• Patients younger than 18 years old at the start of the HH stay.
• HH stays beginning with a Low Utilization Payment Adjustment (LUPA) claim.
• Patients receiving services from multiple HHAs during the HH stay.
• Patients not continuously enrolled in Part A FFS Medicare for the 12 months prior to the HH stay.
• The patient is missing information needed for risk adjustment.



• HHS NQS Priority: Communication and Care Coordination
  
  
• HHS Data Source: Claims
  
  
• Measure Type: Outcome
  
  
• Steward: Centers for Medicare & Medicaid Services
  
  
• Endorsement Status:
  
  
• Meaningful Measure Area: Admissions and readmissions to hospitals
  
  
• Is the measure specified as an electronic clinical quality measure? No
  
  

Summary of Workgroup Deliberations

• Workgroup Recommendation: Conditional Support for Rulemaking
  
  
• Workgroup Rationale: MAP conditionally supported MUC2019-34 Home Health Within-Stay Potentially Preventable Hospitalization, pending NQF review and endorsement. CMS clarified that it intends to eventually replace related measures, NQF 0171 Acute Care Hospitalization During the First 60 Days of Home Health and NQF 0173 Emergency Department Use without Hospitalization During the First 60 Days of Home Health with the measure under consideration. MAP agreed that the measure adds value to the program measure set by adding measurement of potentially preventable hospitalizations and observation stays that may occur at any point in the home health stay. No measure in the program currently provides this information. The measure supports alignment for the measure focus area of admissions and readmissions across care settings and providers. There is variation in performance on this measure, and home health agencies have the ability to implement processes and interventions that can positively influence the measure results. The MAP Rural Health Workgroup noted that older, poorer, and sicker patients reflected in rural populations often have issues with access to care. No public comments were received on this measure or on the HH QRP. MAP encouraged consideration of including Medicare Advantage patients in future iterations of the measure.
  
  
• Public comments received: 2

Rationale for measure provided by HHS
Factors associated with hospitalizations from HH including functional disability, primary diagnoses of heart disease, and primary diagnosis of skin wounds (Lohman et al, 2017). Some other factors associated with hospitalization include time since most recent hospitalization (Hua et al, 2015) and chronic conditions such as chronic obstructive pulmonary disease and congestive heart failure (Dye et al, 2018). These factors, including how HHAs address chronic conditions present before the HH stay, can determine whether patients can successfully avoid hospitalizations (Lohman et al, 2017). Understanding these factors can help HHAs design strategies to address avoidable hospitalizations.References:1. Lohman MC, Cotton, BP, Zagaria, AB, Bao, Y, Greenberg, RL, Fortuna, KL, Bruce, ML Hospitalization Risk and Potentially Inappropriate Medications among Medicare Home Health Nursing Patients,( 2017) J Gen Intern Med. 32(12):1301-1308.2. Hua M, Gong, MN, Brady J, Wunsch, H, Early and late unplanned rehospitalizations for survivors of critical illness(2015) Crit Care Med.;43(2):430-4383. Dye C, Willoughby D, Aybar-Damali B, Grady C, Oran R, Knudson A, Improving Chronic Disease Self-Management by Older Home Health Patients through Community Health Coaching (2018) Int J Environ Res Public Health. 15(4): 660

Measure Specifications

• NQF Number (if applicable): 0
  
  
• Description: Structural Measure to address severe maternal morbidity in the inpatient hospital setting.
  
  
• Numerator: One question attestation – “Does your hospital or health system participate in a Statewide and/or National Perinatal Quality Improvement Collaborative Program aimed at improving maternal outcomes during inpatient labor, delivery and post-partum care, which includes implementation of patient safety practices or bundles to address complications, including, but not limited to, hemorrhage, severe hypertension/preeclampsia or sepsis? o Yes o No o N/A (our hospital does not provide inpatient labor & delivery care)
  
  
• Denominator: Not Applicable
  
  
• Exclusions: None
  
  
• HHS NQS Priority: Communication and Care Coordination
  
  
• HHS Data Source: TBD
  
  
• Measure Type: Structure
  
  
• Steward: Centers for Medicare & Medicaid Services
  
  
• Endorsement Status:
  
  
• Meaningful Measure Area: Preventable healthcare harm
  
  
• Is the measure specified as an electronic clinical quality measure? No
  
  

Summary of Workgroup Deliberations

• Workgroup Recommendation: Do Not Support for Rulemaking with Potential for Mitigation
  
  
• Workgroup Rationale: MAP did not support MUC2019-114 Maternal Morbidity for rulemaking, with potential for mitigation. The potential mitigating factors identified by MAP would be to adjust the language of the question to clarify that the hospital is expected both to attest to participation in a quality improvement initiative as well as to implement patient safety practices or bundles to address complications and that the Maternal Morbidity measure go through the NQF endorsement process. MAP observed that severe maternal morbidity is increasing at an alarming rate in the U.S., nearly doubling in the last decade. There are currently no quality measures that address maternal morbidity, and MAP is encouraged by CMS’s attempts to address this healthcare crisis. However, MAP expressed concern related to using attestation to participation in a quality improvement initiative rather than finding clear process and outcomes measures that address the quality issue directly, such as asking if specific bundles of care are incorporated into the services provided during maternal care. MAP members identified the language “and has implemented patient safety practices or bundles” to replace “which includes implementation of patient safety practices or bundles” as one way to add clarity that the intent of the measure is both to participate in a QI program and implement specific bundles known to improve outcomes. Finally, the Rural Health Workgroup noted a concern that not all rural critical access hospitals would be able to participate in a state QI collaborative. MAP noted that this is balanced by the universal availability of national-level QI programs.
  
  
• Public comments received: 9

Rationale for measure provided by HHS
The rationale for this measure to address Severe Maternal Morbidity (SMM) is that SMM is increasing at an alarming rate in the U.S. Rates have nearly doubled over the past decade. Evidence shows that there is a high rate of preventability of SMM and 60% of maternal deaths are preventable. Identification and effective treatment of SMM are very essential to prevent conditions that lead to maternal mortality. There are currently no quality measures that address maternal morbidity as a whole and the CMS Office of the Administrator (OA) is very dedicated in addressing this healthcare crisis. The structural measure will evaluate how many hospitals and health systems are working within any type of quality improvement collaborative which has proven to help prevent and manage SMM. This measure will eventually be replaced by a comprehensive outcome measure.

Measure Specifications

• NQF Number (if applicable): 0
  
  
• Description: This measure assesses the proportion of hospital days with a severe hyperglycemic event for hospitalized patients 18 or older who have a diagnosis of diabetes mellitus, have received at least one administration of insulin or an anti-diabetic medication during the hospital admission, or have had an elevated blood glucose level (>200 mg/dL) during their hospital admission.
  
  
• Numerator: Number of hospital days with a severe hyperglycemic event, defined as: (1) a day with at least one blood glucose value >300 mg/dL; or (2) a day without any measurement of blood glucose level, and not preceded by two consecutive days where blood glucose levels were measured and all values were <200 mg/dL.Hospital days are measured in 24-hour periods starting from the time of arrival at the hospital (including Emergency Department).
  
  
• Denominator: The initial population is patients 18 years and older (age at the start of the hospital encounter) with a discharged inpatient hospital encounter during the measurement period, and at least one of the following: (1) a diagnosis of diabetes that starts before or during the encounter; or (2) at least one administration of insulin or any anti-diabetic medication during the encounter; or (3) at least one blood glucose value >200 mg/dL at any time during the encounter. The measure includes inpatient admissions for patients who were directly admitted, or who were initially seen in the emergency department or in observation status and subsequently became an inpatient.The denominator is the total number of eligible days across all encounters which match the initial population criteria. We do not count the first 24-hour period after admission to the hospital (including the Emergency Department) or the last time period before discharge, if it was less than 24 hours. Eligible encounters that exceed 10 days are truncated to equal 10 days.
  
  
• Exclusions: N/A
  
  
• HHS NQS Priority: Communication and Care Coordination
  
  
• HHS Data Source: EHR
  
  
• Measure Type: Outcome
  
  
• Steward: Centers for Medicare & Medicaid Services
  
  
• Endorsement Status:
  
  
• Meaningful Measure Area: Preventable healthcare harm
  
  
• Changes to Endorsed Measure Specifications?: The MUC list indicates the measure has not been modified from its endorsed version.
  
  
• Is the measure specified as an electronic clinical quality measure? Yes
  
  

Summary of Workgroup Deliberations

• Workgroup Recommendation: Conditional Support for Rulemaking
  
  
• Workgroup Rationale: MAP offered conditional support for MUC2019-26 Hospital Harm – Severe Hyperglycemia, pending NQF endorsement of the measure. IQR currently does not include a measure that assesses severe hyperglycemia events that are largely avoidable through proper glycemic monitoring and intervention. MAP expressed concern and encouraged CMS to consider the unintended consequence that this measure may lead to increases in hypoglycemia, which was regarded as a more serious issue. The Rural Health Workgroup noted that diabetes rates are high in rural settings, and the measure addresses a preventable patient safety issue that is relevant for rural populations. The Rural Health Workgroup expressed concern that if glucose levels are derived from laboratory data (rather than at point of care), they may be more difficult to obtain and/or incorporate into EHR systems in rural hospitals. They also were concerned that EHR systems in rural hospitals may not be as robust or current, making it more difficult to compute the measure (e.g., using RxNORM). Finally, MAP generally agreed that the measure did not carry any significant implementation burden.
  
  
• Public comments received: 13

Rationale for measure provided by HHS
Severe hyperglycemia - an extremely elevated blood glucose level - is significantly associated with a range of harms, including increased in-hospital mortality, infection rates, and hospital length of stay.5-9 Moreover, the rate of severe hyperglycemia varies across hospitals, suggesting opportunities for improvement in inpatient glycemic management.10 The rate of inpatient hyperglycemia can be considered a marker for quality of hospital care, since inpatient hyperglycemia is largely avoidable with proper glycemic management. The use of evidence-based standardized protocols and insulin management protocols have been shown to improve glycemic control and safety.11-12References:5. Falciglia M, Freyberg RW, Almenoff PL, D'Alessio DA, Render ML. Hyperglycemia-Related Mortality in Critically Ill Patients Varies with Admission Diagnosis. Crit Care Med. 2009;37(12):3001-3009. 6. King JT, Jr., Goulet JL, Perkal MF, Rosenthal RA. Glycemic Control and Infections in Patients with Diabetes Undergoing Noncardiac Surgery. Ann Surg. 2011;253(1):158-165. 7. Pasquel FJ, Spiegelman R, McCauley M, et al. Hyperglycemia During Total Parenteral Nutrition: An Important Marker of PoorOutcome and Mortality in Hospitalized Patients. Diabetes Care. 2010;33(4):739-741. 8. Rady MY, Johnson DJ, Patel BM, Larson JS, Helmers RA. Influence of Individual Characteristics on Outcome of Glycemic Control in Intensive Care Unit Patients With or Without Diabetes Mellitus. Mayo Clin Proc. 2005;80(12):1558-1567. 9. Umpierrez GE, Isaacs SD, Bazargan N, You X, Thaler LM, Kitabchi AE. Hyperglycemia: An Independent Marker of In-Hospital Mortality in Patients with Undiagnosed Diabetes. J Clin Endocrinol Metab. 2002;87(3):978-982. 10. Swanson CM, Potter DJ, Kongable GL, Cook CB. Update on Inpatient Glycemic Control in Hospitals in the United States. Endocr Pract. 2011;17(6):853-861. 11. Donihi AC, DiNardo MM, DeVita MA, Korytkowski MT. Use of a Standardized Protocol to Decrease Medication Errors and Adverse Events Related to Sliding Scale Insulin. Qual Saf Health Care. 2006;15(2):89-91. 12. Maynard G, Kulasa K, Ramos P, et al. Impact of a Hypoglycemia Reduction Bundle and a Systems Approach to Inpatient Glycemic Management. Endocr Pract. 2015;21(4):355-367.

Measure Specifications

• NQF Number (if applicable): 0
  
  
• Description: The proportion of hospice patients who have received visits from a Registered Nurse or Medical Social Worker (non-telephonically) on at least two out of the final three days of the patient's life.
  
  
• Numerator: The numerator of this measure is the number of patient stays in the denominator in which the patient and/or caregiver received at least two days with visits from registered nurses or medical social workers in the final three days of life, as captured by hospice claims records.Registered nurse visits are identified by revenue code 055x (with the presence of HCPCS code G0299); Non-telephone visits are MSWs are identified by revenue code 056x (other than 0569; HCPCP code G0155)
  
  
• Denominator: The denominator for the measure includes all hospice patient stays enrolled in hospice except those meeting exclusion criteria.
  
  
• Exclusions: Patient stays are excluded from the measure if the patient did not expire in hospice care or if the patient received any continuous home care, respite care, or general inpatient care in the final three days of life.The exclusion criteria are:1.Patient did not expire in hospice care as indicated by reason for discharge (exclude if PTNT_DSCHRG_STUS_CD does not equal [40, 41, or 42]]); OR2.Patient received any continuous home care, respite care or general inpatient care in the final three days of life (exclude if revenue codes = [0652, 0655, or 0656]).3.Patient was enrolled in hospice at least three days.
  
  
• HHS NQS Priority: Communication and Care Coordination
  
  
• HHS Data Source: Claims; CAHPS Hospice Scores
  
  
• Measure Type: Process
  
  
• Steward: Centers for Medicare & Medicaid Services
  
  
• Endorsement Status:
  
  
• Meaningful Measure Area: End of life care according to preferences
  
  
• Is the measure specified as an electronic clinical quality measure? No
  
  

Summary of Workgroup Deliberations

• Workgroup Recommendation: Conditional Support for Rulemaking
  
  
• Workgroup Rationale: MAP recommended conditional support for rulemaking pending NQF review and endorsement and further recommended that the existing hospice visit measures be removed from the program. Collecting information about hospice staff visits for measuring quality of care will encourage hospices to visit patients and caregivers and provide services that will address their care needs and improve quality of life during the patient’s last days of life. MAP observed that currently, Hospice Visits When Death is Imminent, Measure 1 and Measure 2, address this quality objective in the Hospice QRP, but this measure performed better in validity and reliability testing and has lower provider burden because it is reported using claims data. MAP agreed that the goal of hospice is comfort. MAP encouraged that future iterations of this measure consider the quality of provider visits in addition to the quantity of visits. MAP members reviewed analysis from CMS demonstrating that not all types of provider visits correlate positively with Hospice CAHPS results. MAP examined the possible variations on the measure concept and generally agreed that the analysis supported the current proposed measure. The MAP Rural Health Workgroup noted concerns related to access to care in rural areas. Public comments expressed concern about overlap with the existing hospice visits measures. Commenters also had concerns with a hospice program’s ability to accurately identify imminent death, and with only including some members of the interdisciplinary team in the visits captured in the measure. The commenters suggested examining other options for this measure concept such as different numbers of visits.
  
  
• Public comments received: 3

Rationale for measure provided by HHS
There is evidence available from clinical organizations and panels, as well as from individual studies, supporting the measure's basis that clinician visits to patients at the end of life are associated with improved outcomes for both the patients and their caregivers. The last week of life is typically the period in the terminal illness trajectory with the highest symptom burden. Particularly during the last few days before death, patients experience many physical and emotional symptoms, necessitating close care and attention from the integrated hospice team and drawing increasingly on hospice team resources (de la Cruz 2014, Dellon 2010, Kehl 2013). Highly specific physical signs associated with death were identified within 3 days of death (Hui et al., 2014). Hospice responsiveness during times of patient and caregiver need is an important aspect of care for hospice patients (Ellington 2016). Although Medicare-certified hospices do not have any mandated minimum number of required visits for patients in routine home care (RHC), the most common level of hospice care, at the end of life, hospices should be equipped to meet the higher symptom and caregiving burdens of patients and their caregivers during this critical period (Teno 2016). Clinician visits to patients at the end of life are associated with decreased risk of hospitalization and emergency room visits in the last 2 weeks of the patients’ life, decreased likelihood of a hospital-related disenrollment, as well as decreased odds of dying in the hospital (Sewo 2010, Phongtankuel 2018, Almaawiy 2014). In addition, clinician visits to patients at the end of life is also associated with decreased distress for caregivers and higher satisfaction with home care (Pivodic 2016).Visits by staff who can assess symptoms and make changes to the plans of care as well as work with the patient and the primary caregiver to provide the appropriate palliation and emotional support (nurses, social workers, and physicians) are important to the quality of care hospices deliver, as noted by the NQF’s preferred practices on the recognition and management of the actively dying patient (Teno 2016). During the development of the Family Evaluation of Hospice Care survey, families voiced the importance of visits by these staff in the last days of life (Teno 2016).Citations:de la Cruz, M., et al. (2015). Delirium, agitation, and symptom distress within the final seven days of life among cancer patients receiving hospice care. Palliative & Supportive Care, 13(2): 211-216. doi: 10.1017/S1478951513001144Dellon, E. P., et al. (2010). Family caregiver perspectives on symptoms and treatments for patients dying from complications of cystic fibrosis. Journal of Pain & Symptom Management, 40(6): 829-837. doi: 10.1016/j.jpainsymman.2010.03.024Kehl, K. A., et al. (2013). A systematic review of the prevalence of signs of impending death and symptoms in the last 2 weeks of life. American Journal of Hospice & Palliative Care, 30(6): 601-616. doi: 10.1177/1049909112468222Hui D et al. (2014). Clinical Signs of Impending Death in Cancer Patients. The Oncologist. 19(6):681-687. doi:10.1634/theoncologist.2013-0457.Ellington, L., et al. (2016). Interdisciplinary Team Care and Hospice Team Provider Visit Patterns during the Last Week of Life. Journal of Palliative Medicine, 19(5), 482-487. doi: 10.1089/jpm.2015.0198Teno, J. M., et al. (2016). Examining Variation in Hospice Visits by Professional Staff in the Last 2 Days of Life. JAMA Internal Medicine, 176(3): 364-370. doi: 10.1001/jamainternmed.2015.7479Seow, H., Barbera, L., Howell, D., & Dy, S. M. (2010). Using more end-of-life homecare services is associated with using fewer acute care services: A population-based cohort study. Medical Care, 48(2): 118−124. doi: 10.1097/MLR.0b013e3181c162efPhongtankuel, V., et al. (2018). Association Between Nursing Visits and Hospital-Related Disenrollment in the Home Hospice Population. American Journal of Hospice & Palliative Medicine, 35(2): 316-323. doi: 10.1177/1049909117697933Almaawiy, U., et al. (2014). Are family physician visits and continuity of care associated with acute care use at end-of-life? A population-based cohort study of homecare cancer patients. Palliative Medicine, 28(2), 176−183. doi: 10.1177/0269216313493125Pivodic, L., Harding, R., Calanzani, N., McCrone, P., Hall, S., Deliens, L., & Gomes, B. (2015). Home care by general practitioners for cancer patients in the last 3 months of life: An epidemiological study of quality and associated factors. Palliative Medicine, 30(1), 64−74. doi:10.1177/0269216315589213Pivodic, L., Harding, R., Calanzani, N., McCrone, P., Hall, S., Deliens, L., & Gomes, B. (2015). Home care by general practitioners for cancer patients in the last 3 months of life: An epidemiological study of quality and associated factors. Palliative Medicine, 30(1), 64−74. doi:10.1177/0269216315589213

Measure Specifications

• NQF Number (if applicable): 0
  
  
• Description: The Follow-Up After Psychiatric Hospitalization measure assesses the percentage of inpatient discharges with principal diagnoses of select mental illness or substance use disorders (SUD) for which the patient received a follow-up visit for treatment of mental illness or SUD. Two rates are reported:1. Percentage of discharges for which the patient received follow-up within 7 days of discharge2. Percentage of discharges for which the patient received follow-up within 30 days of dischargeThe performance period used to identify denominator cases is 12 months. Data from the performance period and 30 days after are used to identify follow-up visits in the numerator.
  
