condition-specific mortality measures, which are more actionable and amenable to intervention and improvement, are already included in the program. While we would support development of a hybrid measure, the hybrid approach should be tested on the existing condition-specific mortality measures. Condition-specific hybrid mortality measures should be considered an eCQM and available as one of the options for meeting the 4 eCQM measure requirements. (Submitted by: Premier, Inc.)
  
  
• We would not support the use of this measure on that basis that it assumes death is always and necessarily a bad outcome rather than an inevitability of the human condition. We are concerned that this assumption and the corresponding measure could ultimately lead to increased use of medically inappropriate services at the end of life, including some that may be excessively burdensome as well as some that may be medically non-indicated. (Submitted by: Ascension)
  
  
• MUC17-196 Hybrid Hospital-Wide All-Cause Risk Standardized Mortality Measure The National Coalition for Hospice and Palliative Care (NCHPC/www.nationalcoalitionhpc.org) does not recommend the use of measure MUC17-196 for inclusion in the HIQR and EHR Incentive/EH/CAH programs. Although we acknowledge that the measure denominator excludes hospice and metastatic cancer, Schwarze et al. (2014) noted that the use of 30-day mortality measures can inhibit the use of palliative services and fail to accommodate the wishes of patients who would prefer death over prolonged life-sustaining treatment. As proposed, this measure does not provide for consideration of a patient’s goals of care – including the decision to withdraw life-sustaining treatment. Inclusion in the HIQR and EHR Incentive/EH/CAH programs could create perverse incentives to keep patients alive despite their wishes and potentially inhibit palliative care referral, as some providers may fear the consequences of patients choosing to pursue comfort-related goals. Therefore, due to these potential unintended consequences, we urge the MAP to not recommend this measure for these programs. (Submitted by: National Coalition for Hospice and Palliative Care)
  
  
• - The AAMC has concerns with the use of this hospital-wide-all cause risk standardized mortality measure in the program. We believe that condition-specific mortality measures more accurately capture a hospital’s overall quality in regards to mortality and enable providers to implement targeted strategies to improve performance. In addition, the AAMC has concerns with the risk adjustment for this measure. It is critical for this measure to be appropriately risk adjusted to account for patient complexity and for sociodemographic factors. 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 believes that integrating EHR data with claims data is a positive step. That being said, we recommend that the Agency focus its efforts on the use of EHR data to adjust condition specific mortality measures that are currently beings used in the programs. - - The AAMC is also unclear on how the measure includes/excludes hospice enrollment for index admissions. We are concerned that the timing of hospice decisions within a patient’s family might result in a delay in hospice enrollment such that a hospice-enrolled patient’s admission might be included inappropriately when measuring the hospital’s mortality rate. - - The AAMC believes that this measure should be NQF endorsed before being proposed for the IQR program and the EHR Incentive program. - (Submitted by: Association of American Medical Colleges)
  
  
• While the FAH does not disagree with the importance of assessing the morality rates of those patients who had a hospital admission, the rationale for this measure does not provide sufficient evidence that a death in the 30 days following an inpatient admission is a predictor of the quality of care provided by a hospital and may well be due to other factors outside of the control of a hospital. During the FAH’s review of the preliminary measure specifications for the claims-based measure during the public comment period, we noted that the articles and research cited to demonstrate the importance and underlying evidence to support the measure were solely focused on inpatient mortality. The FAH does not believe that these studies provide adequate justification for developing and proposing this measure at this time. Prior to implementation of this measure in the HIQR program, linking a hospital’s quality and a patient’s chance of survival within 30 days of a hospitalization must be demonstrated and the risk adjustment approach must adequately address the social risk factors within a given community that may influence a 30-day mortality rate. To date, hospitals and others have not been provided information on the testing results of this measure, and as such the FAH is unable to fully assess this measure’s appropriateness for use in federal programs. In addition, CMS just recently scheduled a webcast to discuss the requirements for hospitals to provide the clinical electronic health record data and at this point in time, the true requirements for reporting are still unknown. Some individuals estimate that fewer than 100 hospitals will voluntarily submit data to CMS, which is not a sufficient number to provide a true representative sample of all of the hospitals in the nation. While the FAH is encouraged to see measures put forward by CMS using electronic clinical data to further improve the validity of the measure, this measure should submitted to and reviewed by the NQF for endorsement prior to support from the MAP and subsequent implementation in the HIQR or Medicare and Medicaid EHR Incentive Program for Eligible Hospitals and Critical Access Hospitals (CAHs). (Submitted by: Federation of American Hospitals (FAH))
  
  
• - The Society of Hospital Medicine (SHM) has concerns about the duplication of this measure with the hospital-wide all-cause risk standardized mortality measure (MUC17-195). We strongly discourage duplicate measures in federal programs as this creates confusion and potential opportunities for performance assessment on identical patient pools. We urge clarification on the differences between these measures, and encourage CMS to adopt a single measure in this area. If it is the case that this measure is meant to be tested while MUC17-195 is implemented, we ask this be explicitly articulated. (Submitted by: Society of Hospital Medicine)
  
  
• In general, GNYHA supports the development of hybrid measures because adding EHR data to claims data could improve the explanatory power of risk-adjustment models and, thus, the accuracy and fairness of conclusions made about hospital performance. However, we oppose the use of EHR data for the public reporting of any hybrid measure, due to ongoing concerns about the accuracy and efficiency of capturing, calculating, and reporting electronic clinical quality measures (eCQMs), as well as concerns about the ability of EHR technology to support current measure reporting requirements. (Submitted by: Greater New York Hospital Association)
  
  

• Comment on MUC17-210: Why the exclusion for "within 2 hours of a procedure?" Use of reversal agents (naloxone or flumazenil) in the PACU is already an internal quality metric for many anesthesia departments and hospitals. I would vote to keep peri-procedural use of naloxone in the measure. (Submitted by: US Anesthesia Partners)
  
  
• As a respiratory therapist, I think this is an excellent step in raising awareness on over-sedation from opioids and other respiratory depressants (e.g., benzodiazepines). My only comment is that the qualifier "unless administered during or within 2 hours following a procedure" will likely underestimate the problem. I recognize that some physicians give reversal agents after a procedure as a part of their protocol but unplanned reversals do occur during this time period for procedural sedation as well. I would suggest rewording as "Number of admissions with documentation of any of the following criteria for defining ORARE: administration of narcotic antagonist (i.e., IV naloxone), unless a planned administration (i.e., 'standing order') immediately following procedural sedation, OR respiratory stimulant (i.e., doxapram) all within 24 hours of opioid administration, over a 12-month period." (Submitted by: Medtronic)
  
  
• It might be appropriate to expand this measure to include other widely used opioids in the hospital setting. (Submitted by: Respiratory Motion)
  
  
• The Anesthesia Patient Safety Foundation (APSF), a non-profit organization dedicated to improving perioperative patient safety, strongly supports proposed CMS Hospital Harm Performance Measure: Opioid-Related Adverse Respiratory Events (ORARE), MUC 17-210. The APSF has long expressed concern about the potentially catastrophic perioperative outcomes related to ORARE. The APSF sponsored a consensus conference on this topic that included participation of representatives of a number of pharmaceutical and patient monitoring industry leaders as well as the following organizations: • American College of Surgeons • American Society of Anesthesiologists • American Association of Nurse Anesthestists • American Association of Anesthesiologists Assistants • The Joint Commission • Association of Operating Room Nurses • American Society of Perianesthetic Nurses Examples of APSF’s involvement with issue involve multiple articles published in the APSF Newsletter. This newsletter is distributed to 122,000 U.S. and Canadian anesthesia professionals and is also read by more than 50,000 anesthesia professionals outside of North America. • https://www.apsf.org/newsletters/html/2012/winter/05_capMonitor.htm; https://www.apsf.org/newsletters/html/2012/spring/01_postop.htm) • https://www.apsf.org/newsletters/html/2011/fall/01_opioid.htm The APSF also has produced a highly-viewed ORARE-related video (https://www.apsf.org/resources/oivi/). The APSF believes that approval and implementation of the proposed MUC 17- 210 will focus important attention on this vital patient safety issue and lead to a reduction in the frequency of this devastating set of events. (Submitted by: Anesthesia Patient Safety Foundation)
  
  
• Anesthesiologists have long recognized that postoperative respiratory depression is a serious patient safety issue. Current monitors are monitors for detecting hypoxemia and the exhaled carbon dioxide (eTCO2) neither of which directly measures metrics of respiratory depression. It is important that hospitals continue to evaluate better monitors to detect respiratory depression. I am a member of the ASA Committee on Performance and Outcomes Measures. This committee recognizes the serious patient safety issues which continue to this day even with changes in managing patients at risk for opioid induced respiratory depression. It is critically important for all hospitals, physicians to search for best ways to monitor and prevent opioid induced respiratory depression. (Submitted by: Member of American Society of Anestheisologists)
  