  
• Numerator: The numerator includes discharges from a psychiatric facility that are followed by an outpatient visit for treatment of mental illness or SUD within 7 and 30 days. Outpatient visits are defined as outpatient visits, intensive outpatient encounters, or partial hospitalization. For additional information, see HSAG. Draft Methodology Report: Follow-Up After Psychiatric Hospitalization, Version 1.0. Prepared for CMS by Health Services Advisory Group, Inc., Contract HHSM-500-2013-13007I, Task Order HHSM-500-T0004, 37 pages (7 January 2019).
  
  
• Denominator: The denominator includes discharges paid under the inpatient psychiatric facility (IPF) prospective payment system (PPS) during the performance period for Medicare fee-for-service (FFS) patients with a principal diagnosis of mental illness or substance use disorder. For additional information, see HSAG. Draft Methodology Report: Follow-Up After Psychiatric Hospitalization, Version 1.0. Prepared for CMS by Health Services Advisory Group, Inc., Contract HHSM-500-2013-13007I, Task Order HHSM-500-T0004, 37 pages (7 January 2019).
  
  
• Exclusions: Medicare files are used to identify all exclusions. The denominator excludes inpatient psychiatric facility (IPF) discharges for patients:
  • Admitted or transferred to acute and non-acute inpatient facilities within the 30-day follow-up period because admission or transfer to other institutions may prevent an outpatient follow-up visit from taking place.
  • Who were discharged against medical advice (AMA) because the IPF may have limited opportunity to complete treatment and prepare for discharge.
  • Who died during the 30-day follow-up period because patients who expire may not have the opportunity for an outpatient follow-up visit.
  • Who use hospice services or elect to use a hospice benefit any time during the measurement year, regardless of when the services began because patients in hospice may require different follow-up services.
  
  
  
• HHS NQS Priority: Communication and Care Coordination
  
  
• HHS Data Source: Claims
  
  
• Measure Type: Process
  
  
• Steward: Centers for Medicare & Medicaid Services
  
  
• Endorsement Status:
  
  
• Meaningful Measure Area: Prevention, treatment, and management of mental health
  
  
• Is the measure specified as an electronic clinical quality measure? No
  
  

Summary of Workgroup Deliberations

• Workgroup Recommendation: Do Not Support for Rulemaking
  
  
• Workgroup Rationale: MAP did not support MUC2019-22 for rulemaking. MAP noted that this measure is an expansion of the existing NQF 0576 Inpatient Psychiatric Facility Quality Reporting Program Follow-Up After Hospitalization for Mental Illness measure, broadening the measure population to include patients hospitalized for drug and alcohol disorders as those patients also require follow-up care post-discharge. MAP noted the importance of robust care transitions for this expanded population but also identified several critical concerns with the proposed measure. MAP expressed concern that the numerator requires patient choice in pursuing follow-up care and may not reflect whether follow-up care has been arranged by the hospital being measured. MAP also noted that the Stark Law may limit the ability for hospitals and care managers to ensure necessary SUD treatment follow-up after hospitalization. MAP members were also concerned that patients may not have access to appropriate SUD outpatient follow-up care. MAP members also noted the importance of telehealth follow-up as a critical tool and the importance of including these visit types in the measure. CMS noted that telehealth is currently billable in a limited fashion, only if it is submitted with a GT modifier. MAP was generally not satisfied in the current specifications and expressed concern that the measure could lead to unintended negative consequences for patients. Finally, several members noted that the evidence base for this measure needs to be specific to the conditions of interest. The MAP Rural Health Workgroup viewed this measure as appropriate, as SUD and mental health issues impact many rural residents, but the Workgroup expressed concern about to access to care, recommending telehealth follow-up as a potential solution, which would harmonize with the NCQA HEDIS measure.
  
  
• Public comments received: 4

Rationale for measure provided by HHS
Studies have found that readmission rates for those with psychiatric diagnoses are lower if patients receive follow-up visits within 30 days of discharge. A 2017 study found that receipt of a follow-up visit within 30 days of hospital discharge lowered the readmission risk during days 31 to 120 for patients with schizophrenia and for patients with bipolar disorder. Similarly, a 2018 study observed that among patients discharged with schizophrenia, psychiatric readmission rates on days 31-180 were lower if the patient saw a primary care physician or psychiatrist within 30 days of discharge.Inpatient psychiatric facilities can influence rates of follow-up care for patients hospitalized for mental illness or SUD. Interventions that have been shown effective in the literature include following up with letters or telephone calls, discussing barriers to attending the first outpatient post-discharge appointment with the patient, and serving as a contact for questions or concerns between discharge and the first outpatient appointment. Three studies reported that with certain interventions facilities achieved follow-up rates of 88 percent or more, compared to the national 30-day follow-up rate of approximately 54 percent observed in the current Inpatient Psychiatric Facility Quality Reporting program's Follow-Up After Hospitalization for Mental Illness measure for Medicare FFS discharges between July 1, 2015 and June 30, 2016.

Measure Specifications

• NQF Number (if applicable): 3495
  
  
• Description: This measure is a re-specified version of the measure, Risk-adjusted readmission rate (RARR) of unplanned readmission within 30 days of hospital discharge for any condition†(NQF 1789), which was developed for patients 65 years and older using Medicare claims. This re-specified measure attributes outcomes to MIPS participating clinician groups and assesses each group's readmission rate. The measure comprises a single summary score, derived from the results of five models, one for each of the following specialty cohorts (groups of discharge condition categories or procedure categories): medicine, surgery/gynecology, cardio-respiratory, cardiovascular, and neurology.
  
  
• Numerator: The outcome for this measure is unplanned all-cause 30-day readmission. Readmission is defined as a subsequent inpatient admission to any acute care facility which occurs within 30 days of the discharge date of an eligible index admission. Any readmission is eligible to be counted as an outcome, except those that are considered planned. To align with data years used, the planned readmission algorithm version 4.0 was used to classify readmissions as planned or unplanned.
  
  
• Denominator: Patients eligible for inclusion in the measure have an index admission hospitalization to which the readmission outcome is attributed and includes admissions for patients: Enrolled in Medicare Fee-For-Service (FFS) Part A for the 12 months prior to the date of admission; Aged 65 or over; Discharged alive from a non-federal short-term acute care hospital; and, Not transferred to another acute care facility.
  
  
• Exclusions: 1. Patients discharged against medical advice (AMA) are excluded.2. Admissions for patients to a PPS-exempt cancer hospital are excluded.3. Admissions primarily for medical treatment of cancer are excluded.4. Admissions primarily for psychiatric disease are excluded.5. Admissions for rehabilitation care; fitting of prostheses and adjustment devices (CCS 254) are excluded.6. Admissions where patient cannot be attributed to a clinician group.
  
  
• HHS NQS Priority: Communication and Care Coordination
  
  
• HHS Data Source: Administrative clinical data
  
  
• Measure Type: Outcome
  
  
• Steward: Centers for Medicare & Medicaid Services
  
  
• Endorsement Status:
  
  
• Meaningful Measure Area: Admissions and readmissions to hospitals
  
  
• Is the measure specified as an electronic clinical quality measure? No
  
  

Summary of Workgroup Deliberations

• Workgroup Recommendation: Conditional Support for Rulemaking
  
  
• Workgroup Rationale: MAP conditionally supported MUC2019-27 for rulemaking. Support for this measure is pending removal and replacement of NQF 1789 in the MIPS program measure set with this measure, and NQF CDP Standing Committee review of reliability performance at the physician group level in spring 2020. MAP noted that this measure is a respecified version of the measure Risk-adjusted readmission rate (RARR) of unplanned readmission within 30 days of hospital discharge for any conditions (NQF 1789), which was developed for patients 65 years of age and older using Medicare claims. MAP emphasized the importance of addressing unplanned readmissions and noted that physician groups can influence this outcome by supporting appropriate medication reconciliation at discharge, reducing infection risk, and ensuring proper outpatient follow-up. MAP suggested that this measure promotes a systems level approach by clinicians and suggested a future focus on especially high-risk conditions such as COPD and heart failure. MAP noted that the NQF All-Cause Admissions and Readmissions Standing Committee had requested additional information from the developer on reliability performance of this measure at various case sizes for the physician group level of analysis in the course of the consensus development process (CDP).
  
  
• Public comments received: 17

Rationale for measure provided by HHS
HHS has provided additional documentation to support this measure under consideration. Hospital readmission, for any reason, is disruptive to patients and caregivers, costly to the healthcare system, and puts patients at additional risk of hospital-acquired infections and complications. Readmissions are also a major source of patient and family stress and may contribute substantially to loss of functional ability, particularly in older patients. Some readmissions are unavoidable and result from inevitable progression of disease or worsening of chronic conditions. However, readmissions may also result from poor quality of care or inadequate transitional or post-discharge care. Transitional care includes effective discharge planning, transfer of information at the time of discharge, patient assessment and education, and coordination of care and monitoring in the post-discharge period. Numerous studies have found an association between quality of inpatient or transitional care and early (typically 30-day) readmission rates for a wide range of conditions.1-8Randomized controlled trials have shown that improvement in the following areas can directly reduce readmission rates: quality of care during the initial admission; improvement in communication with patients, their caregivers, and their clinicians; patient education; pre-discharge assessment; and coordination of care after discharge.9-17 Successful randomized trials have reduced 30-day readmission rates by 20-40%.18 Widespread application of these clinical trial interventions to general practice has also been encouraging. Since 2008, Medicare Quality Improvement Organizations have been funded to focus on care transitions by applying lessons learned from clinical trials. Several have been notably successful in reducing readmissions within 30 days.19 Many of these study interventions involved enhanced clinician involvement and indicate a key role for clinicians in reducing readmissions. Further, analyses CORE performed pre-development of this measure support variation in clinician- and clinician group-level performance on 30-day readmissions for patients with acute myocardial infraction.Despite these demonstrated successful interventions, the overall national readmission rate remains high, with a 30-day readmission following over 15% of discharges. Readmission rates also vary widely across institutions.20-22 Moreover, we show below that RARRs vary from 7%-25% for clinician groups for 2015-16. Both the high baseline rate and the variability across eligible clinician groups speak to the need for a quality measure to prompt greater care improvement. Given that studies have shown readmissions within 30 days to be related to quality of care, that interventions, including those utilizing clinicians, have been able to reduce 30-day readmission rates for a variety of specific conditions, and that high and variable clinician-level readmission rates indicate opportunity for improvement, we sought to develop eligible clinician group-level measure of all-cause, all-condition 30-day unplanned readmission.1. Frankl SE, Breeling JL, L. G. Preventability of emergent hospital readmission American Journal of Medicine. Jun 1991;90(6):667-674.2. Corrigan J, Martin J. Identification of factors associated with hospital readmission and development of a predictive model. Health Services Research. Apr 1992;27(1):81-101.3. Oddone E, Weinberger M, Horner M, et al. . Classifying general medicine readmissions. Are they preventable? Veterans Affairs Cooperative Studies in Health Services Group on Primary Care and Hospital Readmissions. Journal of General Internal Medicine. Oct 1996;11(10):597-607.4. Ashton C, Del Junco DJ, Souchek J, Wray N, Mansyur C. The association between the quality of inpatient care and early readmission: a meta-analysis of the evidence. . Med Care. Oct 1997;35(10):1044-1059.5. Benbassat J, Taragin M. Hospital readmissions as a measure of quality of health care: advantages and limitations. Archives of Internal Medicine. Apr 24 2000;160(8):1074-1081.6. Courtney EDJ, Ankrett S, McCollum PT. 28-Day emergency surgical re-admission rates as a clinical indicator of performance. Annals of the Royal College of Surgeons of England. Mar 2003;85(2):75-78.7. Halfon P, Eggli Y, Pr, et al. . Validation of the potentially avoidable hospital readmission rate as a routine indicator of the quality of hospital care. Medical Care. Nov 2006;44(11):972-981.8. Hernandez AF, Greiner MA, Fonarow GC, et al. . Relationship between early physician follow-up and 30-day readmission among Medicare beneficiaries hospitalized for heart failure. JAMA. May 5 2010;303(17):1716-1722.9. Naylor M, Brooten D, Jones R, Lavizzo-Mourey R, Mezey M, Pauly M. Comprehensive discharge planning for the hospitalized elderly. A randomized clinical trial. Ann Intern Med. Jun 15 1994;120(12):999-1006.10. Naylor MD, Brooten D, Campbell R, et al. Comprehensive discharge planning and home follow-up of hospitalized elders: a randomized clinical trial. JAMA. 1999;281(7):613-620.11.Krumholz HM, Amatruda J, Smith GL, et al. Randomized trial of an education and support intervention to prevent readmission of patients with heart failure. Journal of the American College of Cardiology. . Jan 2 2002;39(1):83-89.12. van Walraven C, Seth R, Austin PC, Laupacis A. Effect of discharge summary availability during post-discharge visits on hospital readmission. Journal of General Internal Medicine. Mar 2002;;17(3):186-192.13. Conley RR, Kelly DL, Love RC, McMahon RP. Rehospitalization risk with second-generation and depot antipsychotics. . Annals of Clinical Psychiatry. Mar 2003;15(1):23-31.14. Coleman EA, Smith JD, Frank JC, Min S-J, Parry C, Kramer AM. Preparing patients and caregivers to participate in care delivered across settings: the Care Transitions Intervention. Journal of the American Geriatrics Society. Nov 2004;52(11):1817-1825.15. Phillips CO, Wright SM, Kern DE, Singa RM, Shepperd S, Rubin HR. Comprehensive discharge planning with postdischarge support for older patients with congestive heart failure: a meta-analysis. JAMA. Mar 17 2004;291(11):1358-1367.16. Jovicic A, Holroyd-Leduc JM, Straus SE. Effects of self-management intervention on health outcomes of patients with heart failure: a systematic review of randomized controlled trials. BMC Cardiovasc Disord. 2006;6:43.17. Garasen H, Windspoll R, Johnsen R. Intermediate care at a community hospital as an alternative to prolonged general hospital care for elderly patients: a randomised controlled trial. BMC Public Health. 2007;7:69.18. Leppin AL, Gionfriddo MR, Kessler M, et al. Preventing 30-day hospital readmissions: a systematic review and meta-analysis of randomized trials. JAMA Intern Med. 2014;174(7):1095-1107.19. CFMC. CFfMC. Care Transitions QIOSC. 2010; http://www.cfmc.org/caretransitions/.20. Keenan PS, Normand SL, Lin Z, et al. An administrative claims measure suitable for profiling hospital performance on the basis of 30-day all-cause readmission rates among patients with heart failure. Circ Cardiovasc Qual Outcomes. 2008;1(1):29-37.21. Krumholz HM, Lin Z, Drye EE, et al. An administrative claims measure suitable for profiling hospital performance based on 30-day all-cause readmission rates among patients with acute myocardial infarction. Circulation. Mar 1 2011;4(2):243-252.22. Lindenauer PK, Normand SL, Drye EE, et al. Development, validation, and results of a measure of 30-day readmission following hospitalization for pneumonia. J Hosp Med. 2011;6(3):142-150.

Summary of NQF Endorsement Review

• Year of Most Recent Endorsement Review: 2019
  
  
• Project for Most Recent Endorsement Review: Admissions and Readmissions
  
  
• Review for Importance: 1a. Evidence: Y-14; N-2; 1b. Performance Gap: H-0; M-15; L-1; I-0Rationale:•This is a re-specified version of the hospital-level measure, “Hospital-Wide All-Cause, Unplanned Readmission Measure” (NQF 1789). NQF 1789 was developed for patients who are 65 years or older, are enrolled in fee-for-service (FFS) Medicare, and are hospitalized in non-federal hospitals. This specified measure attributes admissions to up to three participating MIPS eligible clinicians.•The Standing Committee reviewed the logic model presented by the developer demonstrating physician group interventions that can reduce the risk of unplanned hospital visits.•The Standing Committee reviewed the range of performance for clinician groups from 13.1 in the first decile to 18.0 in the tenth decile.
  
  
• Review for Scientific Acceptability: 2a. Reliability: H-0; M-13; L-2; I-1; 2b. Validity: H-0; M-10; L-6; I-0Rationale:•The Standing Committee reviewed the input provided by the NQF Scientific Methods Panel (SMP).•The Standing Committee generally agreed with the input from the SMP that the reliability testing methodology was appropriate and that results demonstrated moderate reliability. The Standing Committee noted SMP concerns that social risk factors are excluded from the risk model given the effect size and the potential for negative consequences on access to care if this measure is not adequately risk adjusted. The Standing Committee agreed that the developer should examine other clinical variables that could underlie disparities such as frailty or functional status.•The Standing Committee reviewed the measure score empirical validity testing and face validity testing submitted at the individual physician and physician group level. The Standing Committee noted that the evidence and validity testing must be evaluated separately for the two levels of analysis. The Committee agreed to vote individually on the two levels of analysis.•The Standing Committee noted that eligible clinician groups’ risk adjusted readmission rates go down with increasing Overall Hospital Quality Star Rating and with increasing quintile of the Star Rating readmission quality score.•The Committee reviewed the Face Validity testing and results, the approach to risk adjustment and the conceptual model for socio-demographic risk adjustment.•The Standing Committee had a mixed review of the face validity of the measure, specifically, the role physician groups have in improving this outcome.
  
  
• Review for Feasibility: 3. Feasibility: H-6; M-9; L-1; I-0(3a. Clinical data generated during care delivery; 3b. Electronic sources; 3c. Susceptibility to inaccuracies/ unintended consequences identified; 3d. Data collection strategy can be implemented)Rationale:•The Standing Committee agreed that the measure uses claims data that can be operationalized; however, the measure is not yet in use. There are no fees, licensing, or requirements to use the measure.
  
  
• Review for Usability: 4a. Use: Pass-14; No Pass-2; 4b. Usability: H-1; M-11; L-4; I-0Rationale:•The Standing Committee acknowledged that this measure is planned for use in the CMS MIPS program.•The Standing Committee noted that this is a new measure and there is no information available on performance improvement. This measure is not currently used in a program, but a primary goal of the measure is to provide information necessary to implement focused quality improvement efforts. Once the measure is implemented, the developer plans to examine trends in improvements by comparing RSRR over time.
  
  
• Review for Related and Competing Measures: No related or competing measures noted.
  
  
• Endorsement Committee Recommendation: Standing Committee Recommendation for Endorsement: Yes-11; No-5Rationale•The Committee noted that the measure passed each of the criteria and is suitable for continued endorsement.
  
  

Measure Specifications

• NQF Number (if applicable): 3493
  
  
• Description: This measure is a re-specified version of the measure, Hospital-level Risk-standardized Complication rate (RSCR) following Elective Primary Total Hip Arthroplasty (THA) and/or Total Knee Arthroplasty (TKA) (National Quality Forum 1550), which was developed for patients 65 years and older using Medicare claims. This re-specified measure attributes outcomes to Merit-based Incentive Payment System participating clinicians and/or clinician groups (provider) and assesses each provider's complication rate, defined as any one of the specified complications occurring from the date of index admission to up to 90 days post date of the index procedure.
  
  
• Numerator: The outcome for this measure is complication defined as acute myocardial infarction (AMI), pneumonia, and sepsis/septicemia/shock complications within seven days from the index admission date; death, surgical site bleeding, and pulmonary embolism within 30 days from the index admission; mechanical complications and periprosthetic joint infection/wound infection within 90 days of the index admission. The complication outcome is a dichotomous (yes/no) outcome. If a patient experiences one or more of these complications in the applicable time period, the complication outcome for that patient is counted in the measure as a "yes".
  
  
• Denominator: Patients eligible for inclusion in the measure are those age 65 years and older admitted to non-federal acute care hospitals. An index admission is the hospitalization during which an elective Total Hip Arthroplasty (THA) and/or Total Knee Arthroplasty (TKA) procedure was performed and to which the complication outcome is attributed. Eligible index admissions are identified using International Classification of Diseases-Tenth Revision-Procedure Coding System (ICD-10-PCS) procedure codes in Medicare inpatient claims data. For risk adjustment and outcome assessment, patients must have continuous enrollment in Medicare fee-for-service (FFS) for 12 months prior to the procedure and 90 days after it. The measure cohort is fully harmonized with the existing hospital-level measure.
  
  
• Exclusions: This measure excludes from the denominator admissions for patients:1. With a femur, hip or pelvic fracture coded in the principal discharge diagnosis field for the index admission.2. Undergoing partial hip arthroplasty (PHA) procedures (with a concurrent Total Hip Arthroplasty or Total Knee Arthroplasty [THA/TKA]).3. Undergoing revision procedures (with a concurrent THA/TKA).4. Undergoing resurfacing procedures (with a concurrent THA/TKA). 5. With a mechanical complication coded in the principal discharge diagnosis field for the index admission.6. With a malignant neoplasm of the pelvis, sacrum, coccyx, lower limbs, or bone/bone marrow or a disseminated malignant neoplasm coded in the principal discharge diagnosis field for the index admission.7. With a procedure code for removal of implanted devices/prostheses.After excluding the above admissions to identify elective primary THA/TKA procedures, the measure also excludes admissions for patients:8. Who were transferred to the index hospital.9. Who leave the hospital against medical advice (AMA).10. With more than two THA/TKA procedure codes during the index hospitalization.Note: The measure does not count complications that occur in the outpatient setting and do not require a readmission.
  
  
• HHS NQS Priority: Communication and Care Coordination
  
  
• HHS Data Source: Claims
  
  
• Measure Type: Outcome
  
  
• Steward: Centers for Medicare & Medicaid Services
  
  
• Endorsement Status:
  
  
• Meaningful Measure Area: Preventable healthcare harm
  
  
• Is the measure specified as an electronic clinical quality measure? No
  
  

Summary of Workgroup Deliberations

• Workgroup Recommendation: Support for Rulemaking
  
  
• Workgroup Rationale: MAP supported MUC2019-28 for rulemaking. MAP noted that this measure can improve the quality of surgical care delivery and follow-up care for a common and costly surgical procedure performed for Medicare beneficiaries. MAP agreed that patient-reported outcomes performance measures related to TKA and THA would also be desirable but would be complementary to this measure. MUC2019-18 is endorsed as NQF 3493 and is a respecified version of Hospital-level Risk-standardized Complication rate (RSCR) following Elective Primary Total Hip Arthroplasty (THA) and/or Total Knee Arthroplasty (TKA) (NQF 1550), which was developed for patients 65 years and older using Medicare claims. MAP noted that NQF 1550 is currently being used in the CMS Hospital Inpatient Quality Reporting Program, though it underwent substantial respecification to allow for clinician and clinician group attribution. In adapting the hospital-level measure for MIPS-eligible clinicians, the same cohort of patients will be measured, but the outcomes will be attributed to a larger number of healthcare entities with a shared responsibility for delivery of high quality postsurgical care. The MAP Rural Health Workgroup noted that this measure will be limited to clinicians/clinician groups with at least 25 patients, and as such, the low case-volume issue will not come into play for rural providers. However, access to supportive services prior to surgery will be even more critical when these procedures are done in the outpatient setting, and access to such services may be more limited in rural areas.
  
  
• Public comments received: 8

Rationale for measure provided by HHS
HHS has provided additional documentation to support this measure under consideration. There is evidence that over time, hospital Total Hip Arthroplasty and/or Total Knee Arthroplasty (THA/TKA) volumes have increased, while hospital THA/TKA risk-standardized complication rates (RSCRs) have decreased. This evidence supports the fact that improving complication rates is possible and feasible. There is evidence that specific practices can reduce the chances of complications [1-2]. By attributing the outcome to clinicians who care for inpatient THA/TKA patients, the Merit Based Incentive Payment System (MIPS) THA/TKA complication measure will incentivize those clinicians to promote practices known to reduce post-operative complications and identify new interventions at the clinician level that may also do so. Studies have demonstrated that hospital-based interventions targeting critical aspects of care can reduce the risk of complications such as strategies to reduce blood loss, reduce length of stay, and routine wound care [3-4]. References:1.Kocher MS, Frank JS, Nasreddine AY, et al. Intra-abdominal fluid extravasation during hip arthroscopy: a survey of the MAHORN group. Arthroscopy : the journal of arthroscopic & related surgery : official publication of the Arthroscopy Association of North America and the International Arthroscopy Association. 2012;28(11):1654-1660.e1652.2.Ponzio DY, Poultsides LA, Salvatore A, Lee YY, Memtsoudis SG, Alexiades MM. In-Hospital Morbidity and Postoperative Revisions After Direct Anterior vs Posterior Total Hip Arthroplasty. J Arthroplasty. 2017.3.Chen AF, Heyl AE, Xu PZ, Rao N, Klatt BA. Preoperative Decolonization Effective at Reducing Staphylococcal Colonization in Total Joint Arthroplasty Patients. The Journal of Arthroplasty. 2013;28(8, Supplement):18-20.4.Rao N, Cannella BA, Crossett LS, Yates AJ, McGough RL, Hamilton CW. Preoperative Screening/Decolonization for Staphylococcus aureus to Prevent Orthopedic Surgical Site Infection: Prospective Cohort Study With 2-Year Follow-Up. The Journal of Arthroplasty. 2011;26(8):1501-1507.

Summary of NQF Endorsement Review

• Year of Most Recent Endorsement Review: 2019
  
  
• Project for Most Recent Endorsement Review: Surgery
  
  
• Review for Importance: 1a. Evidence: Pass-17; No Pass-0; 1b. Performance Gap: H-2; M-15; L-0; I-0Rationale:•Because this is a re-specified measure (1550), the Committee questioned whether the distinctions between 1550 and 3493 were great enough to justify having both hospital and provider versions. The Committee discussed that complication rates can be brought down by standardization of process and agreed that there was great value to having provider-level outcomes data available.•The Committee agreed that there is significant variation in complication rates, as evidenced by the hospital measure (1550), which demonstrates first and tenth decile rates of 1.9% and 4.3%). Additionally, for the clinician level, the risk-standardized measure scores had a mean (SD) of 2.83% (0.65%) and for the clinician group level, the risk-standardized measure scores had a mean (SD) of 2.81% (0.51%), demonstrating a performance gap that the Committee deemed moderate.
  
  
• Review for Scientific Acceptability: 2a. Reliability: H-2; M-14; L-1; I-0; 2b. Validity: H-2; M-13; L-2; I-0Rationale:•This measure was reviewed by the Scientific Methods Panel (SMP) and passed both reliability and validity.•Reliability testing was conducted at the measure score level and used Adams method to estimate entity-level reliability. The entity-level reliability testing indicated that for entities with 25 procedures or more, the median signal-to-noise ratio reliability was 0.793 [IQR 0.695-0.878] for clinicians, and 0.790 [IQR 0.647-0.907] for clinician groups. The median reliability scores reflected the reliability of the hospital-level measure score (NQF 1550).•Validity testing was demonstrated through empirical validity testing and by systematic assessment of the measure’s face validity by a technical expert panel (TEP) of national experts and stakeholder organizations. For empirical validity testing, the developer examined the relationship between volume and the measure score for clinicians and clinician groups. Correlations between volume and measure score were calculated for each provider type, and the measure score for each decile of volume was summarized. There was a moderate, yet meaningful, inverse relationship between volume and measure outcome for both clinicians (correlation coefficient of -0.2379; p<0.0001) and clinician groups (correlation coefficient of -0.19026; p<0.0001). Furthermore, the TEP supported the final measure with high agreement.•The Committee addressed a comment made by the American Medical Association (AMA) that questioned whether the case minimum of 25 cases was acceptable, given the low reliability results (0.582 to 0.988 and 0.463 to 0.996 for clinicians and clinician groups, respectively). The developer reported that the 25 cases provided was acceptable reliability while capturing lower volume providers.•The Committee noted that the database is an administrative database for CMS that is based on submitted diagnosis codes for billing, which is less valid than registry data.•The Committee discussed a comment made by the AMA that stated additional testing is needed to evaluate clinical factors in conjunction with social risk factors, as opposed to prioritizing clinical factors. The Committee agreed that including volume as a risk adjuster would not identify important modifiable risk factors that the measure should identify.•The Committee raised questions regarding the inclusion criteria for the measure: continuous 12-month enrollment in Medicare Part A. The developer clarified that this criterion is in place to ensure that all co-morbidities are captured adequately for risk adjustment, as well as for the duration that they are evaluating for complications. Additionally, it was pointed out that this measure is specified for fee-for-service (FFS) beneficiaries only and therefore does not capture Medicare Advantage patients.•It was recommended that it would be worth researching whether there is a substantial difference between FFS beneficiaries and those enrolled in Medicare Advantage. Since Medicare Advantage is more cost-effective for parts of the country, leaving out this population may blunt an important socioeconomic risk adjustment.
  
  
• Review for Feasibility: 3. Feasibility: H-6; M-11; L-0; I-0(3a. Clinical data generated during care delivery; 3b. Electronic sources; 3c. Susceptibility to inaccuracies/ unintended consequences identified 3d. Data collection strategy can be implemented)Rationale:•The data elements can be found in defined fields in electronic claims, and administrative data are routinely collected as part of the billing process. The measure was designed to capture data that are already present in administrative data collection. There are no fees, licensing, or other requirements reported to use any aspect of the measure.•NQF measure 1550 was deemed feasible when it was originally evaluated and endorsed. The Committee agreed that feasibility is moderate to high for this measure.
  
  
• Review for Usability: 4. Use and Usability4a. Use; 4a1. Accountability and transparency; 4a2. Feedback on the measure by those being measured and others; 4b. Usability; 4b1. Improvement; 4b2. The benefits to patients outweigh evidence of unintended negative consequences to patients)4a. Use: Pass-17; No Pass-0 4b. Usability: H-3; M-13; L-1; I-0Rationale:•Since this is a new measure, there are currently no public reporting targets. However, per the developer, the primary goal of the measure is to provide information necessary to implement focused quality improvement efforts.•The Committee discussed that expanding this measure to all-payer or to a broader population would have great usability.
  
  
• Review for Related and Competing Measures: This measure is harmonized with measure 1550 regarding cohort definition, outcome, and risk adjustment approach. The only discrepancy is the attribution approach, but instead of assigning each index admission to a hospital (1550), it assigns it to a clinician or a clinician group.
  
  
• Endorsement Committee Recommendation: Standing Committee Recommendation for Endorsement: Y-17; N-0
  
  

Measure Specifications

• NQF Number (if applicable): 0
  
  
• Description: Annual risk-standardized rate of acute, unplanned hospital admissions among Medicare Fee-for-Service (FFS) patients aged 65 years and older with multiple chronic conditions (MCCs).
  
  
• Numerator: The outcome for this measure is the number of acute, unplanned hospital admissions per 100 person-years at risk for admission during the measurement period. Time PeriodThe outcome includes inpatient admissions to an acute care hospital during the measurement year. Excluded AdmissionsThis measure does not include the following types of admissions in the outcome because they do not reflect the quality of care provided by ambulatory care clinicians who are managing the care of MCC patients: 1. Planned hospital admissions.2. Admissions that occur directly from a skilled nursing facility (SNF) or acute rehabilitation facility.3. Admissions that occur within a 10-day “buffer period†of time after discharge from a hospital, SNF, or acute rehabilitation facility.4. Admissions that occur after the patient has entered hospice.5. Admissions related to complications from procedures or surgeries.6. Admissions related to accidents or injuries.7. Admissions that occur prior to the first visit with the assigned clinician.To identify planned admissions, the measure adopted an algorithm CORE previously developed for CMS's hospital readmission measures, CMS's Planned Readmission Algorithm Version 4.0. [1,2] In brief, the algorithm uses the procedure codes and principal discharge diagnosis code on each hospital claim to identify planned admissions. A few specific, limited types of care are always considered planned (for example, major organ transplant, rehabilitation, and maintenance chemotherapy). Otherwise, a planned admission is defined as a non-acute admission for a scheduled procedure (for example, total hip replacement or cholecystectomy). Admissions for an acute illness are never considered planned. To identify complications of procedures or surgeries, we use the Agency for Healthcare Research and Quality's (AHRQ's) Clinical Classifications Software (CCS), which clusters diagnoses into clinically meaningful categories using International Classification of Diseases, Ninth Revision, Clinical Modification (ICD-9-CM) or International Classification of Diseases, Tenth Revision, and Clinical Modification (ICD-10-CM) codes. We exclude the following 23 CCS categories. 1. 145: Intestinal obstruction without hernia2. 237: Complication of device; implant or graft3. 238: Complications of surgical procedures or medical care4. 257: Other aftercareb) Accidents or injuries 5. 2601 E Codes: Cut/pierce6. 2602 E Codes: Drowning/submersion7. 2604 E Codes: Fire/burn8. 2605 E Codes: Firearm9. 2606 E Codes: Machinery10. 2607 E Codes: Motor vehicle traffic (MVT)11. 2608 E Codes: Pedal cyclist; not MVT12. 2609 E Codes: Pedestrian; not MVT13. 2610 E Codes: Transport; not MVT14. 2611 E Codes: Natural/environment15. 2612 E Codes: Overexertion16. 2613 E Codes: Poisoning17. 2614 E Codes: Struck by; against18. 2615 E Codes: Suffocation19. 2616 E Codes: Adverse effects of medical care20. 2618 E Codes: Other specified and classifiable21. 2619 E Codes: Other specified; NEC22. 2620 E Codes: Unspecified23. 2621 E Codes: Place of occurrencePerson-time at riskPersons are considered at risk for hospital admission if they are alive, enrolled in Medicare FFS, and not in the hospital during the measurement period. In addition to time spent in the hospital, we also exclude from at-risk time: 1) time spent in a SNF or acute rehabilitation facility; 2) the time within 10 days following discharge from a hospital, SNF, or acute rehabilitation facility; and 3) time after entering hospice care.Citations1. Yale New Haven Health Services Corporation – Center for Outcomes Research & Evaluation (YNHHSC/CORE). 2018 All-Cause Hospital Wide Measure Updates and Specifications Report - Hospital-Level 30-Day Risk-Standardized Readmission Measure – Version 7.0. Centers for Medicare & Medicaid Services; March 2018.2. Horwitz L, Grady J, Cohen D, et al. Development and validation of an algorithm to identify planned readmissions from claims data. Journal of Hospital Medicine. October 2015;10(10):670-677.
  
  
• Denominator: Patients included in the measure (target patient population)The cohort is comprised of patients whose combinations of chronic conditions put them at high risk of admission and whose admission rates could be lowered through better care. This definition reflects NQF's Multiple Chronic Conditions Measurement Framework, which defines patients with MCCs as people having two or more concurrent chronic conditions that. . .act together to significantly increase the complexity of management, and affect functional roles and health outcomes, compromise life expectancy, or hinder self-management. [1] The specific inclusion criteria are as follows.Patient is alive at the start of the measurement period and has two or more of nine chronic disease groups in the year prior to the measurement period. Chronic conditions, except for diabetes, are defined using CMS's Chronic Conditions Data Warehouse (CCW). For diabetes, we used the diabetes cohort definition from the Accountable Care Organization (ACO) diabetes admission measure developed by CORE (v2018a ACO-36) as opposed to the definition used in CCW; CCW includes diagnoses for secondary and drug-induced diabetic conditions that are not the focus of the MIPS MCC admission measure. 1. Acute myocardial infarction (AMI),2. Alzheimer's disease and related disorders or senile dementia,3. Atrial fibrillation,4. Chronic kidney disease (CKD),5. Chronic obstructive pulmonary disease (COPD) or asthma,6. Depression,7. Diabetes,8. Heart failure, and9. Stroke or transient ischemic attack (TIA).Patient is aged >=65 years at the start of the year prior to the measurement period.Patient is a Medicare FFS beneficiary with continuous enrollment in Medicare Parts A and B during the year prior to the measurement period.Provider types included for measurement Primary care providers (PCPs): CMS designates PCPs as physicians who practice internal medicine, family medicine, general medicine, or geriatric medicine, and non-physician providers, including nurse practitioners, certified clinical nurse specialists, and physician assistants.Relevant specialists: Specialists covered by the measure are limited to those who provide overall coordination of care for patients with MCCs and who manage the chronic diseases that put the MCC patients in the measure at risk of admission. These specialists were chosen with input from our Technical Expert Panel (TEP) and include cardiologists, pulmonologists, nephrologists, neurologists, endocrinologists, and hematologists/oncologists.Outcome attributionWe begin by assigning each patient to the clinician most responsible for the patient’s care, based on the pattern of outpatient visits with PCPs and relevant specialists. The patient can be assigned to a PCP, a relevant specialist, or can be left unassigned. A patient who is eligible for attribution can be assigned to a relevant specialist only if the specialist has been identified as "dominant". A specialist is considered "dominant" if they have two or more visits with the patient, as well as at least two more visits than any primary care provider or other relevant specialist.There are two scenarios where a patient can be assigned to a PCP. First, the patient must have seen at least one PCP. The patient will then be assigned to the PCP with the highest number of visits if there are no relevant specialists who are considered “dominantâ€. Second, if the patient has had more than one visit with a relevant specialist, no "dominant" specialist has been identified, and has two or more visits with a PCP, they will be assigned to that PCP.Finally, the patient will be unassigned if they only saw non-relevant specialists, if the patient has not seen a PCP and no "dominant" specialist can be identified, or if the patient has not had more than one visit with any individual PCP.Patients are then assigned at the Taxpayer Identification Number (TIN) level, which includes solo clinicians and groups of clinicians who have chosen to report their quality under a common TIN. Patients "follow" their clinician to the TIN designated by the clinician (i.e. they are assigned to their clinician's TIN). Patients unassigned at the individual clinician-level, therefore, continue to be unassigned at the TIN level.Citations1. National Quality Forum. Multiple Chronic Conditions Measurement Framework. http://www.qualityforum.org/WorkArea/linkit.aspx?LinkIdentifier=id&ItemID=71227. Accessed February 20, 2019.
  