  
• The AAMI Foundation, a non-profit charitable organization whose mission is to drive reductions in preventable patient harm and improvements in outcomes associated with the use of health technology, strongly supports the proposed CMS Hospital Harm Performance Measure: Opioid-Related Adverse Respiratory Events (ORARE), MUC 17-210. Anoxic brain injury and death from unrecognized ORARE are often preventable if the patient’s deterioration is detected and addressed in time. This measure will provide quantitative evidence that ORARE is a major patient safety threat as well as track reduction in preventable harm from ORARE through implementation of opioid sparing pain strategies and continuous monitoring of patients. The time and cost to track this measure is offset by lives that will be saved from the preventable tragedy of ORARE. To support of the latter, AAMI Foundation initiated the National Coalition to Promote the Continuous Monitoring of Patients on Opioids in 2014. Over the past four years, AAMI Foundation has produced numerous webinars and papers highlighting the hospitals that have successfully implemented continuous electronic monitoring for all their patients on parenteral opioids. These hospitals, both large and small, have addressed the barriers to implementing continuous monitoring (fear of increased alarm fatigue, problems with changing the nursing workflow to accommodate a new process, and cost of the technology) and their nursing staff would now refuse to be without the continuous monitoring. Here is the link to our webpage where all these stories can be heard (all foundation materials are free): http://www.aami.org/PatientSafety/content.aspx?ItemNumber=2933&navItemNumber=3086 The AAMI Foundation believes that approval and implementation of the proposed MUC 17-210 will focus attention on this vital patient safety issue and provide a valuable quality outcome metric towards reducing the incidence of light threatening ORARE’s. We would like to suggest a few modifications to the formula: (1) Naloxone use is a sensitive yet nonspecific indicator for ORARE. To increase the specificity of the measure for unanticipated and preventable ORARE’s, the numerator should track naloxone use outside of clinical areas where naloxone use is indicated and anticipated, such as emergency rooms and post anesthesia recovery rooms. The two hour post procedure timing of naloxone administration in the numerator is arbitrary and easily manipulated. The intent of this threshold is to exclude ‘appropriate’ opioid reversal post procedure. This goal is better served by restricting the metric’s location of naloxone administration to areas outside of procedure recovery areas, ICU’s, or emergency rooms. The goal of this metric is to reduce unanticipated opioid reversal on a hospital ward or unmonitored setting to an infrequent event. Thank you for the opportunity to comment on this important proposed measure. Sincerely, Marilyn Flack Executive Director, AAMI Foundation Frank J. Overdyk, MSEE, MD Chair, AAMI Foundation National Coalition to Promote Continuous Monitoring of Patients on Opioids (Submitted by: AAMI Foundation)
  
  
• The AAMI Foundation, a non-profit charitable organization whose mission is to drive reductions in preventable patient harm and improvements in outcomes associated with the use of health technology, strongly supports the proposed CMS Hospital Harm Performance Measure: Opioid-Related Adverse Respiratory Events (ORARE), MUC 17-210. Anoxic brain injury and death from unrecognized ORARE are often preventable if the patient’s deterioration is detected and addressed in time. This measure will provide quantitative evidence that ORARE is a major patient safety threat as well as track reduction in preventable harm from ORARE through implementation of opioid sparing pain strategies and continuous monitoring of patients. The time and cost to track this measure is offset by lives that will be saved from the preventable tragedy of ORARE. To support of the latter, AAMI Foundation initiated the National Coalition to Promote the Continuous Monitoring of Patients on Opioids in 2014. Over the past four years, AAMI Foundation has produced numerous webinars and papers highlighting the hospitals that have successfully implemented continuous electronic monitoring for all their patients on parenteral opioids. These hospitals, both large and small, have addressed the barriers to implementing continuous monitoring (fear of increased alarm fatigue, problems with changing the nursing workflow to accommodate a new process, and cost of the technology) and their nursing staff would now refuse to be without the continuous monitoring. Here is the link to our webpage where all these stories can be heard (all foundation materials are free): http://www.aami.org/PatientSafety/content.aspx?ItemNumber=2933&navItemNumber=3086 The AAMI Foundation believes that approval and implementation of the proposed MUC 17-210 will focus attention on this vital patient safety issue and provide a valuable quality outcome metric towards reducing the incidence of light threatening ORARE’s. We would like to suggest a few modifications to the formula: (1) Naloxone use is a sensitive yet nonspecific indicator for ORARE. To increase the specificity of the measure for unanticipated and preventable ORARE’s, the numerator should track naloxone use outside of clinical areas where naloxone use is indicated and anticipated, such as emergency rooms and post anesthesia recovery rooms. The two hour post procedure timing of naloxone administration in the numerator is arbitrary and easily manipulated. The intent of this threshold is to exclude ‘appropriate’ opioid reversal post procedure. This goal is better served by restricting the metric’s location of naloxone administration to areas outside of procedure recovery areas, ICU’s, or emergency rooms. The goal of this metric is to reduce unanticipated opioid reversal on a hospital ward or unmonitored setting to an infrequent event. Thank you for the opportunity to comment on this important proposed measure. Sincerely, Marilyn Flack Executive Director, AAMI Foundation Frank J. Overdyk, MSEE, MD Chair, AAMI Foundation National Coalition to Promote Continuous Monitoring of Patients on Opioids (Submitted by: AAMI Foundation)
  
  
• Leapfrog strongly supports the addition of the Hospital Harm Performance Measure: Opioid Related Adverse Respiratory Events. Adverse drug events remain the most common type of medical error that occurs during a hospitalization. The documented administration of naloxone is an IHI endorsed 'trigger.' (Submitted by: The Leapfrog Group)
  