  
• Exclusions: The cohort excludes the following patients:1) Patients without continuous enrollment in Medicare Part A or B during the measurement period.2) Patients who were in hospice at any time during the year prior to the measurement year or at the start of the measurement year.3) Patients who had no Evaluation & Management (E&M) visits to a MIPS-eligible clinician.
  
  
• HHS NQS Priority: Communication and Care Coordination
  
  
• HHS Data Source: Claims
  
  
• Measure Type: Outcome
  
  
• Steward: Centers for Medicare & Medicaid Services
  
  
• Endorsement Status:
  
  
• Meaningful Measure Area: Management of chronic conditions
  
  
• Is the measure specified as an electronic clinical quality measure? No
  
  

Summary of Workgroup Deliberations

• Workgroup Recommendation: Conditional Support for Rulemaking
  
  
• Workgroup Rationale: MAP conditionally supported MUC2019-37 MIPS Clinician and Clinician Group Risk-standardized Hospital Admission Rates for Patients with Multiple Chronic Conditions pending NQF endorsement. MAP noted that with over 80 percent of adults over the age of 65 having MCCs, this measure has the potential to significantly improve the quality of care for the Medicare beneficiary population. MAP also noted that this measure carries a higher reliability score than the measure considered for MIPS, and MAP considered it still appropriate for the SSP program. MAP noted that ACOs in SSP focus on processes and interventions that reduce disease progression and undesirable sequalae that lead to hospital admission for Medicare patients with MCCs. Moreover, the accountability structure of an ACO allows for stronger oversight and care coordination to influence measure performance within the ACO system.MAP identified several measure gaps within SSP: diagnostic efficiency, measures of cultural change, and additional measures of care coordination and hand-offs using eCQMs.
  
  
• Public comments received: 9

Rationale for measure provided by HHS
Hospital admission rates are an effective marker of ambulatory care quality. Hospital admissions from the outpatient setting reflect a deterioration in patients’ clinical status and as such reflect an outcome that is meaningful to both patients and providers. Patients receiving optimal, coordinated high-quality care should use fewer inpatient services than patients receiving fragmented, low-quality care. Thus, high population rates of hospitalization may, at least to some extent, signal poor quality of care or inefficiency in health system performance.Patients with MCCs are at high risk for hospital admission, often for potentially preventable causes, such as exacerbation of pulmonary disease. [1] Evidence from several Medicare demonstration projects suggests that care coordination results in decreased hospital admission rates among high-risk patients. [2] In addition, studies have shown that the types of ambulatory care clinicians this measure targets (for example, primary care providers and specialists caring for patients with MCCs) can influence admission rates through primary care clinician supply, continuity of care, and patient-centered medical home interventions such as team-based and patient-oriented care. [3-5]Given evidence that ambulatory care clinicians can influence hospital admission rates through optimal care and coordination, this measure will incentivize quality improvement efforts leading to improved patient outcomes.Citations:1. Abernathy K, Zhang J, Mauldin P, et al. Acute Care Utilization in Patients With Concurrent Mental Health and Complex Chronic Medical Conditions. Journal of primary care & community health. 2016;7(4):226-233. 2. Brown RS, Peikes D, Peterson G, Schore J, Razafindrakoto CM. Six features of Medicare coordinated care demonstration programs that cut hospital admissions of high-risk patients. Health Aff (Millwood). 2012;31(6):1156-1166. 3. van Loenen T, van den Berg MJ, Westert GP, Faber MJ. Organizational aspects of primary care related to avoidable hospitalization: a systematic review. Fam Pract. 2014;31(5):502-516.4. Dale SB, Ghosh A, Peikes DN, et al. Two-Year Costs and Quality in the Comprehensive Primary Care Initiative. N Engl J Med. 2016;374(24):2345-2356.5. Casalino LP, Pesko MF, Ryan AM, et al. Small primary care physician practices have low rates of preventable hospital admissions. Health Aff (Millwood). 2014;33(9):1680-1688.

Measure Specifications

• NQF Number (if applicable): 0
  
  
• Description: Percentage of adult hemodialysis patient-months using a catheter continuously for three months or longer for vascular access attributable to an individual practitioner or group practice.
  
  
• Numerator: The numerator is the number of adult patient-months in the denominator who were on maintenance hemodialysis using a catheter continuously for three months or longer as of the last hemodialysis session of the reporting month.
  
  
• Denominator: All patients at least 18 years old as of the first day of the reporting month who are determined to be maintenance hemodialysis patients (in-center and home HD) for the complete reporting month under the care of the same practitioner or group partner.
  
  
• Exclusions: Exclusions that are implicit in the denominator definition include:Pediatric patients (<18 years old);Patients on Peritoneal Dialysis for any portion of the reporting month; Patient-months where there are more than one MCP provider listed for the month. In addition, patients with a catheter that have limited life expectancy, as defined by the following criteria are excluded: Patients under hospice care in the current reporting month;Patients with metastatic cancer in the past 12 months;Patients with end stage liver disease in the past 12 months;Patients with coma or anoxic brain injury in the past 12 months
  
  
• HHS NQS Priority: Communication and Care Coordination
  
  
• HHS Data Source: Claims;CROWNWeb
  
  
• Measure Type: Intermediate Outcome
  
  
• Steward: Centers for Medicare & Medicaid Services
  
  
• Endorsement Status:
  
  
• Meaningful Measure Area: Management of chronic conditions
  
  
• Is the measure specified as an electronic clinical quality measure? No
  
  

Summary of Workgroup Deliberations

• Workgroup Recommendation: Conditional Support for Rulemaking
  
  
• Workgroup Rationale: MAP conditionally supported MUC2019-66 for rulemaking. As the measure has not been reviewed by an NQF CDP Standing Committee to determine the strength of the measure’s reliability and validity, MAP’s support is conditional upon NQF endorsement. MAP noted that the use of a long-term catheter has a higher observed mortality rate than the use of arteriovenous fistula, thus this measure has the potential to provide greater quality of care for patients by reducing the associated mortality and morbidity from long-term catheter use. MAP noted that a modified version of this measure is currently being used in a CMS quality program—the End-Stage Renal Disease Quality Improvement Program (ESRD QIP). The measure is undergoing changes to allow specification for individual clinicians and clinician groups. While MAP questioned the ability of providers to move patients from catheters to fistulas, the measure developer noted that clinicians can influence this as evidenced by rate improvements after implementation of this measure in ESRD QIP. MAP expressed concern on the reliability of the measure and encouraged CMS to rigorously test the measure. The Rural Health Workgroup noted that kidney diseases are prevalent conditions in rural populations. They emphasized that rural patients on dialysis are older and have more comorbidities, and voiced concern that these patients might be pressed to use a fistula, even when there is little benefit.
  
  
• Public comments received: 7

Rationale for measure provided by HHS
HHS has provided additional documentation to support this measure under consideration. Several observational studies have demonstrated an association between type of vascular access used for hemodialysis and patient mortality. Long term catheter use is associated with the highest mortality risk while arteriovenous fistula use has the lowest mortality risk. Arteriovenous grafts (AVG) have been found to have a risk of death that is higher than AVF but lower than catheters. The measure focus is the process of assessing long term catheter use at chronic dialysis facilities.This process leads to improvement in mortality as follows:Measure long term catheter rate -> Assess value -> Identify patients who do not have an AV Fistula or AV graft->Evaluation for an AV fistula or graft by a qualified dialysis vascular access provider -> Increase Fistula/Graft Rate -> Lower catheter rate ->Lower patient mortality.

Measure Specifications

• NQF Number (if applicable): 0
  
  
• Description: Annual risk-standardized rate of acute, unplanned hospital admissions among Medicare Fee-for-Service (FFS) patients aged 65 years and older with multiple chronic conditions (MCCs).
  
  
• Numerator: The outcome for this measure is the number of acute, unplanned hospital admissions per 100 person-years at risk for admission during the measurement period. Time PeriodThe outcome includes inpatient admissions to an acute care hospital during the measurement year. Excluded AdmissionsThis measure does not include the following types of admissions in the outcome because they do not reflect the quality of care provided by ambulatory care clinicians who are managing the care of MCC patients: 1. Planned hospital admissions.2. Admissions that occur directly from a skilled nursing facility (SNF) or acute rehabilitation facility.3. Admissions that occur within a 10-day “buffer period†of time after discharge from a hospital, SNF, or acute rehabilitation facility.4. Admissions that occur after the patient has entered hospice.5. Admissions related to complications from procedures or surgeries.6. Admissions related to accidents or injuries.7. Admissions that occur prior to the first visit with the assigned clinician.To identify planned admissions, the measure adopted an algorithm CORE previously developed for CMS's hospital readmission measures, CMS's Planned Readmission Algorithm Version 4.0. [1,2] In brief, the algorithm uses the procedure codes and principal discharge diagnosis code on each hospital claim to identify planned admissions. A few specific, limited types of care are always considered planned (for example, major organ transplant, rehabilitation, and maintenance chemotherapy). Otherwise, a planned admission is defined as a non-acute admission for a scheduled procedure (for example, total hip replacement or cholecystectomy). Admissions for an acute illness are never considered planned. To identify complications of procedures or surgeries, we use the Agency for Healthcare Research and Quality's (AHRQ's) Clinical Classifications Software (CCS), which clusters diagnoses into clinically meaningful categories using International Classification of Diseases, Ninth Revision, Clinical Modification (ICD-9-CM) or International Classification of Diseases, Tenth Revision, and Clinical Modification (ICD-10-CM) codes. We exclude the following 23 CCS categories. 1. 145: Intestinal obstruction without hernia2. 237: Complication of device; implant or graft3. 238: Complications of surgical procedures or medical care4. 257: Other aftercareb) Accidents or injuries 5. 2601 E Codes: Cut/pierce6. 2602 E Codes: Drowning/submersion7. 2604 E Codes: Fire/burn8. 2605 E Codes: Firearm9. 2606 E Codes: Machinery10. 2607 E Codes: Motor vehicle traffic (MVT)11. 2608 E Codes: Pedal cyclist; not MVT12. 2609 E Codes: Pedestrian; not MVT13. 2610 E Codes: Transport; not MVT14. 2611 E Codes: Natural/environment15. 2612 E Codes: Overexertion16. 2613 E Codes: Poisoning17. 2614 E Codes: Struck by; against18. 2615 E Codes: Suffocation19. 2616 E Codes: Adverse effects of medical care20. 2618 E Codes: Other specified and classifiable21. 2619 E Codes: Other specified; NEC22. 2620 E Codes: Unspecified23. 2621 E Codes: Place of occurrencePerson-time at riskPersons are considered at risk for hospital admission if they are alive, enrolled in Medicare FFS, and not in the hospital during the measurement period. In addition to time spent in the hospital, we also exclude from at-risk time: 1) time spent in a SNF or acute rehabilitation facility; 2) the time within 10 days following discharge from a hospital, SNF, or acute rehabilitation facility; and 3) time after entering hospice care.Citations1. Yale New Haven Health Services Corporation – Center for Outcomes Research & Evaluation (YNHHSC/CORE). 2018 All-Cause Hospital Wide Measure Updates and Specifications Report - Hospital-Level 30-Day Risk-Standardized Readmission Measure – Version 7.0. Centers for Medicare & Medicaid Services; March 2018.2. Horwitz L, Grady J, Cohen D, et al. Development and validation of an algorithm to identify planned readmissions from claims data. Journal of Hospital Medicine. October 2015;10(10):670-677.
  
  
• Denominator: Patients included in the measure (target patient population)The cohort is comprised of patients whose combinations of chronic conditions put them at high risk of admission and whose admission rates could be lowered through better care. This definition reflects NQF's Multiple Chronic Conditions Measurement Framework,which defines patients with MCCs as people having two or more concurrent chronic conditions that. . .act together to significantly increase the complexity of management, and affect functional roles and health outcomes, compromise life expectancy, or hinder self-management. [1] The specific inclusion criteria are as follows.Patient is alive at the start of the measurement period and has two or more of nine chronic disease groups in the year prior to the measurement period. Chronic conditions, except for diabetes, are defined using CMS’s Chronic Conditions Data Warehouse (CCW). For diabetes, we used the diabetes cohort definition from the Accountable Care Organization (ACO) diabetes admission measure developed by CORE (v2018a ACO-36) as opposed to the definition used in CCW; CCW includes diagnoses for secondary and drug-induced diabetic conditions that are not the focus of the MIPS MCC admission measure. 1. Acute myocardial infarction (AMI),2. Alzheimer's disease and related disorders or senile dementia,3. Atrial fibrillation,4. Chronic kidney disease (CKD),5. Chronic obstructive pulmonary disease (COPD) or asthma,6. Depression,7. Diabetes,8. Heart failure, and9. Stroke or transient ischemic attack (TIA).Patient is aged >=65 years at the start of the year prior to the measurement period.Patient is a Medicare FFS beneficiary with continuous enrollment in Medicare Parts A and B during the year prior to the measurement period.Provider types included for measurement Primary care providers (PCPs): CMS designates PCPs as physicians who practice internal medicine, family medicine, general medicine, or geriatric medicine, and non-physician providers, including nurse practitioners, certified clinical nurse specialists, and physician assistants.Relevant specialists: Specialists covered by the measure are limited to those who provide overall coordination of care for patients with MCCs and who manage the chronic diseases that put the MCC patients in the measure at risk of admission. These specialists were chosen with input from our Technical Expert Panel (TEP) and include cardiologists, pulmonologists, nephrologists, neurologists, endocrinologists, and hematologists/oncologists.Outcome attributionWe begin by assigning each patient to the clinician most responsible for the patient’s care, based on the pattern of outpatient visits with PCPs and relevant specialists. The patient can be assigned to a PCP, a relevant specialist, or can be left unassigned. A patient who is eligible for attribution can be assigned to a relevant specialist only if the specialist has been identified as “dominantâ€. A specialist is considered “dominant†if they have two or more visits with the patient, as well as at least two more visits than any primary care provider or other relevant specialist.There are two scenarios where a patient can be assigned to a PCP. First, the patient must have seen at least one PCP. The patient will then be assigned to the PCP with the highest number of visits if there are no relevant specialists who are considered "dominant". Second, if the patient has had more than one visit with a relevant specialist, no "dominant" specialist has been identified, and has two or more visits with a PCP, they will be assigned to that PCP.Finally, the patient will be unassigned if they only saw non-relevant specialists, if the patient has not seen a PCP and no "dominant" specialist can be identified, or if the patient has not had more than one visit with any individual PCP.Patients are then assigned at the Taxpayer Identification Number (TIN) level, which includes solo clinicians and groups of clinicians who have chosen to report their quality under a common TIN. Patients follow their clinician to the TIN designated by the clinician (i.e. they are assigned to their clinician's TIN). Patients unassigned at the individual clinician-level, therefore, continue to be unassigned at the TIN level.Citations1. National Quality Forum. Multiple Chronic Conditions Measurement Framework. http://www.qualityforum.org/WorkArea/linkit.aspx?LinkIdentifier=id&ItemID=71227. Accessed February 20, 2019.
  
  
• Exclusions: The cohort excludes the following patients:1) Patients without continuous enrollment in Medicare Part A or B during the measurement period.2) Patients who were in hospice at any time during the year prior to the measurement year or at the start of the measurement year.3) Patients who had no Evaluation & Management (E&M) visits to a MIPS-eligible clinician.
  
  
• HHS NQS Priority: Communication and Care Coordination
  
  
• HHS Data Source: Claims
  
  
• Measure Type: Outcome
  
  
• Steward: Centers for Medicare & Medicaid Services
  
  
• Endorsement Status:
  
  
• Meaningful Measure Area: Management of chronic conditions
  
  
• Is the measure specified as an electronic clinical quality measure? No
  
  

Summary of Workgroup Deliberations

• Workgroup Recommendation: Do Not Support for Rulemaking with Potential for Mitigation
  
  
• Workgroup Rationale: MAP does not support MUC2019-37 for rulemaking in MIPS with potential for mitigation. The measure is a modified version of an existing NQF-endorsed measure (NQF 2888), last reviewed for endorsement in 2016. MAP noted that the newly developed measure differs from its predecessor in a few ways.•Cohort: CMS added diabetes as a cohort-qualifying condition.•Outcome: CMS narrowed the outcome to focus on admissions where risk can be reduced by providing high-quality ambulatory care, so that the measure can be used to assess ambulatory (rather than ACO-wide) care quality.•Risk-adjustment: CMS added social risk factors to the risk-adjustment model.MAP noted several potential areas of mitigation for the measure: 1. The measure should apply to clinician groups, not to individual clinicians; 2. The measure should use a higher reliability threshold, e.g., 0.7; 3. The measure developer should consider the NQF guidance on attribution and consider patient preference and selection as a method of attribution as that date becomes available; 4. The measure should undergo the NQF endorsement process. MAP suggested that rather than moving directly to this outcome measure, process measures that would get to the desired outcome might be an appropriate stepwise approach to increasing accountability. The MAP Rural Health Workgroup noted that chronic conditions included in this measure are prevalent in rural residents. However, the Rural Health Workgroup does not believe this measure should be linked to payment for rural clinicians or clinician groups.
  
  
• Public comments received: 16

Rationale for measure provided by HHS
HHS has provided additional documentation to support this measure under consideration. Hospital admission rates are an effective marker of ambulatory care quality. Hospital admissions from the outpatient setting reflect a deterioration in patient's clinical status and as such reflect an outcome that is meaningful to both patients and providers. Patients receiving optimal, coordinated high-quality care should use fewer inpatient services than patients receiving fragmented, low-quality care. Thus, high population rates of hospitalization may, at least to some extent, signal poor quality of care or inefficiency in health system performance.Patients with MCCs are at high risk for hospital admission, often for potentially preventable causes, such as exacerbation of pulmonary disease. [1] Evidence from several Medicare demonstration projects suggests that care coordination results in decreased hospital admission rates among high-risk patients. [2] In addition, studies have shown that the types of ambulatory care clinicians this measure targets (for example, primary care providers and specialists caring for patients with MCCs) can influence admission rates through primary care clinician supply, continuity of care, and patient-centered medical home interventions such as team-based and patient-oriented care. [3-5]Given evidence that ambulatory care clinicians can influence hospital admission rates through optimal care and coordination, this measure will incentivize quality improvement efforts leading to improved patient outcomes.Citations:1. Abernathy K, Zhang J, Mauldin P, et al. Acute Care Utilization in Patients With Concurrent Mental Health and Complex Chronic Medical Conditions. Journal of primary care & community health. 2016;7(4):226-233. 2. Brown RS, Peikes D, Peterson G, Schore J, Razafindrakoto CM. Six features of Medicare coordinated care demonstration programs that cut hospital admissions of high-risk patients. Health Aff (Millwood). 2012;31(6):1156-1166. 3. van Loenen T, van den Berg MJ, Westert GP, Faber MJ. Organizational aspects of primary care related to avoidable hospitalization: a systematic review. Fam Pract. 2014;31(5):502-516.4. Dale SB, Ghosh A, Peikes DN, et al. Two-Year Costs and Quality in the Comprehensive Primary Care Initiative. N Engl J Med. 2016;374(24):2345-2356.5. Casalino LP, Pesko MF, Ryan AM, et al. Small primary care physician practices have low rates of preventable hospital admissions. Health Aff (Millwood). 2014;33(9):1680-1688.

Measure Specifications

• NQF Number (if applicable): 138
  
  
• Description: Standardized Infection Ratio (SIR) of healthcare-associated, catheter-associated urinary tract infections (UTI) will be calculated among patients in bedded inpatient care locations, except level II or level III neonatal intensive care units (NICU). This includes acute care general hospitals, long-term acute care hospitals, rehabilitation hospitals, oncology hospitals, and behavior health hospitals.
  
  
• Numerator: Total number of observed healthcare-associated CAUTI among patients in bedded inpatient care locations (excluding patients in Level II or III neonatal ICUs).
  
  
• Denominator: Total number of predicted healthcare-associated CAUTI among inpatient care locations under surveillance for CAUTI during the data period, based on the national CAUTI baseline (denominator count excludes information about Level II Nursery or NICU locations)
  
  
• Exclusions: Denominator Exclusions: The following are not considered indwelling catheters by NHSN definitions:1.Suprapubic catheters 2.Condom catheters 3."In and out" catheterizations4. Nephrostomy tubesNote, that if a patient has either a nephrostomy tube or a suprapubic catheter and also has an indwelling urinary catheter, the indwelling urinary catheter will be included in the CAUTI surveillance.
  
  
• HHS NQS Priority: Communication and Care Coordination
  
  
• HHS Data Source: National Healthcare Safety Network
  
  
• Measure Type: Outcome
  
  
• Steward: Centers for Disease Control and Prevention
  
  
• Endorsement Status:
  
  
• Meaningful Measure Area: Healthcare-associated infections
  
  
• Changes to Endorsed Measure Specifications?: The MUC list indicates the measure has been modified from its endorsed version.
  
  
• Is the measure specified as an electronic clinical quality measure? No
  
  

Summary of Workgroup Deliberations

• Workgroup Recommendation: Support for Rulemaking
  
  
• Workgroup Rationale: MAP supported MUC2019-18 for rulemaking. A prior version of this measure is currently included in PCHQR and addresses the Meaningful Measure Area of healthcare-associated infections. The risk-adjustment model for this measure was updated, and the measure was submitted and re-endorsed by the NQF Patient Safety Standing Committee in the spring 2019 CDP cycle. The measure is otherwise identical to the existing measure in PCHQR. MAP members noted the need to monitor the use of the measure in spinal cord injuries. MAP also noted the importance of comparing cancer hospitals to like hospitals given the differences in the patient populations. The Rural Health Workgroup noted that the 11 PPS-exempt cancer hospitals in the program are in urban centers, but rural patients often use them, and expressed support of MUC2019-18. MAP supported the continued use of this measure in PCHQR with the updated specifications.
  
  
• Public comments received: 2

Rationale for measure provided by HHS
Evidence that this measure promotes CAUTI prevention activities that will lead to improved patient outcomes including reduction of avoidable medical costs, and patient morbidity and mortality through reduced need for antimicrobials and reduced length of stay.

Summary of NQF Endorsement Review

• Year of Most Recent Endorsement Review: 2019
  
  
• Project for Most Recent Endorsement Review: Patient Safety
  
  
• Review for Importance: 1a. Evidence: Pass-20; No Pass-0; 1b. Performance Gap: H-1; M-19; L-0; I-0Rationale:•The Committee agreed that there are preventive activities that can reduce the incidence of CAUTI. These include:oAppropriate catheter useoProper techniques for urinary catheter insertionoProper techniques for urinary catheter maintenance•To support these practices, the developer cites a guideline from the Healthcare Infection Control Practices Advisory Committee (HICPAC): Guideline for Prevention of Catheter-Associated Urinary Tract Infections (2009) revised February 15, 2017.•The developer provided national Standardized Infection Ratios (SIRs) for CAUTI in 2015, 2016, and 2017:oNational Catheter-associated UTI SIR in 2015 is 0.993 = 28,712 observed / 28,910.634 predictedoNational Catheter-associated UTI SIR in 2016 is 0.930 = 26,983 observed / 29,002.430 predictedoNational catheter-associated UTI SIR in 2017 is 0.880 = 24,865 observed / 28,241.960 predicted•The developer also reports that there was a 6% decrease in CAUTI between 2015 and 2016, and a 5% decrease between 2016 and 2017.•The Committee agreed that there is a performance gap warranting measurement in this area; Committee members suggested that the developer analyze and provide data related to performance across different types of institutions (e.g., rehabilitation, acute care, long-term care, etc.).•The Committee also discussed performance gaps on this measure with respect to variation across ethnic groups, rural vs. urban areas, hospital size, and other factors.
  
  
• Review for Scientific Acceptability: 2a. Reliability: M-14; L-4; I-0; 2b. Validity: M-10; L-8; I-2•This measure is deemed as complex and was evaluated by the NQF Scientific Methods Panel.The Standing Committee chose to vote on this measure for, both, reliability and validity.Rationale:•This measure was reviewed for Scientific Acceptability by NQF’s Scientific Methods Panel (SMP).•Data element validity testing was conducted, which NQF accepts as a demonstration of data element reliability.•There was some question from SMP reviewers about the appropriateness of using data element validity testing to stand in for reliability testing. NQF reminded the group that NQF allows this substitution.•The developer notes that the critical data elements of this measure have been validated by several state health departments that require mandatory reporting of CAUTI through the NHSN.•Data validation is conducted by trained auditors, who review medical records and determine whether facilities’ identification of patients meeting CAUTI criteria were accurate.•Sensitivity, specificity, positive predicted value, and negative predicted value are calculated.•Validation results from 10 states are provided—the developer reports that these validations indicated a pooled mean sensitivity of 88.1% (range: 50%-95.6%), specificity of 99.1% (range: 91.4% - 100%), positive predictive value of 94.4% (range: 84.6% - 100%) and negative predictive value of 97.9% (range: 91.4% - 99.8%).•Some SMP reviewers expressed concern about the lack of measure score testing, given that this is a maintenance measure. NQF clarified that either empirical data element or score-level testing are acceptable validity testing methods for maintenance measures.•The measure uses a statistical risk model with risk factors relevant to the facility type. No social risk factors are applied in the modeling.•There was some concern that no statistical results (e.g., c-statistic) of model power were reported.•The Patient Safety Standing Committee discussed the definition of UTIs and the timeframe for determining whether or not a CAUTI is present but focused its discussion largely on the issue of appropriate exclusions, particularly for spinal cord injury (SCI) patients.•A number of representatives of the SCI physician community submitted comments and/or attended the Committee meeting in person to voice their concerns about the measure. These commenters suggested that the measure could be causing unintended adverse consequences by encouraging bladder management practices that are inconsistent with appropriate SCI care and have led to harm for SCI patients.•Representatives of the developer organization (CDC) maintained that there was not enough rigorous evidence supporting exclusion of SCI patients, adding that SCI patients are at high risk for CAUTI and should not be removed from the measure.•Committee members expressed their desire to find a resolution to this issue, noting their general support for the measure and their appreciation of the need for evidence to support exclusions, while also acknowledging that the SCI community had brought forth compelling information suggesting that harm to SCI patients could be an unintended consequence of this measure.•The Committee voted to pass the measure on the Reliability criterion, but consensus was not reached on the Validity criterion.
  
  
• Review for Feasibility: 3. Feasibility: H-2; M-18; 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:•Data for the measure are collected through the National Healthcare Safety Network (NHSN) using a set of standardized forms.•The developer reports that CAUTI and catheter days (the numerator and denominator) must be collected by trained hospital staff from information available in clinical data sources.•The developer notes that some of the data used in the measure can be mined from electronic sources, adding that NHSN is moving towards an electronically captured CAUTI measure for future use. However, development and testing is not complete at this time; barriers include a lack of consistency in the use of electronic records across different platforms and facility types.•The Committee noted that this measure does require manual abstraction of clinical information, but agreed that measuring CAUTI rates is worth the effort.
  
  
• Review for Usability: 4. Use and Usability4a. Use; 4a1. Accountability and transparency; 4a2. Feedback on the measure by those being measured and others; 4b. Usability; 4b1. Improvement; 4b2. The benefits to patients outweigh evidence of unintended negative consequences to patients)4a. Use: Pass-20; No Pass-0 4b. Usability: H-0; M-18; L-0; I-0Rationale:•The measure is used in several accountability programs, including:oHospital Inpatient Quality Reporting Program (HIQR)oHospital Value-Based PurchasingoHospital-Acquired Condition Reduction Program (HACRP)•The developer notes that SIR results are available to NHSN users at any time, based on their current data entry. Data provided within the analysis report includes numerator, denominator, SIR, p-value, and 95% confidence interval. Educational materials are available on the NHSN website that explain each data element.•Based on results from a polling survey, hospitals have indicated that they are running SIR analysis reports within NHSN on a monthly basis, and that they use SIRs for prevention activities in their hospital. State health departments are using the SIR for public reporting purposes and to help target facilities for additional prevention. Feedback was received via email regarding the extent of risk adjustment and the limitations.
  
  
• Review for Related and Competing Measures: No related or competing measures noted.
  
  
• Endorsement Committee Recommendation: Consensus Not Reached
  
  

Measure Specifications

• NQF Number (if applicable): 139
  
  
• Description: Standardized Infection Ratio (SIR) and Adjusted Ranking Metric (ARM) of healthcare-associated, central line-associated bloodstream infections (CLABSI) will be calculated among patients in bedded inpatient care locations.
  
  
• Numerator: Total number of observed healthcare-associated CLABSIs among patients in bedded inpatient care locations.
  
  
• Denominator: Total number of predicted healthcare-associated CLABSI among patients in bedded inpatient care locations, calculated using the facility’s number of central line days and the following significant risk factors:
  • Acute Care Hospitals: CDC location, facility bed size, medical school affiliation, facility type, birthweight category (NICU locations only)
  • Critical Access Hospitals: no significant risk factors, calculation based intercept only model
  • Inpatient Rehabilitation Facilities: Proportion of admissions with stroke, proportion of admissions in other non-specific diagnostic categories
  • Long Term Acute Care Hospitals: CDC location type , facility bed size, average length of stay, proportion of admissions on a ventilator, proportion of admissions on hemodialysis
  
  
  
• Exclusions: S.8. Denominator Exclusions: The following devices are excluded as central lines: oNon-lumened pacemaker wires and other non-lumened devices inserted into central blood vessels or the heart oArterial cathetersoArteriovenous fistulaoArteriovenous graftoExtracorporeal membrane oxygenation (ECMO)oHemodialysis reliable outflow (HERO) dialysis cathetersoIntra-aortic balloon pump (IABP) devicesoAtrial catheters (also known as transthoracic intra-cardiac catheters, those catheters inserted directly into the right or left atrium via the heart wall)oPeripheral IV or MidlinesoVentricular Assist Device (VAD)
  
  
• HHS NQS Priority: Communication and Care Coordination
  
  
• HHS Data Source: National Healthcare Safety Network
  
  
• Measure Type: Outcome
  
  
• Steward: Centers for Disease Control and Prevention
  
  
• Endorsement Status:
  
  
• Meaningful Measure Area: Healthcare-associated infections
  
  
• Changes to Endorsed Measure Specifications?: The MUC list indicates the measure has been modified from its endorsed version.
  
  
• Is the measure specified as an electronic clinical quality measure? No
  
  

Summary of Workgroup Deliberations

• Workgroup Recommendation: Support for Rulemaking
  
  
• Workgroup Rationale: MAP supported MUC2019-19 for rulemaking. MAP noted that MUC2019-19 is an updated version of the existing measure in PCHQR (NQF 0139). The risk-adjustment model for this measure was updated, and the measure was submitted and re-endorsed by the NQF Patient Safety Standing Committee in the spring 2019 CDP review cycle. MAP noted that this measure is also otherwise identical to the existing measure in PCHQR. MAP noted that CLABSIs are associated with significant morbidity, mortality, and costs. Patients in ICUs are at an increased risk for CLABSI because 48 percent of ICU patients have indwelling central venous catheters, accounting for 15 million central line days per year in U.S. MAP encouraged CMS and CDC to review if there are patient-specific traits that lead to higher rates of CLABSI within cancer hospitals. The Rural Health Workgroup also noted that the 11 cancer hospitals in the program are in urban centers, but rural patients often use them, and the Workgroup expressed support of MUC2019-19.
  
  
• Public comments received: 2

Rationale for measure provided by HHS
A substantial body of peer-reviewed studies and reviews document that CLABSI can be minimized through proper management of the central line. Efforts to improve central line insertion and maintenance practices, with early discontinuance of lines are recommended. These efforts result in decreased morbidity and mortality and reduced healthcare costs.Use of this measure to track CLABSIs through a nationalized standard for HAI monitoring, leads to improved patient outcomes and provides a mechanism for identifying improvements and evaluating prevention efforts.

Summary of NQF Endorsement Review

• Year of Most Recent Endorsement Review: 2019
  
  
• Project for Most Recent Endorsement Review: Patient Safety
  
  
• Review for Importance: 1a. Evidence: Pass-20; No Pass-0; 1b. Performance Gap: H-5; M-15; L-0; I-0Rationale:•The Committee agreed that there are preventive activities that can reduce the incidence of CLABSI; these include:oAppropriate central line use: promptly removing non-essential intravascular catheters,oHand hygiene and aseptic techniqueoThe use of maximal barrier equipment including a large patient drape, inserter mask, sterile gloves, cap, and sterile gown during aseptic insertion of the central lineoAppropriate insertion site decontamination before central line insertionoChlorhexidine-impregnated dressings (in patients = 18 years), and (vi) implementing surveillance strategies•To support these practices, the developer cites a guideline:oO'Grady NP, Alexander M, Burns LA, Dellinger PE, Garland J, et al. Guidelines for the prevention of intravascular catheter-related infections. Available at http://www.cdc.gov/hicpac/pdf/guidelines/bsi-guidelines-2011.pdf.•The developer provided national Standardized Infection Ratios (SIRs) for CAUTI in 2015, 2016, and 2017:oNational CLABSI SIR in 2015 is 0.994 = 26,029 observed / 26,183.537 predictedoNational CLABSI SIR in 2016 is 0.891 = 23,591 observed / 26,472.710 predictedoNational CLABSI SIR in 2017 is 0.814 = 21,173 observed / 25,993.180 predicted•The developer also reports that there was a 10% decrease in CLABSI between 2015 and 2016, and a 9% decrease between 2016 and 2017.•The Committee discussed performance gaps on this measure with respect to variation across ethnic groups, rural vs. urban areas, hospital size, and other factors.
  
  
• Review for Scientific Acceptability: 2a. Reliability precise specifications, testing; 2b. Validity testing, threats to validity•This measure is deemed as complex and was evaluated by the NQF Scientific2a. NQF Scientific Methods Panel Ratings for Reliability: H-0; M-4; L-0; I-02b. NQF Scientific Methods Panel Ratings for Validity: H-0; M-3 L-1; I-0(The Committee accepted the NQF Scientific Methods Panel’s Moderate rating, unanimously.)Rationale:•Data element validity testing was conducted, which NQF allows to serve as a demonstration of data element reliability.•The developer notes that the critical data elements of this measure have been validated by a number of state health departments that require mandatory reporting of CLABSI through the NHSN.•Data validation is conducted by trained auditors, who review medical records and determine whether facilities’ identification of patients meeting or not meeting CLABSI criteria was accurate.•Sensitivity, specificity, positive predicted value, and negative predicted value were calculated.•Validation results from 5 states are provided—the developer reports that these validations indicated a pooled mean sensitivity of 87.5% (range: 80.3%-100%), specificity of 99.3% (range: 98.7% - 100%), positive predictive value of 96.9% (range: 94.2% - 100%) and negative predictive value of 96.9% (range: 93.7% - 100%).•Committee members discussed the relationship between ‘catheter days’ and infections, noting that CLABSI risk likely increases the longer a line is left in.oThe developer noted that CDC is exploring ways of incorporating this and other factors into measurement calculations.•This measure was reviewed against the Scientific Acceptability criteria by NQF’s Scientific Methods Panel (SMP); the SMP judged it to have met NQF’s standards for reliability and validity.•The Patient Safety Standing Committee accepted the SMP’s ratings.
  
  
• Review for Feasibility: Feasibility: H-1; M-19; L-0; I-0 Rationale:•Data for the measure are collected through the National Healthcare Safety Network (NHSN) using a set of standardized forms.•The developer reports that CLABSI and central line days (the numerator and denominator) must be collected by trained hospital staff from information available in clinical data sources.•The developer noted that some of the data used in the measure can be mined from electronic sources, adding that NHSN is moving towards an electronically captured CAUTI measure for future use. However, development and testing are not complete at this time.
  
  
• Review for Usability: 4a. Use: Pass-20; No Pass-0 4b. Usability: H-7; M-13; L-0; I-0Rationale:•The measure is used in several accountability programs, including:oHospital Inpatient Quality Reporting Program (HIQR)oHospital Value-Based PurchasingoHospital-Acquired Condition Reduction Program (HACRP)•The Committee agreed that this measure meets the Use & Usability criteria, noting that it is used in federal payment and public reporting programs.•Committee members did raise caution about potential ‘gaming’ of the measure, suggesting that the developer should be watchful for these issues and find ways of addressing them.
  
  
• Review for Related and Competing Measures: No related or competing measures noted.
  
  
• Endorsement Committee Recommendation: Standing Committee Recommendation for Endorsement: Y-20; N-0
  
  

Measure Specifications

• NQF Number (if applicable): 0
  
  
• Description: The percent of emergency department visits for Medicare beneficiaries ages 18 and older with multiple high-risk chronic conditions (MCC) who had a follow-up service within 7 days of the ED visit. Multiple high-risk chronic conditions include 2 or more of the following: Alzheimer's disease, atrial fibrillation, chronic kidney disease, COPD, depression, heart failure, cardiovascular disease evidenced by acute myocardial infarction, and stroke or transient ischemic attack. Appropriate follow-up services include but not limited to: an outpatient visit; telephone visit; transitional or complex care management services, outpatient or telehealth behavioral health visit, or observation visit.
  