  
• The American Medical Association (AMA) supports the development of measures that are targeted at reducing and eliminating adverse events that lead to patient harm. While the intent of the proposed measure appears to be appropriate, no specifics such as detailed specifications or testing results have been released for review and comment. As a result, questions including whether there is true variation in care across hospitals and if the measure yields information and data that is useful for quality improvement are not yet answered. This information should be made available prior to the MAP supporting this measure for the HIQR or Medicare and Medicaid EHR Incentive Program for Eligible Hospitals and Critical Access Hospitals (CAHs). (Submitted by: American Medical Association)
  
  
• To begin, we think it is good that you are considering adding this measure to determine potential problems with opioid use in organizations by measuring naloxone use. But aspects of the wording within the proposed is of concern. First, we disagree with the statement "unless administered during or within 2 hours following a procedure" for a couple of reasons. First, and unfortunately, we've seen organizations who perform procedures where practitioners INTENTIONALLY give naloxone to reverse the effects of an opioid simply to finish one procedure to quickly move to another procedure (thus reversing the legitimate pain control effects of the opioid). This type of naloxone use is inappropriate and, in fact, this measure could be used to measure this type of use. Second, there are times during procedures where practitioners intentionally or unintentionally give too much opioids (e.g. fentanyl) that leads to preventable respiratory depression and naloxone would need to be given and this should absolutely be measured. Lastly, the inclusion of that statement along with “or respiratory stimulant” would be very difficult for most organizations to gather, even with robust E.H.R. capabilities. In addition, organizations who may be smaller in bed size may have to manually do chart reviews to find out a) who got an opioid, b) who got naloxone, c) who got naloxone “during or within 2 hours” and d) got a respiratory stimulant. The labor involved to accomplish this task would be overwhelming for most organizations. It may be best to start this measure simply or with fewer exclusions and when organizations IT capabilities improve, then include exclusions to the measure. (Submitted by: Institute for Safe Medication Practices)
  
  
• Premier agrees that measures assessing opiod use should be included in the program; however, other measures should be considered over this measure. CMS has put forth a concurrent prescribing of opioids at discharge measure for review by the NQF Patient Safety committee. Conceptually, concurrent prescribing may be better suited for use in IQR. The measure under consideration is simply a utilization measure without clear signal of quality. Use alone does not equate to harm. If the meaure is used for national comparisons it shoud be standardized (e.g. patient days). (Submitted by: Premier, Inc.)
  
  
• We support the use of this measure. (Submitted by: Ascension)
  
  
• MDT position: • Medtronic’s Minimally Invasive Therapies Group strongly supports the proposal to create a quality measure for opioid-related adverse respiratory events (ORAREs) in hospitalized patients. We support this measure because identifying the rate at which naloxone is given in the hospital setting will provide further information on the frequency of ORARE and promote strategies to improve patient safety. These are necessary steps to reduce ORARE-associated morbidity and mortality for Medicare beneficiaries. Clinical and economic burden: • Recent evidence suggests ORAREs are increasing and often under-recognized patient safety consequences of pain management that have substantial clinical and economic impact. • An analysis of the National Inpatient Sample demonstrated that the frequency of postoperative opioid overdose doubled to 1 patient per 1000 from 2002-2011 with a 1.7% mortality rate among these patients (Cauley, Ann Surg 2017). • Untoward outcomes have also been observed in patients hospitalized with primary diagnoses related to opioid use. The mortality in these patients has now risen to over 2% with admissions for opioid and heroin poisoning specifically more likely to be Medicare recipients with disabilities (Song, Health Affairs, 2017). • In addition to mortality, patients who received naloxone reversal following abdominal surgery also have an increased use of ventilator support, length of stay, and overall resource utilization (Bloom, ESICM, 2010). • The American Society of Anesthesiologists Closed Claims Analyses identified 92 cases in which Postoperative Opioid-induced Respiratory Depression occurred and suggested nearly all of these events were preventable with better patient monitoring and clinical response (Lee, Anesthesia & Analgesia, 2015). Suggestions on how to improve this QM: • The qualifier "unless administered during or within 2 hours following a procedure" may lead to underestimating of ORAREs. Recognizing that some physicians give reversal agents after a procedure as a part of their normal protocol, unplanned reversals do occur during this time period for procedural sedation as well. Potential rewording as "Number of admissions with documentation of any of the following criteria for defining ORARE: administration of narcotic antagonist (i.e., IV naloxone), unless a planned administration (i.e., 'standing order') immediately following procedural sedation, OR respiratory stimulant (i.e., doxapram) all within 24 hours of opioid administration, over a 12-month period. • Consistent with the CMS Opioid Misuse strategy calling for increased utilization of evidence-based approaches to acute and chronic pain management, we suggest that continuous respiratory patient monitoring be considered as an additional process measure to improve patient outcomes. (https://www.cms.gov/Outreach-and-Education/Outreach/Partnerships/Downloads/CMS-Opioid-Misuse-Strategy-2016.pdf) Limitations of this QM: • Implementation of continuous electronic monitoring may increase recognition of early precursors to ORARE (e.g., oxygen desaturation, hypercapnia, low respiratory rate, etc.) which were previously unrecognized prior to continuous monitoring. Initially, this may lead to increased use of reversal agents, representing an actual improvement in patient safety, which could be misinterpreted as a negative indicator. • As an increased trend in reversal agent use may be viewed as a negative indicator, this may have the potential to discourage its appropriate use in situations in which it is warranted. (Submitted by: Medtronic - Minimally Invasive Therapies Group)
  