  
• Numerator: A follow-up service within 7 days after the ED visit (8 total days)
  
  
• Denominator: ED visits for Medicare beneficiaries ages 18 and older with multiple high-risk chronic conditions
  
  
• Exclusions: 1. Medicare beneficiaries in hospice2. ED visits followed by admission to an acute or non-acute inpatient care setting on the date of the ED visit or within 7 days after the ED visit
  
  
• HHS NQS Priority: Communication and Care Coordination
  
  
• HHS Data Source: Encounter Data
  
  
• Measure Type: Process
  
  
• Steward: National Committee for Quality Assurance
  
  
• Endorsement Status:
  
  
• Meaningful Measure Area: Management of chronic conditions
  
  
• Is the measure specified as an electronic clinical quality measure? No
  
  

Summary of Workgroup Deliberations

• Workgroup Recommendation: Conditional Support for Rulemaking
  
  
• Workgroup Rationale: MAP conditionally supported MUC2019-14 for rulemaking, pending NQF endorsement. MAP noted the importance of the care coordination domain as a CMS priority. MAP observed that care coordination is the deliberate organization of patient care activities between two or more participants involved in a patient’s care to facilitate the appropriate delivery of healthcare services. This measure is an additional process measure to the Medicare Part C and D Star Ratings that lends itself to better care efficiencies and coordination for health plans and their beneficiaries. MAP also discussed the increase of utilization and costs associated with use of emergency departments for Medicare beneficiaries, particularly those with dual-eligible status and with a behavioral health diagnosis, both of which are much higher cost demographics. Coordinating the care of beneficiaries who use emergency services is an important component to ensuring that they also are receiving outpatient care and preventive services with the potential to mitigate disease progression that results in further unnecessary use of emergency facilities. The Rural Health Workgroup noted that the chronic conditions included in this measure are prevalent in rural residents, and that lack of access to care in rural areas may make performance on this measure more difficult for plans that cover rural residents. MAP was encouraged that telephone follow-up was included in this measure but encouraged CMS to ensure that the telephone follow-ups are meaningful to patients.
  
  
• Public comments received: 8

Rationale for measure provided by HHS
The Medicare population includes a large number of individuals and older adults with high-risk multiple chronic conditions (MCC) who often receive care from multiple providers and settings and, as a result, are more likely to experience fragmented care and adverse healthcare outcomes, including an increased likelihood of ED visits (1,2). Medicare beneficiaries with MCCs require high levels of care coordination, particularly as the transition from the ED to the community. During these transitions, they often face communication lapses between ED and outpatient providers and inadequate patient, caregiver and provider understanding of diagnoses, medication and follow-up needs (3,4,5,6). This poor care coordination results in an increased risk for medication errors, repeat ED visits, hospitalization, nursing home admission and death (7,8). Medicare beneficiaries with MCCs not only experience poorer health outcomes, but also greater health care utilization (e.g., physician use, hospital and ED use, medication use) and costs (e.g., medication, out-of-pocket, total health care) (9). Medicare beneficiaries with MCCs are some of the heaviest users of high-cost, preventable services such as those offered by the ED (10,11). An estimated 75 percent of health care spending is on people with MCCs (12,13).REFERENCES1. AHRQ. 2010. Multiple Chronic Conditions Chartbook. “2010 Medical Expenditure Panel Survey Data.†https://www.ahrq.gov/sites/default/files/wysiwyg/professionals/prevention-chronic-care/decision/mcc/mccchartbook.pdf (Accessed January 11, 2017)2. Agency for Healthcare Quality and Research (AHRQ). 2012. “Coordinating Care for Adults with Complex Care Needs in the Patient-Centered Medical Home: Challenges and Solutions.†https://pcmh.ahrq.gov/sites/default/files/attachments/coordinating-care-for-adults-with-complex-care-needs-white-paper.pdf3. Altman, R., J.S. Shapiro, T. Moore and G.J. Kuperman. 2012. “Notifications of hospital events to outpatient clinicians using health information exchange: a post-implementation survey.†Journal of Innovation in Health Informatics 20(4).4. Coleman, E.A., R.A. Berenson. 2004. “Lost in transition: challenges and opportunities for improving the quality of transitional care.†Annals of Internal Medicine 141(7).5. Dunnion, M.E., and B. Kelly. 2005. “From the emergency department to home.†Journal of Clinical Nursing 14(6), 776–85.6. Rowland, K., A.K. Maitra, D.A. Richardson, K. Hudson and K.W. Woodhouse. 1990. “The discharge of elderly patients from an accident and emergency department: functional changes and risk of readmission.†Age Ageing 19(6), 415–18.7. Hastings, S.N., E.Z. Oddone, G. Fillenbaum, R.J. Sloane and K.E. Schmader. 2008. “Frequency and predictors of adverse health outcomes in older Medicare beneficiaries discharged from the emergency department.†Medical Care 46(8), 771–7.8. Niedzwiecki, M., K. Baicker, M. Wilson, D.M. Cutler and Z. Obermeyer. 2016. “Short-term outcomes for Medicare beneficiaries after low-acuity visits to emergency departments and clinics.†Medical Care 54(5), 498–503.9. Lehnert, T., D. Heider, H. Leicht, S. Heinrich, S. Corrieri, M. Luppa, S. Riedel-Heller and H.H. Konig. 2011. “Review: health care utilization and costs of elderly persons with multiple chronic conditions.†Medical Care Research & Review 68(4), 387–420.10. CMS. 2012. Chronic Conditions among Medicare Beneficiaries, Chartbook, 2012 Edition. Baltimore, MD. https://www.cms.gov/research-statistics-data-and-systems/statistics-trends-and-reports/chronic-conditions/downloads/2012chartbook.pdf (Accessed July 19, 2016)11. Lochner, K.A., and C.S. Cox. 2013. Prevalence of multiple chronic conditions among Medicare beneficiaries, United States, 2010. https://www.cdc.gov/pcd/issues/2013/12_0137.htm (Accessed January 11, 2017)12. CDC. 2009. The power of prevention: Chronic disease…the public health challenge of the 21st century. http://www.cdc.gov/chronicdisease/pdf/2009-power-of-prevention.pdf (Accessed January 24, 2017)13. Care Innovations. 2013. “Cost Control for Chronic Conditions: An Imperative for MA Plans.†The Business Case for Remote Care Management (RCM). https://www.rmhpcommunity.org/sites/default/files/resource/The%20Business%20Case%20for%20RCM.pdf (Accessed January 24, 2017).

Measure Specifications

• NQF Number (if applicable):
  
  
• Description: The intent of the measure is to improve the coordination of care for Medicare Advantage members as they transition between inpatient and outpatient settings. The measure assesses the percentage of discharges for members 18 years of age and older who had each of the following four indicators: notification of inpatient admission; receipt of discharge information; patient engagement after inpatient discharge; and medication reconciliation post-discharge. Plans report separate rates for individuals 18-64 years of age and those 65 years and older, as well as a total rate for each indicator in the measure.
  
  
• Numerator: 1.Notification of inpatient admission: Documentation of receipt of notification of inpatient admission on the day of admission or the following day and2.Receipt of discharge information: Documentation of receipt of discharge information on the day of discharge or the following day and3.Patient engagement after inpatient discharge: Documentation of patient engagement (e.g., office visits, visits to the home, telehealth) provided within 30 days after discharge and4.Medication reconciliation post-discharge: Documentation of medication reconciliation on the date of discharge through 30 days after discharge (31 total days).
  
  
• Denominator: Acute or non-acute inpatient discharges for Medicare beneficiaries 18 years and older. The denominator is based on discharges, not members. Members may appear more than once.For Administrative Specification, the denominator is the eligible population.For Hybrid Specification, the denominator is a systematic sample drawn from the eligible population.
  
  
• Exclusions: Members in Hospice
  
  
• HHS NQS Priority: Communication and Care Coordination
  
  
• HHS Data Source: Claims; Record Review
  
  
• Measure Type: Composite
  
  
• Steward: National Committee for Quality Assurance
  
  
• Endorsement Status:
  
  
• Meaningful Measure Area: Transfer of health information and interoperability
  
  
• Is the measure specified as an electronic clinical quality measure? No
  
  

Summary of Workgroup Deliberations

• Workgroup Recommendation: Conditional Support for Rulemaking
  
  
• Workgroup Rationale: MAP conditionally supported MUC2019-21 for rulemaking, pending NQF endorsement. MAP noted that this measure was also designated as a first-year measure for HEDIS 2018. MAP observed that Medicare beneficiaries are at particular risk during transitions of care because of higher comorbidities, declining cognitive function, and increased medication use. There is observed variance in performance among health plans on all four components of the measure. Further, evidence indicates that good care transitions and care coordination reduce healthcare costs and improve outcomes. MAP also noted that the medication reconciliation postdischarge component of this measure is already included in the Star Ratings as an independent measure and has been since 2017. The measure developer (NCQA) indicated its intention to work with CMS to develop a plan to avoid the need for health plans to report on both measures. MAP expressed concern that this measure is not entirely electronic, but it was noted that alternative data sources are not available. The Rural Health Workgroup noted the importance of measures to assess transitions of care for rural residents but that the measure requires chart abstraction, which can be particularly burdensome for small rural providers. They also noted that a yes/no checkbox measure of medication reconciliation may not drive improvements in care quality. There was some concern with the medication reconciliation component, particularly given the lack of pharmacists in rural areas.
  
  
• Public comments received: 5

Rationale for measure provided by HHS
The Medicare population includes older adults and individuals with complex health needs who often receive care from multiple providers and settings, and thus experience highly fragmented care and adverse health care utilization patterns and outcomes. This population is at particular risk during transitions of care because of higher comorbidities, declining cognitive function and increased medication use (1). Transitions from the inpatient setting to home often results in poor care coordination, including communication lapses between inpatient and outpatient providers, intentional and unintentional medication changes, incomplete diagnostic work-ups and inadequate beneficiary, caregiver and provider understanding of diagnoses, medication and follow-up needs (2).Poor hospital transitions are not only associated with poor health outcomes, but also increased health care utilization and cost, including duplicate medical services, medication errors and increased emergency department visits and readmissions (3). In 2010, Medicare beneficiaries 65 years and older accounted for 11.9 million (approximately 34 percent) of all hospital discharges in the United States (4). One study estimated that inadequate care coordination and poor care transitions resulted in $25 billion-$45 billion in unnecessary spending in 2011 (5). Other studies have found that care coordination programs that do not incorporate timely transitional care elements are unlikely to result in reduced hospitalizations and associated Medicare spending (6), and current payment structures do not provide much incentive for the collaboration necessary to implement effective care coordination post-discharge (7). Hospital transitions require clear communication between inpatient and outpatient providers to ensure optimal health outcomes during patient handoffs (8, 9, 10, 11, 12). Effective care coordination efforts must include notifying patients' primary care practitioners (PCP) of admission, PCP receipt of meaningful and timely discharge information (13), patient engagement through follow-up provided post-discharge and medication reconciliation post-discharge.REFERENCES1. Vognar, L., and N. Mujahid. 2015. “Healthcare transitions of older adults: An overview for the general practitioner.†Rhode Island Medical Journal http://www.rimed.org/rimedicaljournal/2015/04/2015-04-15-ltc-vognar.pdf (Accessed July 12, 2016)2. Rennke, S., O.K. Nguyen, M.H. Shoeb, Y. Magan, R.M. Wachter and S.R. Ranji. 2013. “Hospital-initiated transitional care as a patient safety strategy: A systematic review.†Annals of Internal Medicine 158(5, Pt. 2), 433–40.3. Sato, M., T. Shaffer, A.I. Arbaje and I.H. Zuckerman. 2011. “Residential and health care transition patterns among older Medicare beneficiaries over time.†The Gerontologist 51(2), 170–8.4. Centers for Disease Control and Prevention (CDC). 2010. Number, rate, and average length of stay for discharges from short-stay hospitals, by age, region, and sex: United States, 2010. http://www.cdc.gov/nchs/data/nhds/1general/2010gen1_agesexalos.pdf (Accessed June 22, 2016)5. Health Affairs. 2012. Health Policy Brief: Care Transitions. September 13, 2012. http://healthaffairs.org/healthpolicybriefs/brief_pdfs/healthpolicybrief_76.pdf (Accessed July 12, 2016)6. Peikes, D., A. Chen, J. Schore and R. Brown. 2009. “Effects of care coordination on hospitalization, quality of care, and health care expenditures among Medicare beneficiaries.†Journal of the American Medical Association 301(3).7. Coleman, E.A. and R.A. Berenson. 2004. “Lost in transition: Challenges and opportunities for improving the quality of transitional care.†Annals of Internal Medicine 141(7), 533–6.8. Kripalani, S., A.T. Jackson, J.L. Schnipper and E.A. Coleman. 2007. “Promoting effective transitions of care at hospital discharge: A review of key issues for hospitalists.†Journal of Hospital Medicine 2(5).9. Kripalani, S., F. LeFevre, C.O. Phillips, M.V. Williams, P. Basaviah and D.W. Baker. 2007. “Deficits in communication and information transfer between hospital-based and primary care physicians: Implications for patient safety and continuity of care.†Journal of the American Medical Association 297(8), 831–41.10. Peart, K. N. 2015. When used effectively, discharge summaries reduce hospital readmissions. http://news.yale.edu/2015/01/15/when-used-effectively-discharge-summaries-reduce-hospital-readmissions (Accessed May 4, 2015)11. van Walraven, C., R. Seth and A. Laupacis. 2002. “Dissemination of discharge summaries. Not reaching follow-up physicians.†Canadian Family Physician 48, 737–4212. van Walraven, C., R. Seth, P.C. Austin and A. Laupacis, A. 2002. “Effect of discharge summary availability during post-discharge visits on hospital readmission.†Journal of General Internal Medicine 17(3), 186–92.13. Kind, A.J.H., and M.A. Smith. 2008. “Documentation of Mandated Discharge Summary Components in Transitions from Acute to Subacute Care.†In: Henriksen, K., J.B. Battles, M.A. Keyes, and M.L. Grady, editors. Advances in Patient Safety: New Directions and Alternative Approaches (Vol. 2: Culture and Redesign). Rockville, MD: Agency for Healthcare Research and Quality, August.Notification of inpatient admissions14. Commonwealth Fund. 2015. Reducing Care Fragmentation. http://www.improvingchroniccare.org/downloads/reducing_care_fragmentation.pdf (Accessed May 4, 2015)15. Jones, C.D., M.B. Vu, C.M. O’Donnell, M.E. Anderson, S. Patel, H.L. Wald, … and D.A. DeWalt. 2015. “A failure to communicate: A qualitative exploration of care coordination between hospitalists and primary care providers around patient hospitalizations.†Journal of General Internal Medicine 30(4), 417–24. 16. Moran, W.P., K.S. Davis, T.J. Moran, R. Newman and P.D. Mauldin. 2012. “Where are my patients? It is time to automate notification of hospital use to primary care practices.†Southern Medical Journal 105(1), 18–23.17. Oregon Health Quality Corporation. 2011. Transitions in Care Hospital Survey. http://q-corp.org/sites/qcorp/files/Transitions-in-Care-Hospital-Survey.pdf (Accessed May 4, 2015) 18. Pantilat, S.Z., P.K. Lindenauer, P.P. Katz and R.M. Wachter. 2002. “Primary care physician attitudes regarding communication with hospitalists.†DM 8(4), 218–29.19. UT Health Science Center San Antonio. 2015. Clinical Safety and Effectiveness, Session Five. http://uthscsa.edu/cpshp/CSEProject/To%20increase%20the%20notification%20of%20primary%20care%20physicians%20(PCP)%20when%20their%20patients%20are%20admitted%20or%20discharged.pdf (Accessed May 4, 2015) 20. Ventura, T., D. Brown, T. Archibald, A. Goroski and J. Brock. 2010. “Improving care transitions and reducing hospital readmissions: Establishing the evidence for community-based implementation strategies through the care transitions theme.†The Remington Report. http://www.communitysolutions.com/assets/2012_Institute_Presentations/caretransitioninterventions051812.pdf (Accessed July 26, 2016) 21. Bell, C.M., J.L. Schnipper, A.D. Auerbach, P.J. Kaboli, T.B. Wetterneck, D.V. Gonzales, V.M. Arora, J.X. Zhang and D.O. Meltzer. 2009. “Association of communication between hospital-based physicians and primary care providers with patient outcomes.†Journal of General Internal Medicine 24(3).Receipt of discharge information22. Alpers, A. 2001. “Key legal principles for hospitalists.†American Journal of Medicine 111(9B), 5S–9S.23. Goldman, L., S.Z. Pantilat and W.F. Whitcomb. 2001. “Passing the clinical baton: 6 principles to guide the hospitalist.†American Journal of Medicine 111(9B), 36S–39S.24. Jack, B.W., K.C. Veerappa, D. Anthony, J.L. Greenwald, G.M. Sanchez, A.E. Johnson, S.R. Forsythe, J.K., O’Donnell, M.K. Paasche-Orlow, C. Manasseh, S. Martin and L.A. Culpepper. 2009. “Reengineered hospital discharge program to decrease rehospitalization: A randomized trial.†Annals of Internal Medicine 150(3).25. RAND. 2014. “Evaluation and Development of Outcome Measures for Quality Assessment in Medicare Advantage and Special Needs Plans.†Validation Study Final Report. Santa Monica, CA: RAND.Patient engagement after inpatient discharge26. Arbaje, A.I., D.L. Kansagara, A.H. Salanitro, H.L. Englander, S. Kripalani, S.F. Jencks and L.A. Lindquist. 2014. “Regardless of age: Incorporating principles from geriatric medicine to improve care transitions for patients with complex needs.†Journal of General Internal Medicine 29(6), 932–9.27. Berkowitz, R.E., Z. Fang, B.K. Helfand, R.N. Jones, R. Schreiber and M.K. Paasche-Orlow. 2013. “Project ReEngineered Discharge (RED) lowers hospital readmissions of patients discharged from a skilled nursing facility.†Journal of the American Medical Directors Association 14(10) 736–40.28. Bisognano, M., and A. Boutwell. 2009. “Improving transitions to reduce readmissions.†Frontiers of Healthcare Services Management. https://www.ache.org/pdf/secure/gifts/July10-frontiers.pdf (Accessed July 27, 2016)29. Braun, E., A. Baidusi, G. Alroy and Z.S. Azzam. 2009. “Telephone follow-up improves patients satisfaction following hospital discharge.†European Journal of Internal Medicine 20(2), 221–5.30. Coleman, E.A., C. Parry, S. Chalmers, et al. 2006. “The Care Transitions Intervention: Results of a randomized controlled trial.†Archives of Internal Medicine 166(17), 1822–8.31. Forster, A.J., H.J. Murff, J.F. Peterson, T.K. Gandhi and D.W. Bates. 2003. “The incidence and severity of adverse events affecting patients after discharge from the hospital.†Annals of Internal Medicine 138(3).32. Hansen, L.O., J.L. Greenwald, T. Budnitz, E. Howell, L. Halasyamani, G. Maynard, ... and M.V. Williams. 2013. “Project BOOST: Effectiveness of a multihospital effort to reduce rehospitalization.†Journal of Hospital Medicine 8(8), 421–7.33. Harrison, P.L., P.A. Hara, J.E. Pope, M.C. Young and E.Y. Rula. 2011. “The impact of postdischarge telephonic follow-up on hospital readmissions.†Population Health Management 14(1), 27–32.34. Hernandez, A.F., M.A. Greiner, G.C. Fonarow, B.G. Hammill, P.A. Heidenreich, C.W. Yancy, E.D. Peterson and L.H. Curtis. 2010. “Relationship between early physician follow-up and 30-day readmission among Medicare beneficiaries hospitalized for heart failure.†Journal of the American Medical Association 303(17), 1716–22.35. Lin, C.Y., A.E. Barnato and H.B. Degenholtz. 2011. “Physician follow-up visits after acute care hospitalization for elderly Medicare beneficiaries discharged to noninstitutional settings.†Journal of The American Geriatrics Society 59(10), 1947–54.36. Misky, G.J., H.L. Wald and E.A. Coleman. 2011. “Post-hospitalization transitions: Examining the effects of timing of primary care provider follow-up.†Journal of Hospital Medicine 5(7), 392–7.37. Muus, K.J., A. Knudson, M.G. Klug, J. Gokun, M. Sarrazin and P. Kaboli. 2010. “Effect of post-discharge follow-up care on re-admissions among US veterans with congestive heart failure: A rural-urban comparison.†Rural Remote Health 10(2), 1447.38. Naylor, M. D., Brooten, D. A., Campbell, R., et al. 2003. “Comprehensive discharge planning and home follow-up of hospitalized elders.†Journal of the American Medical Association 281, 613–20. 39. Naylor, M.D. 2003. Transitional care of older adults. Annual Review of Nursing Research 20, 127–47.40. The Bridge Model. 2016. The Bridge Model. http://www.transitionalcare.org/the-bridge-model/ (Accessed August 22, 2016)41. Balaban, R.B., J.S. Weissman, P.A. Samuel and S. Woolhandler. 2008. “Redefining and redesigning hospital discharge to enhance patient care: A randomized controlled study.†Journal of General Internal Medicine 23(8), 1228–33.Medication reconciliation post-discharge42. Patterns of medications use in the United States 2006: a report from the Slone Survey. http://www.bu.edu/slone/files/2012/11/SloneSurveyReport2006.pdf (Accessed July 17, 2014)43. Vogeli, C., A.E. Shields, T.A. Lee, et al. 2007. “Multiple Chronic Conditions: Prevalence, Health Consequences, and Implications for Quality, Care Management, and Costs.†J Gen Intern Med 22(suppl 3): 391–5.