  
• The National Association for the Medical Direction of Respiratory Care (NAMDRC) appreciates the opportunity to comment on CMS’ proposed Quality Measure concerning opiod – related adverse respiratory events (ORARE). While we agree that the motivation to address ORARE is laudable, there are several concerns we have with the existing description provided. First, we do not believe that coding of the timing of IV Naloxone during a hospitalization for an opioid overdose as opposed to use in recovery from a procedure is very precise. The 2 hour cut-off may also be subject to error and seems arbitrary. For instance, a patient with chronic narcotic use goes for a procedure in which additional opiate medication is given. At the end of the procedure, Naloxone is given for a respiratory arrest. Since the Naloxone administration took place in the context of the procedure, it is not counted as a “rescue” of an opiate patient. But the reality is that the addition of additional opiate during the procedure (e.g., fentanyl for sedation) contributed to the patients respiratory arrest. If the patient had not been on chronic opiates, nothing would have happened to the patient with opiate procedural sedation. This case is, then, a “false negative” for this metric. Another example of this metric not working as intended is when Naloxone may be administered in the hospital setting during a rapid response team encounter when there is an unknown cause of respiratory arrest but the cause turns out to be something other than opioid use. In this situation, Naloxone is used unnecessarily. We also take issue with the use of Doxapram, a respiratory stimulant rarely used in clinic practice, as being part of the numerator for the ORARE calculation. It works differently from Naloxone rescue of opiate induced respiratory adverse events and we believe including it is misleading and unnecessary. Thank you for the opportunity to respond and for your consideration of our remarks. (Submitted by: Nat'l Ass'n for Medical Direction of Respiratory Care )
  
  
• The FAH supports the intent of this measure as it focuses on promoting reductions in patient harm, but there is insufficient information to assess the measure’s appropriateness for use in the HIQR or Medicare and Medicaid EHR Incentive Program for Eligible Hospitals and Critical Access Hospitals (CAHs). This measure should be tested for reliability and validity and reviewed by the NQF for endorsement prior to support from the MAP and subsequent implementation in either program. (Submitted by: Federation of American Hospitals (FAH))
  
  
• - The Society of Hospital Medicine (SHM) is broadly supportive of this measure as it represents an area of clinical and social importance. However, we note that there was confusion with the specifications of the measure and whether patients who received opioids outside the hospital (e.g. overdose in the community) could potentially or accidentally be counted in this measure. We strongly encourage more clear measure specifications to ensure that the measure focuses exclusively on the number of admissions with administration of narcotic antagonist or respiratory stimulant after hospital-administered opioid treatments. (Submitted by: Society of Hospital Medicine)
  
  
• The American Society of Anesthesiologists (ASA) supports the continued development of this measure and would welcome the opportunity to work with CMS and other stakeholders on this measure, especially as its relevancy increases in future years as physician anesthesiologists reporting to the Merit-based Incentive Payment System (MIPS) will have the opportunity of using facility-based metrics as a proxy score for the Quality component. While we support this metric, there is concern that it could perversely incentivize facilities and potentially discourage the administration of naloxone or doxapram when clinically necessary. Additionally, we request the MAP clarifies with the measure developer that appropriate clinical scenarios for using these drugs, other than reversal, are not overlooked or do not inappropriately impact measure performance. ASA looks forward to the MAP’s discussion and assessment of this measure. (Submitted by: American Society of Anesthesiologists)
  
  
• This measure does not take into account the uses of naloxone for indications other than those related to adverse respiratory events. Furthermore, the measure has not been endorsed and is currently being field tested. While GNYHA supports the direction of this measure and its importance on addressing opioid-related adverse events, we recommend further development before adopting it as part of HIQR. A potential refinement of the measure specifications could include narrow dose and route of administration parameters to focus the measure on naloxone use for adverse respiratory events. (Submitted by: Greater New York Hospital Association)
  
  

• According to NQF-QPS endorsement has been removed. The measure is currently included in the program but should be removed. (Submitted by: Premier, Inc.)
  
  
• We support this measure as addressing the underlying condition is a significant priority. (Submitted by: Ascension)
  
  

• Given the increase in the number and complexity of procedures performed at ASCs, and the lack of information about the quality and safety of those facilities, Leapfrog strongly supports the addition of this measure. This addition is critical missing component of the ASC Quality Reporting Program. (Submitted by: The Leapfrog Group)
  
  
• On behalf of the ASC Quality Collaboration (ASC QC), thank you for considering the following comments regarding 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. We have several areas of concern regarding this measure, which we describe briefly below. A. Attribution of Outcomes The measure’s methodology for identifying outcomes is flawed. Review of Table 4 of the measure documentation, titled “Top hospital visit diagnoses for any hospital visit within 7 days of general surgery procedures (dataset: Medicare FFS CY 2015)”, shows that there are a significant number of outcomes that are inappropriately included and identified as “quality signals”. Several of these “top diagnoses” do not indicate poor quality of care, but rather reflect the indication for the patient’s ASC care. For example, a diagnosis of lymphoma following surgery on the hemic or lymphatic system is not an indication of an acute illness or complication of care. It reflects a new diagnosis established by the patient’s surgery for which treatment is being rendered. Similarly, a new diagnosis of a breast neoplasm is not an illness caused by the index breast surgery or a complication of the surgery itself. Conditions such as the acquired absence of a breast/nipple are expected following a mastectomy, and healthcare providers should not be penalized for the patient’s expected post-operative status. Since only the “top hospital visit diagnoses” have been presented, it is not clear how many other inappropriate outcomes are included in the measure dataset. B. The Measure Cohort The measure focuses on outpatient procedures that are within the scope of general surgery training, including abdominal, alimentary tract, breast, skin/soft tissue, wound, and varicose vein procedures. The majority of the cases included in the measure are skin and soft tissue procedures, which have been included in order to generate sufficient volume for the cohort. However, the resultant case mix diverges significantly from the typical practice of a general surgeon in the ASC setting. The inclusion of so many skin repair, graft and plastic repair surgeries of the face - including eyelids, ears, nose, lips, forehead, cheeks, and chin makes little sense. Theses services are not routinely performed by general surgeons in ASCs, but rather by other physician specialties. (Please do not misunderstand: we are not saying general surgeons do not or cannot perform these surgeries. We are saying this does not reflect how general surgeons spend their operating time in ASCs.) The title of the measure sets the expectation that the results will be reflective of the practice of general surgery in the ASC setting. If CMS and the developer remain intent on retaining all the skin surgeries, it would be helpful to rename the measure so it better reflects 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. Changing the title would also help improve the face validity of the measure. C. Low ASC Case Volumes The measure developer has indicated that the high number of low-volume ASCs made the development of this measure challenging. To manage this, the measure has been specified in ways that prop up the overall volume of cases included for analysis, principally through the inclusion of large numbers of skin surgeries as described above. The other strategy used to inflate the overall volume of cases 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 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).” Despite the inclusion of cases that are not typically performed by general surgeons in ASCs, the majority of ASCs had very few cases in the Medicare FFS CY 2015 dataset. This low volume of services would not provide sufficient information about the quality of care in individual facilities, and therefore measure scores should not be used for public reporting and accountability. D. Limited Ability to Make Distinctions Among Facilities According to the developer and CMS the purpose of this measure is threefold: “to illuminate variation in quality of care for general surgery procedures across ASCs, inform patient choice, and drive quality improvement.” This measure does not achieve these objectives. The measure does not illuminate variation in quality of care. Review of the measure documentation reveals that the developers initially used unadjusted outcome rates to assert a variation in quality. Following adjustment, there is virtually no discernable variability in performance: using the standard 95 percent interval estimate to report the measure score, 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”. This lack of variability also impacts the second objective – to inform patient choice. 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. In addition, the inclusion of so many procedures that are typically performed by physicians other than general surgeons tends to obscure the outcomes that are related to the actual practice of general surgery in ASCs. Patients would be unlikely to understand this, and could be led to believe that the rates reflect performance for the procedure they are considering having performed by their general surgeon in the ASC setting. Finally, the measure is unlikely to drive performance improvement in a meaningful way when only 16 facilities “underperform”. Even this underperformance is questionable given the issues with the measure’s misclassification of outcomes, such new cancer diagnoses, as discussed in Section A above. Industry 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 were ultimately identified as performing worse than the national rate. *** We encourage the Hospital Workgroup to issue a “Do Not Support for Rulemaking” recommendation for this measure. A “Refine and Resubmit” might seem a logical choice, however experience indicates that when the MAP issues this particular recommendation, CMS does not refine and resubmit, so there is little point in selecting thisoption when measures might have merit, but are not ready for inclusion in a quality reporting program. (Submitted by: ASC Quality Collaboration)
  