Measure Specifications

• NQF Number (if applicable): 2940
  
  
• Description: Percent of beneficiaries receiving opioid prescriptions with an average daily morphine milligram equivalent (MME) greater than or equal to 90 mg over a period of 90 days or longer.
  
  
• Numerator: Number of member-years of Medicare Part D beneficiaries 18 years and older in the denominator with an average MME greater than or equal to 90 mg.
  
  
• Denominator: Number of member-years of enrolled Medicare Part D beneficiaries 18 years and older with at least 2 fills of a prescription opioid on unique dates of service (DOS) and at least 15 total opioid days supply over a period of 90 days or longer during the measurement period.
  
  
• Exclusions: Medicare beneficiaries with a cancer diagnosis or that are enrolled in hospice at any time during the measurement period are excluded from the denominator.
  
  
• HHS NQS Priority: Communication and Care Coordination
  
  
• HHS Data Source: Prescription Drug Event Data Elements
  
  
• Measure Type: Process
  
  
• Steward: Pharmacy Quality Alliance
  
  
• Endorsement Status:
  
  
• Meaningful Measure Area: Prevention and treatment of opioid and substance use disorders
  
  
• Changes to Endorsed Measure Specifications?: The MUC list indicates the measure has been modified from its endorsed version.
  
  
• Is the measure specified as an electronic clinical quality measure? No
  
  

Summary of Workgroup Deliberations

• Workgroup Recommendation: Conditional Support for Rulemaking
  
  
• Workgroup Rationale: MAP conditionally supported for rulemaking MUC2019-57 Use of Opioids at High Dosage in Persons without Cancer (OHD). The condition of support was that other opioid measures considered would not move into the Star Ratings; this measure was otherwise considered fit for implementation without conditions. The measure has been endorsed by NQF at the health plan level as NQF 2940. MAP noted that this measure leads to health plans carefully considering the needs of patients at high doses, encouraging appropriate nonopioid pain management, providing appropriate personalized pain care plans, directly addressing OUD, and potentially tapering patients off of high dose opioid regimens. MAP noted that concerns have been raised that pressure from health plans to diminish prescribing could be associated with the unintended consequence of patients seeking illicitly obtained opioids or heroin. This may lead to changes in prescribing practices for clinicians to adhere to CDC prescribing guidelines that were intended to serve as guidance and not as a strict mandate. The MAP Rural Health Workgroup agreed that opioid use is a relevant issue for rural residents, but expressed concern that without a balancing measure, there is a potential for patient harm due to forced tapering and the potential for seeking illicit drugs to treat pain. Rural residents have relatively less access to alternative pain treatment and other resources.
  
  
• Public comments received: 10

Rationale for measure provided by HHS
CMS adapted three PQA opioid overuse measures related to opioid use, including this OHD measure, to examine the quality of use related to the dose of the medications over time, access to the medications and the combination of both of these criteria. CMS has provided each Part D sponsor monthly reports using these metrics, and will publish these as CMS display measures beginning for 2020. Pending rule-making, we will consider adding one of these into the CMS Part C & D Star Ratings.Claims data from commercially insured patients indicate that approximately 8% of opioidprescriptions for acute pain and 12% for chronic pain specify a daily dosage of 120 MED ormore (1). The proportion of patients being treated at this dosage for more than 90 days has not been described. However, one study of veterans treated with 180 MED/day or more for 90+ days (2) found that this group was characterized by high rates of psychiatric and substance abuse disorders and frequently did not receive care consistent with clinical guidelines. Other studies have suggested the people at high opioid dosage are at greater risk of overdoses and fractures (3, 4, 5).The Washington State Agency Medical Directors Group has suggested 120 MED as a dosagelevel that should not be exceeded without special consideration (6). Prescription drug monitoring programs, which track the use of multiple providers by patients, indicate that such use is typically found among a small proportion of patients, with the proportion declining as the number of providers increases. In Massachusetts in 2006, considering only Schedule II opioids, 0.5% of patients saw 4+ prescribers and 4+ pharmacies (7). A national study found that 13% of patients had overlapping prescriptions from two or more different prescribers during an 18-month period. Of these, 0.5% used 4+ prescribers and 4+ pharmacies (8). People who see multiple prescribers or use multiple pharmacies are more likely to die of drug overdoses (4). Data from the California PDMP indicates that people with higher daily dosages are more likely to see multiple prescribers or go to multiple pharmacies (9). The data above suggest that prevention of opioid overdose deaths should focus on strategies that target (1) high-dose opioid users as well as (2) persons who seek care from multiple doctors and pharmacies. The data suggest that these criteria can be considered separately, as measures related to prescribed opioids for legitimate uses versus diverted uses. Thus, we will consider use of 3 measures, one for each criteria and one that is the intersection of both criteria. For the Part C and D Star Ratings, we would add only one of these measures. REFERENCES1. Ying Liu, PhD; Joseph E. Logan, PhD; Leonard J. Paulozzi, MD, MPH; Kun Zhang, MS; and Christopher M. Jones, PharmD. Potential Misuse and Inappropriate Prescription Practices Involving Opioid Analgesics. Am J Manag Care. 2013;19(8):648-658.2. Clinical characteristics of veterans prescribed high doses of opioid medications for chronic noncancer pain. Benjamin J. Morasco, Jonathan P. Duckart, Thomas P. Carr, Richard A. Deyo, Steven K. Dobscha. PAIN. 151 (2010) 625–632.3. Kate M. Dunn, PhD; Kathleen W. Saunders, JD; Carolyn M. Rutter, PhD; Caleb J. Banta-Green, MSW, MPH, PhD; Joseph O. Merrill, MD, MPH; Mark D. Sullivan, MD, PhD; Constance M. Weisner, DrPH, MSW; Michael J. Silverberg, PhD, MPH; Cynthia I. Campbell, PhD; Bruce M. Psaty, MD, PhD; and Michael Von Korff, ScD. Opioid Prescriptions for Chronic Pain and Overdose - A Cohort Study. Ann Intern Med. 2010;152:85-92.4. Paulozzi, et al. A History of Being Prescribed Controlled Substances and Risk of Drug Overdose Death. Pain Medicine 2011.5. Kathleen W. Saunders, JD, Kate M. Dunn, Ph. D, Joseph O. Merrill, M.D, M.P.H., Mark Sullivan, M.D., Ph.D., Constance Weisner, DrPH, M.S.W, Jennifer Brennan Braden, M.D., M.P.H., Bruce M. Psaty, M.D., Ph.D., and Michael Von Korff, Sc.D. Relationship of Opioid Use and Dosage Levels to Fractures in Older Chronic Pain Patients. Society of General Internal Medicine. 2009. DOI: 10.1007/s11606-009-1218-z.6. Agency Medical Directors Group (AMDG). Interagency Guideline on Opioid Dosing for Chronic Non-cancer Pain: An educational aid to improve care and safety with opioid therapy. 2010 Update. www.cdc.gov/HomeandRecreationalSafety/ Poisoning/brief.htm7. Nathaniel Katz, Lee Panas, MeeLee Kim, Adele D. Audet, Arnold Bilansky, John Eadie, Peter Kreiner, Florence C Paillard, Cindy Thomas and Grant Carrow. Usefulness of prescription monitoring programs for surveillance – analysis of Schedule II opioid prescription data in Massachusetts, 1996–2006y. Pharmacoepidemiology and drug safety 2010; 19: 115–123.8. M. Soledad Cepeda, Daniel Fife, Wing Chow, Gregory Mastrogiovanni and Scott C. Henderson. Assessing Opioid Shopping Behaviour - A Large Cohort Study from a Medication Dispensing Database in the US. Drug Saf 2012.9. Han H, Kass PH, Wilsey BL, Li C-S (2012) Individual and County-Level Factors Associated with Use of Multiple Prescribers and Multiple Pharmacies to Obtain Opioid Prescriptions in California. PLoS ONE 7(9): e46246. doi:10.1371/journal.pone.0046246.

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: 1a. Evidence: 16-H; 3-M; 0-L; 0-I; 1b. Performance Gap: 13-H; 7-M; 0-L; 0-IRationale:• The developer provided a systematic review of the evidence demonstrating the benefits of highdose opioids for chronic pain are not established and the risks for serious harm related to opioidtherapy increases at higher doses.• Lower dosages of opioids reduce the risk for overdose, but a single dosage threshold for safeopioid use has not been identified.• The measure was tested in three different health plan data sources – the Medicare population(mean rate=39.27 per 1,000), one commercial heath plan (mean rate= 32.003 per 1,000), andthe Medicaid population (mean rate =34.04 per 1,000). The Committee noted that these ratesdemonstrate a significant performance gap.• The Committee noted this is highly important to measure given the current national opioidoveruse problem.
  
  
• Review for Scientific Acceptability: 2a. Reliability: 13-H; 7-M; 0-L; 0-I 2b. Validity: 14-M; 7-L; 0-IRationale:• The developer used several data sets for reliability testing:o For Medicare testing, the analysis included a convenience sample of over 700 MedicarePart D prescription drug plans (comprising a total of 7,067,445 individuals aged 18 andolder)o Testing was also conducted in one Commercial health plan (comprising a total of209,191 individuals age 18 and older)o For Medicaid testing, the analysis included 8 state-based prescription drug planscovering 6 states (comprising a total of 1,437,410 individuals age 18 and older)• The mean reliability score across all plans is 0.9938.• The developer assessed the face validity (only) of the measure using a technical expert panelfrom the Pharmacy Quality Alliance (PQA). 67 percent strongly agreed that the measure resultsreflected quality of care. Five PQA member organizations also tested the measure using theirown data, and all strongly agreed that the measure reflected the quality of care provided fortheir populations.
  
  
• Review for Feasibility: 3. Feasibility: 13-H; 8-M; 0-L; 0-I(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:• Pilot test sites indicated the measure was feasible and results were able to be reportedefficiently and accurately.• All the data elements are in defined fields in electronic claims
  
  
• Review for Usability: 4. Usability and Use: 11-H; 9-M; 1-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:• The measure is currently being used in the Medicare Part D Overutilization Monitoring Systemto monitor the utilization of opioids for members with the Medicare drug benefit.• Although no unintended negative consequences to individuals or populations were identifiedduring testing, concerns have been raised that prescribing changes such as dose reduction(without offering or arranging evidence-based treatment for patients with opioid use disorder)might be associated with unintended negative consequences, such as patients seeking heroin orother illicitly obtained opioids (1,2) or interference with appropriate pain treatment.
  
  
• Review for Related and Competing Measures: 5. Related and Competing MeasuresRelated measures:• Measure 2950: Use of Opioids from Multiple Providers in Persons without Cancer- Theproportion (XX out of 1,000) of individuals without cancer receiving prescriptions for opioidsfrom four (4) or more prescribers AND four (4) or more pharmacies.• Measure 2951: Use of Opioids from Multiple Providers and at High Dosage in Persons withoutCancer- The proportion (XX out of 1,000) of individuals without cancer receiving prescriptionsfor opioids with a daily dosage greater than 120mg morphine equivalent dose (MED) for 90consecutive days or longer, AND who received opioid prescriptions from four (4) or moreprescribers AND four (4) or more pharmacies.• These measures are also being considered for endorsement. The Committee determined thatthey are related but not competing.
  
  
• Endorsement Public Comments: 6. Public and Member CommentComments:This measure received 3 comments. The commenters noted that the measure may be too inclusive andthe developer should consider narrowing the measure to specific chronic conditions or diagnoses to bemore meaningful.Developers Response:The recommendations in the 2015 American Geriatrics Society Beers Criteria are based on a systematicevidence review conducted by American Geriatrics Society Beers Criteria Expert Panel. The review isfocused on the evidence for potential harms of medications in older adults. Medications then includedin the Beers Criteria recommendations are those that the panel found evidence indicating that themedications should in general be avoided in all older adults or avoided in older adults with certainconditions or diseases, due to their associated risks for these populations. The Beers Criteria is updatedregularly based on currently available literature. We believe it's important for this quality measure to bebased on the systematic evidence review that is conducted by the Beers Criteria Expert Panel. Thecomplete evidence tables for the systematic review can be accessed on the American Geriatrics Society'swebsite here: http://geriatricscareonline.org/toc/american-geriatrics-society-updated-beers-criteria-forpotentially-inappropriate-medication-use-in-older-adults/CL001NCQA recognizes that some of the medications that are most attributable to adverse drug events inolder adults that result in ED visits and hospitalizations are not included in the Beers Criteria asmedications to be generally avoided (e.g., warfarin, antidiabetics and oral antiplatelets - although someoral antiplatelets are in fact included in the Beers Criteria and this measure: Dipyridamole, Ticlopidine).These other high-risk medications should be addressed in separate quality measures that focus on safeprescribing and appropriate monitoring, rather than this measure which focuses on medications thatshould be generally avoided. We agree with the need for such quality measures to improve safeprescribing of anticoagulants, antidiabetics, and opioids and have current work underway at NCQA toexplore development of measures in these areas. Of note, the Pharmacy Quality Alliance has severalmeasures addressing opioid prescribing that are currently being considered for NQF endorsement aspart of this Patient Safety project. NCQA supports the endorsement of these measures and has plans toadapt them for health plan reporting in the near future.In terms of the way this measure is currently specified to include a number of different medications, webelieve that creating separate quality measures or indicators for all the specific medications in the BeersCriteria, or for each drug-disease interaction, would be burdensome for measurement and reporting byhealth plans. Plans can look at medications on an individual basis to see where improvements andinterventions are needed, however we do not think this level of detail would be desirable for nationalreporting by health plans.As a measure of potentially inappropriate medication use, NCQA does not expect this measure'sperformance to ever reach 0% (i.e., no prescribing of high-risk medications). There will always be caseswhere the benefits of prescribing a high-risk medication may outweigh the risks for certain patients.Clinicians should take into account various factors when considering the risk-benefit ratio of prescribinga high-risk medication to an individual. A companion paper to the Beers Criteria was published by theAmerican Geriatrics Society Workgroup on Improving Use of the Beers Criteria in 2015. The paperspecifically states "the AGS 2015 Beers Criteria are reasonable to use for performance measurementacross large groups of patients and providers but should not be used to judge care for any individual"(Steinman et al., 2015, JAGS). We believe measuring this concept of potentially inappropriatemedication use among elderly at the health plan (i.e., population) level is an important and usefulmedication safety measure that health plans can use to identify high-risk medication prescribing.34Committee Response:The Committee agrees with the developer response and maintains their decision to recommend thismeasure for continued endorsement.
  
  
• Endorsement Committee Recommendation: Steering Committee Recommendation for Endorsement: 21-Y; 0-N
  
  

Measure Specifications

• NQF Number (if applicable): 2950
  
  
• Description: Percent of beneficiaries receiving opioid prescriptions from 4 or more prescribers and 4 or more pharmacies within 180 days or less.
  
  
• Numerator: Number of member-years of Medicare Part D beneficiaries 18 years and older in the denominator who received opioids from 4 or more prescribers and 4 or more pharmacies within 180 days or less during the measurement period.
  
  
• Denominator: Number of member-years of enrolled Medicare Part D beneficiaries 18 years and older with at least 2 fills of a prescription opioid on unique dates of service (DOS) and at least 15 total opioid days supply over a period of 90 days or longer during the measurement period.
  
  
• Exclusions: Medicare beneficiaries with a cancer diagnosis or that are enrolled in hospice at any time during the measurement period are excluded from the denominator.
  
  
• HHS NQS Priority: Communication and Care Coordination
  
  
• HHS Data Source: Prescription Drug Event Data Elements
  
  
• Measure Type: Process
  
  
• Steward: Pharmacy Quality Alliance
  
  
• Endorsement Status:
  
  
• Meaningful Measure Area: Prevention and treatment of opioid and substance use disorders
  
  
• Changes to Endorsed Measure Specifications?: The MUC list indicates the measure has been modified from its endorsed version.
  