  
• The American Medical Association (AMA) questions whether the information provided as a result of this measure is truly useful for accountability and informing patients of the quality of care provided by ambulatory surgical centers (ASCs). Specifically, our concern relates to the relatively limited amount of variation across applicable ASCs found during testing. On review of the technical report released in September of this year, most ASCs had low rates of hospital visits, the variation across all 1,651 ASCs was minimal (less than 4%) and only 30 centers were identified as significant outliers. In addition, we do not believe that the measure is adequately adjusted for social risk factors. Developers must continue to be responsive to emerging measure methodologies and the traditional approach of risk adjusting at the patient level may not be appropriate for those measures where the measurement period includes care that is outside of the control of the ASC. The AMA believes that there may be community-level variables that could impact the risk model of this measure that are not addressed. Measures that extend beyond the procedure or outside the locus of control of the measured entity should continue to have adjustment for social risk factors addressed and new variables analyzed and at different levels (e.g., patient, ASC, and community). In addition, the developer should continue to explore new variables that are directly related to the community in which a patient resides, particularly given the recent report from the Office of the Assistant Secretary for Planning and Evaluation (ASPE). Until these questions and potential risk factors are examined further, we do not believe that this measure is ready for implementation in ASCQR. (Submitted by: American Medical Association)
  
  
• Not endorsed. Similar measures for specific procedures just added to the program. SES adjust. Similar measure for OQR just endorsed. Was it tested at the ASC facility level, with feedback reports to facilities? Condition/procedure-specific is more amenable to improvement. (Submitted by: Premier, Inc.)
  
  
• We support this measure, which can serve as a counter balance to incentives for the increased use of outpatient care when that may not be the most appropriate setting. (Submitted by: Ascension)
  
  
• • Medtronic’s Minimally Invasive Therapies Group supports this measure that assesses ASC general surgery procedure quality for three reasons: 1) Procedural sedation-related adverse events that can lead to hospital visits, including severe oxygen desaturation, bradycardia, hypotension and cardiac arrest, are common and occur in 0.00042-7.2% 1,2of patients. 2) Tracking ACS procedure quality may help with collecting data on unexpected hospital visits following an ACS procedure and promote strategies to improve quality of patient care. 3) Minimally invasive surgery in, for example, hernia repair, a common ASC procedure, has shown to be associated with less post-operative hospital visits, emergency room visits3, and readmissions.4 References 1 . Frieling T, Heise J, Kreysel C, Kuhlen R, Schepke M. Sedation-associated complications in endoscopy--prospective multicentre survey of 191142 patients. Zeitschrift fur Gastroenterologie. 2013;51(6):568-572. 2. Newstead B, Bradburn S, Appelboam A, et al. Propofol for adult procedural sedation in a UK emergency department: safety profile in 1008 cases. British journal of anaesthesia. 2013;111(4):651-655. 3. Mikami D, Melven S, Murayama M, Murayama K, Impact of minimally invasive surgery on healthcare utilization, cost, and workplace absenteeism in patients with Incisional/ Ventral Hernia (IVH). Surgical Endoscopy 2017 Nov;31(11):4412-4418 4. Fitch K, Engel T, Bochner, A, Cost Differences Between Open and Minimally Invasive Surgery. Managed Care Magazine 2015 Sep;24(9):40-8. (Submitted by: Medtronic - Minimally Invasive Therapies Group)
  
  
• The FAH agrees with the potential for this measure to support quality improvement efforts but as noted during the FAH’s comments during the public comment period this summer, we question whether there is sufficient variation in performance across the ambulatory surgical centers (ASCs) to support its use in accountability programs. Specifically, the performance scores reported in the technical report were generally low and there was less than 4% difference between the bottom and top percentiles of ASCs, with only 30 out of the 1,651 ASCs being identified as significant outliers. In addition, the FAH was disappointed to see that the risk adjustment model does not include the identification and testing of social risk factors. Given that this is a new measure, it provided an opportunity for the measure developer to include these factors within the testing of the model rather than the previous approach of “adding on” factors after the model is developed. This type of approach would assist hospitals and others in understanding how their inclusion could impact the model and provide additional information for groups examining this issue such as the NQF and Office of the Assistant Secretary for Planning and Evaluation. As a result, the FAH remains concerned that this measure is not appropriate for use in the ASCQR program. (Submitted by: Federation of American Hospitals (FAH))
  
  
• The American Society of Anesthesiologists (ASA) supports the continued development of this measure and would welcome the opportunity to work with CMS and other stakeholders on this measure moving forward. This measure is of particular interest to physician anesthesiologists in the Ambulatory Surgical Center (ASC) care setting and supports team-based care and shared accountability throughout the perioperative period. In the future, this measure could also be appropriate for other settings such as hospital outpatient surgery centers. Additionally, this measure could have potential unintended consequences related to patient selection. ASA looks forward to the MAP’s discussion and assessment of this measure. (Submitted by: American Society of Anesthesiologists)
  
  
• GNYHA opposes including Hospital Visits following General Surgery Ambulatory Surgical Center Procedures (MUC17-233) in ASCQR. This measure has not been submitted for endorsement to NQF and there is no clear timeline for endorsement. Measures that have not been evaluated and endorsed by NQF for feasibility, validity and applicability to quality improvement should not be included in CMS programs. In addition to not being fully vetted, it is ambiguous whether this measure is attempting to capture surgical complications versus more general health care utilization, which compromises the clinical utility of the metric. Since the measure is claims-based, the numerator criteria should reflect a principle diagnosis of a complication related to the surgery that was performed within the 7 day window that the measure is proposing. In addition, exclusions should reflect diagnoses that are clearly unrelated to the surgery, e.g. injuries due to trauma, chronic conditions. (Submitted by: Greater New York Hospital Association)
  
  
• MUC17-233: No issues with this measure, per se. However, it would seem that a similar measure for a hospital visit within a 90-day period following initiation of chronic wound treatment in a hospital outpatient setting could be plausible. We continue to be concerned that no wound care-specific measures are proposed for chronic wound procedures (debridement, application of skin substitutes, etc.). There are measures relating to underlying diseases such as diabetes or vascular disease, but none focused on outcomes for the treatment of wounds that occur with these conditions. Without quality measures for chronic wounds (diabetic, vascular, pressure ulcer) in settings where most treatment occurs (hospital outpatient (HOQRP) and physician office (MIPS, MSSP)), we cannot drive innovation and lower cost of care. (Submitted by: Integra LifeSciences)
  
  

• 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: • NQF endorsement. KCP notes that neither transplantation access metric is NQF-endorsed, a general pre-requisite for KCP to support inclusion of a measure in any accountability program. • Facility attribution. Again, KCP strongly objects to attributing successful/unsuccessful placement on a transplant waitlist to dialysis facilities. 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. 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 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. 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. Moreover, 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. • 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. 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. • 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 measure is appropriate for use in the ESRD QIP. (Submitted by: Kidney Care Partners)
  
  
• The National Kidney Foundation requests changes to this measure before it is implemented in the ESRD QIP. Some patients under age 75 may not be eligible for transplantation due to other clinical reasons. In addition, in some cases even the most informed and educated patient may ultimately choose not to pursue a transplant. Limited, but additional exclusions to account for these circumstances should be evaluated. Ultimately, the decision on whether a patient is listed for a transplant is made by the transplant center that evaluated the patient (and the patient’s desire for a transplant). These are complex decisions that take into account many factors and vary by transplant center and geographic region, which would make nationwide comparisons of waitlist percentages difficult to interpret. The effect of this variance in transplantation policy on dialysis facility performance on this measure should be considered prior to implementation. The National Kidney Foundation believes the percentage of prevalent patients waitlisted (PPPW) is meaningful for patients. Dialysis facilities can help support patients in maintaining their active status on the waitlist for routine antibody and other periodic testing. This measure would incentivize greater care coordination by the dialysis facility with the transplant center. Many transplant centers have dialysis outreach programs to better educate facility staff and patients about the transplant process and the patient and dialysis facility role in the process. However, gaps in patients getting waitlisted remain. Patients continue to report that they were not fully informed about transplant or were provided misinformation that led them not to not pursue transplant. Holding dialysis facilities accountable for ensuring their patient population is knowledgeable about transplant and supporting patients to maintain their status on the waitlist will help address this current gap in care. The National Kidney Foundation believes this measure, with the improvements suggested, could improve collaboration between dialysis facilities and transplant centers. (Submitted by: National Kidney Foundation)
  
  
• We would support the use of either MUC17-241 or MUC17-245; the use of both measures would be redundant. (Submitted by: Ascension)
  
  

• 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: • NQF endorsement. KCP notes that neither transplantation access metric is NQF-endorsed, a general pre-requisite for KCP to support inclusion of a measure in any accountability program. • Facility attribution. Again, KCP strongly objects to attributing successful/unsuccessful placement on a transplant waitlist to dialysis facilities. 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. 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 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. 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. Moreover, 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. • 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. 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. • 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 SWR measure is appropriate for use in the ESRD QIP. (Submitted by: Kidney Care Partners)
  
  
• The National Kidney Foundation does not support this measure. NKF appreciates the intent of this measure to ensure that patients are waitlisted as early as possible after starting dialysis, if they were not already waitlisted. However, we are concerned this measure is limited in terms of actionability by the dialysis center as the ultimate decision on waitlist status is made by the transplant center and the patient. Dialysis facilities have a role in educating patients about transplant and supporting their active listing. However, incident dialysis patients, who were not listed before starting dialysis, may be more complex and have comorbidities that make them ineligible for the waitlist during the first year. While it is the responsibility of the dialysis facility to work to improve the health and functional status of dialysis patients during the first year, much of the final decision, regarding acceptance to a transplant list, is beyond their control. In addition, dialysis units involved in pre-education and care coordination in the transition of advanced CKD to ESRD would not be recognized for pre-emptively having patients on the waitlist. (Submitted by: National Kidney Foundation)
  
  
• We would support the use of either MUC17-241 or MUC17-245; the use of both measures would be redundant. (Submitted by: Ascension)
  
  

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.

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