  
• Is the measure specified as an electronic clinical quality measure? No
  
  

Summary of Workgroup Deliberations

• Workgroup Recommendation: Support for Rulemaking
  
  
• Workgroup Rationale: During this cycle, MAP supported one measure for rulemaking in the Part C and D Star Ratings, MUC2019-60 Use of Opioids from Multiple Providers in Persons without Cancer (OMP). This measure appropriately identifies either mismanaged pain or potential opioid seeking behavior. MAP observed that the measure will encourage health plans to address pain management and OUD within their beneficiary population while avoiding unintended consequences associated with rapid decline of opioid dosages. MAP noted that this measure is endorsed at the health plan level as NQF 2950. MAP noted that all three opioid measures are currently in use in the SSP Opioid Utilization Reports as well as in the Part D Overutilization Monitoring System. The MAP Rural Health Workgroup suggested that although this measure could promote use of drug monitoring programs in rural areas, on the whole, it may not be particularly applicable due to the relatively few pharmacies in rural areas.
  
  
• Public comments received: 9

Rationale for measure provided by HHS
CMS adapted three PQA opioid overuse measures related to opioid use, including this OMP measure, to examine the quality of use related to the dose of the medications over time, access to the medications and the combination of both of these criteria. CMS has provided each Part D sponsor monthly reports using these metrics, and will publish these as CMS display measures beginning for 2020. Pending rule-making, we will consider adding one of these into the CMS Part C & D Star Ratings.Claims data from commercially insured patients indicate that approximately 8% of opioidprescriptions for acute pain and 12% for chronic pain specify a daily dosage of 120 MED ormore (1). The proportion of patients being treated at this dosage for more than 90 days has not been described. However, one study of veterans treated with 180 MED/day or more for 90+ days (2) found that this group was characterized by high rates of psychiatric and substance abuse disorders and frequently did not receive care consistent with clinical guidelines. Other studies have suggested the people at high opioid dosage are at greater risk of overdoses and fractures (3, 4, 5).The Washington State Agency Medical Directors Group has suggested 120 MED as a dosagelevel that should not be exceeded without special consideration (6). Prescription drug monitoring programs, which track the use of multiple providers by patients, indicate that such use is typically found among a small proportion of patients, with the proportion declining as the number of providers increases. In Massachusetts in 2006, considering only Schedule II opioids, 0.5% of patients saw 4+ prescribers and 4+ pharmacies (7). A national study found that 13% of patients had overlapping prescriptions from two or more different prescribers during an 18-month period. Of these, 0.5% used 4+ prescribers and 4+ pharmacies (8). People who see multiple prescribers or use multiple pharmacies are more likely to die of drug overdoses (4). Data from the California PDMP indicates that people with higher daily dosages are more likely to see multiple prescribers or go to multiple pharmacies (9). The data above suggest that prevention of opioid overdose deaths should focus on strategies that target (1) high-dose opioid users as well as (2) persons who seek care from multiple doctors and pharmacies. The data suggest that these criteria can be considered separately, as measures related to prescribed opioids for legitimate uses versus diverted uses. Thus, we will consider use of 3 measures, one for each criteria and one that is the intersection of both criteria. For the Part C and D Star Ratings, we would add only one of these measures.REFERENCES1. Ying Liu, PhD; Joseph E. Logan, PhD; Leonard J. Paulozzi, MD, MPH; Kun Zhang, MS; and Christopher M. Jones, PharmD. Potential Misuse and Inappropriate Prescription Practices Involving Opioid Analgesics. Am J Manag Care. 2013;19(8):648-658.2. Clinical characteristics of veterans prescribed high doses of opioid medications for chronic noncancer pain. Benjamin J. Morasco, Jonathan P. Duckart, Thomas P. Carr, Richard A. Deyo, Steven K. Dobscha. PAIN. 151 (2010) 625–632.3. Kate M. Dunn, PhD; Kathleen W. Saunders, JD; Carolyn M. Rutter, PhD; Caleb J. Banta-Green, MSW, MPH, PhD; Joseph O. Merrill, MD, MPH; Mark D. Sullivan, MD, PhD; Constance M. Weisner, DrPH, MSW; Michael J. Silverberg, PhD, MPH; Cynthia I. Campbell, PhD; Bruce M. Psaty, MD, PhD; and Michael Von Korff, ScD. Opioid Prescriptions for Chronic Pain and Overdose - A Cohort Study. Ann Intern Med. 2010;152:85-92.4. Paulozzi, et al. A History of Being Prescribed Controlled Substances and Risk of Drug Overdose Death. Pain Medicine 2011.5. Kathleen W. Saunders, JD, Kate M. Dunn, Ph. D, Joseph O. Merrill, M.D, M.P.H., Mark Sullivan, M.D., Ph.D., Constance Weisner, DrPH, M.S.W, Jennifer Brennan Braden, M.D., M.P.H., Bruce M. Psaty, M.D., Ph.D., and Michael Von Korff, Sc.D. Relationship of Opioid Use and Dosage Levels to Fractures in Older Chronic Pain Patients. Society of General Internal Medicine. 2009. DOI: 10.1007/s11606-009-1218-z.6. Agency Medical Directors Group (AMDG). Interagency Guideline on Opioid Dosing for Chronic Non-cancer Pain: An educational aid to improve care and safety with opioid therapy. 2010 Update. www.cdc.gov/HomeandRecreationalSafety/ Poisoning/brief.htm 7. Nathaniel Katz, Lee Panas, MeeLee Kim, Adele D. Audet, Arnold Bilansky, John Eadie, Peter Kreiner, Florence C Paillard, Cindy Thomas and Grant Carrow. Usefulness of prescription monitoring programs for surveillance – analysis of Schedule II opioid prescription data in Massachusetts, 1996–2006y. Pharmacoepidemiology and drug safety 2010; 19: 115–123.8. M. Soledad Cepeda, Daniel Fife, Wing Chow, Gregory Mastrogiovanni and Scott C. Henderson. Assessing Opioid Shopping Behaviour - A Large Cohort Study from a Medication Dispensing Database in the US. Drug Saf 2012.9. Han H, Kass PH, Wilsey BL, Li C-S (2012) Individual and County-Level Factors Associated with Use of Multiple Prescribers and Multiple Pharmacies to Obtain Opioid Prescriptions in California. PLoS ONE 7(9): e46246. doi:10.1371/journal.pone.0046246.

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: 1a. Evidence: 0-H; 20-M; 0-L; 0-I 1b. Performance Gap: 13-H; 7-M; 0-L; 0-IRationale:• The evidence suggests that prescriptions for opioids from multiple prescribers and pharmaciescorrelate with undesired health outcomes. The use of multiple prescribers and pharmacies are 35associated with increased risks for opioid overdose. The Committee noted this is highlyimportant to measure given the current national opioid overuse problem.• The measure was tested in three different health plan data sources – the Medicare population(mean was 23.31 per 1,000 and the median was 26.12 per 1,000), one commercial heath plan(rate for this plan was 20.57 per 1,000), and the Medicaid population (mean was 72.28 per 1,000and the median was 69.93 per 1,000). The Committee noted that these rates demonstrate asignificant performance gap.
  
  
• Review for Scientific Acceptability: 2a. Reliability: 9-H; 11-M; 0-L;0-I 2b. Validity: 19-M; 0-L; 1-IRationale:• The developer tested the measure at the score level using several data sets for reliability testing:o For Medicare testing, the analysis included a convenience sample of over 700 MedicarePart D prescription drug plans (comprising a total of 7,067,445 individuals aged 18 andolder)o Testing was also conducted in one Commercial health plan (comprising a total of209,191 individuals age 18 and older)o For Medicaid testing, the analysis included 8 state-based prescription drug planscovering 6 states (comprising a total of 1,437,410 individuals age 18 and older)• To demonstrate reliability, the developer conducted a signal-to-noise analysis of the computedmeasure score using a beta-binomial model.• The mean reliability score across all plans is 0.9355.• The developer assessed the face validity (only) of the measure using a technical expert panelfrom the Pharmacy Quality Alliance (PQA). 67 percent strongly agreed that the measure resultsreflected quality of care. Five PQA member organizations also tested the measure using theirown data, and all strongly agreed that the measure reflected the quality of care provided fortheir populations.
  
  
• Review for Feasibility: 3. Feasibility: 18-H; 2-M; 0-L; 0-I(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 in defined field in electronic claims.• Pilot test sites indicated the measure was feasible and results were able to be reportedefficiently and accurately.
  
  
• Review for Usability: 4. Usability and Use: 10-H; 9-M; 1-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:• The measure is currently being used in the Medicare Part D Overutilization Monitoring Systemto monitor the utilization of opioids for members with the Medicare drug benefit.36• Although no unintended negative consequences to individuals or populations were identifiedduring testing, , concerns have been raised that prescribing changes such as dose reduction(without offering or arranging evidence-based treatment for patients with opioid use disorder)might be associated with unintended negative consequences, such as patients seeking heroin orother illicitly obtained opioids (1,2) or interference with appropriate pain treatment
  
  
• Review for Related and Competing Measures: 5. Related and Competing Measures• Measure 2940: Use of Opioids at high Dosage in Persons without Cancer- The proportion (XX outof 1,000) of individuals without cancer receiving prescriptions for opioids with a daily dosagegreater than 120mg morphine equivalent dose (MED) for 90 consecutive days or longer.• Measure 2951: Use of Opioids from Multiple Providers and at High Dosage in Persons withoutCancer- The proportion (XX out of 1,000) of individuals without cancer receiving prescriptionsfor opioids with a daily dosage greater than 120mg morphine equivalent dose (MED) for 90consecutive days or longer, AND who received opioid prescriptions from four (4) or moreprescribers AND four (4) or more pharmacies.• These measures are also being considered for endorsement. The Committee determined thatthey are related but not competing.
  
  
• Endorsement Public Comments: Comment:The measure received 1 comment in support of the measure with a few recommendations for how themeasure could be improved.Developer Response:The recommendations in the 2015 American Geriatrics Society Beers Criteria are based on a systematicevidence review conducted by American Geriatrics Society Beers Criteria Expert Panel. The review isfocused on the evidence for potential harms of medications in older adults. Medications then includedin the Beers Criteria recommendations are those that the panel found evidence indicating that themedications should in general be avoided in all older adults or avoided in older adults with certainconditions or diseases, due to their associated risks for these populations. The Beers Criteria is updatedregularly based on currently available literature. We believe it's important for this quality measure to bebased on the systematic evidence review that is conducted by the Beers Criteria Expert Panel. Thecomplete evidence tables for the systematic review can be accessed on the American Geriatrics Society'swebsite here: http://geriatricscareonline.org/toc/american-geriatrics-society-updated-beers-criteria-forpotentially-inappropriate-medication-use-in-older-adults/CL001NCQA recognizes that some of the medications that are most attributable to adverse drug events inolder adults that result in ED visits and hospitalizations are not included in the Beers Criteria asmedications to be generally avoided (e.g., warfarin, antidiabetics and oral antiplatelets - although someoral antiplatelets are in fact included in the Beers Criteria and this measure: Dipyridamole, Ticlopidine).These other high-risk medications should be addressed in separate quality measures that focus on safeprescribing and appropriate monitoring, rather than this measure which focuses on medications thatshould be generally avoided. We agree with the need for such quality measures to improve safeprescribing of anticoagulants, antidiabetics, and opioids and have current work underway at NCQA to 37explore development of measures in these areas. Of note, the Pharmacy Quality Alliance has severalmeasures addressing opioid prescribing that are currently being considered for NQF endorsement aspart of this Patient Safety project. NCQA supports the endorsement of these measures and has plans toadapt them for health plan reporting in the near future.In terms of the way this measure is currently specified to include a number of different medications, webelieve that creating separate quality measures or indicators for all the specific medications in the BeersCriteria, or for each drug-disease interaction, would be burdensome for measurement and reporting byhealth plans. Plans can look at medications on an individual basis to see where improvements andinterventions are needed, however we do not think this level of detail would be desirable for nationalreporting by health plans.As a measure of potentially inappropriate medication use, NCQA does not expect this measure'sperformance to ever reach 0% (i.e., no prescribing of high-risk medications). There will always be caseswhere the benefits of prescribing a high-risk medication may outweigh the risks for certain patients.Clinicians should take into account various factors when considering the risk-benefit ratio of prescribinga high-risk medication to an individual. A companion paper to the Beers Criteria was published by theAmerican Geriatrics Society Workgroup on Improving Use of the Beers Criteria in 2015. The paperspecifically states "the AGS 2015 Beers Criteria are reasonable to use for performance measurementacross large groups of patients and providers but should not be used to judge care for any individual"(Steinman et al., 2015, JAGS). We believe measuring this concept of potentially inappropriatemedication use among elderly at the health plan (i.e., population) level is an important and usefulmedication safety measure that health plans can use to identify high-risk medication prescribing.Committee Response:The Committee agrees with the developer response and maintains their decision to recommend thismeasure for continued endorsement.
  
  
• Endorsement Committee Recommendation: Steering Committee Recommendation for Endorsement: 20-Y; 0-N
  
  

Measure Specifications

• NQF Number (if applicable): 2951
  
  
• Description: Percent of beneficiaries receiving opioid prescriptions with an average daily morphine milligram equivalent (MME) greater than or equal to 90 mg over a period of 90 days or longer, and opioid prescriptions from 4 or more prescribers and 4 or more pharmacies within 180 days or less.
  
  
• Numerator: Number of member-years of Medicare Part D beneficiaries 18 years and older in the denominator with an average MME greater than or equal to 90 mg during the measurement period and who received opioid prescriptions from 4 or more prescribers and 4 or more pharmacies within 180 days or less during the measurement period.
  
  
• Denominator: Number of member-years of enrolled Medicare Part D beneficiaries 18 years and older with at least 2 fills of a prescription opioid on unique dates of service (DOS) and at least 15 total opioid days supply over a period of 90 days or longer during the measurement period.
  
  
• Exclusions: Medicare beneficiaries with a cancer diagnosis or that are enrolled in hospice at any time during the measurement period are excluded from the denominator.
  
  
• HHS NQS Priority: Communication and Care Coordination
  
  
• HHS Data Source: Prescription Drug Event Data Elements
  
  
• Measure Type: Process
  
  
• Steward: Pharmacy Quality Alliance
  
  
• Endorsement Status:
  
  
• Meaningful Measure Area: Prevention and treatment of opioid and substance use disorders
  
  
• Changes to Endorsed Measure Specifications?: The MUC list indicates the measure has been modified from its endorsed version.
  
  
• Is the measure specified as an electronic clinical quality measure? No
  
  

Summary of Workgroup Deliberations

• Workgroup Recommendation: Do Not Support for Rulemaking
  
  
• Workgroup Rationale: MAP did not support for rulemaking MUC2019-61 Use of Opioids from Multiple Providers and at a High Dosage in Persons without Cancer (OHDMP). MAP observed that this measure was endorsed in 2017 as NQF 2951. This measure was also seen as duplicative of the other two measures, with little added benefit to the program from the combined measure. MAP emphasized the need for parsimony in the measure set. Of the three proposed opioid measures, the MAP Rural Health Workgroup agreed this one was the least useful.
  
  
• Public comments received: 8

Rationale for measure provided by HHS
CMS adapted three PQA opioid overuse measures related to opioid use, including this OHDMP measure, to examine the quality of use related to the dose of the medications over time, access to the medications and the combination of both of these criteria. CMS has provided each Part D sponsor monthly reports using these metrics, and will publish these as CMS display measures beginning for 2020. Pending rule-making, we will consider adding one of these into the CMS Part C & D Star Ratings.Claims data from commercially insured patients indicate that approximately 8% of opioidprescriptions for acute pain and 12% for chronic pain specify a daily dosage of 120 MED ormore (1). The proportion of patients being treated at this dosage for more than 90 days has not been described. However, one study of veterans treated with 180 MED/day or more for 90+ days (2) found that this group was characterized by high rates of psychiatric and substance abuse disorders and frequently did not receive care consistent with clinical guidelines. Other studies have suggested the people at high opioid dosage are at greater risk of overdoses and fractures (3, 4, 5).The Washington State Agency Medical Directors Group has suggested 120 MED as a dosagelevel that should not be exceeded without special consideration (6). Prescription drug monitoring programs, which track the use of multiple providers by patients, indicate that such use is typically found among a small proportion of patients, with the proportion declining as the number of providers increases. In Massachusetts in 2006, considering only Schedule II opioids, 0.5% of patients saw 4+ prescribers and 4+ pharmacies (7). A national study found that 13% of patients had overlapping prescriptions from two or more different prescribers during an 18-month period. Of these, 0.5% used 4+ prescribers and 4+ pharmacies (8). People who see multiple prescribers or use multiple pharmacies are more likely to die of drug overdoses (4). Data from the California PDMP indicates that people with higher daily dosages are more likely to see multiple prescribers or go to multiple pharmacies (9). The data above suggest that prevention of opioid overdose deaths should focus on strategies that target (1) high-dose opioid users as well as (2) persons who seek care from multiple doctors and pharmacies. The data suggest that these criteria can be considered separately, as measures related to prescribed opioids for legitimate uses versus diverted uses. Thus, we will consider use of 3 measures, one for each criteria and one that is the intersection of both criteria. For the Part C and D Star Ratings, we would add only one of these measures.REFERENCES1. Ying Liu, PhD; Joseph E. Logan, PhD; Leonard J. Paulozzi, MD, MPH; Kun Zhang, MS; and Christopher M. Jones, PharmD. Potential Misuse and Inappropriate Prescription Practices Involving Opioid Analgesics. Am J Manag Care. 2013;19(8):648-658.2. Clinical characteristics of veterans prescribed high doses of opioid medications for chronic noncancer pain. Benjamin J. Morasco, Jonathan P. Duckart, Thomas P. Carr, Richard A. Deyo, Steven K. Dobscha. PAIN. 151 (2010) 625–632.3. Kate M. Dunn, PhD; Kathleen W. Saunders, JD; Carolyn M. Rutter, PhD; Caleb J. Banta-Green, MSW, MPH, PhD; Joseph O. Merrill, MD, MPH; Mark D. Sullivan, MD, PhD; Constance M. Weisner, DrPH, MSW; Michael J. Silverberg, PhD, MPH; Cynthia I. Campbell, PhD; Bruce M. Psaty, MD, PhD; and Michael Von Korff, ScD. Opioid Prescriptions for Chronic Pain and Overdose - A Cohort Study. Ann Intern Med. 2010;152:85-92.4. Paulozzi, et al. A History of Being Prescribed Controlled Substances and Risk of Drug Overdose Death. Pain Medicine 2011.5. Kathleen W. Saunders, JD, Kate M. Dunn, Ph. D, Joseph O. Merrill, M.D, M.P.H., Mark Sullivan, M.D., Ph.D., Constance Weisner, DrPH, M.S.W, Jennifer Brennan Braden, M.D., M.P.H., Bruce M. Psaty, M.D., Ph.D., and Michael Von Korff, Sc.D. Relationship of Opioid Use and Dosage Levels to Fractures in Older Chronic Pain Patients. Society of General Internal Medicine. 2009. DOI: 10.1007/s11606-009-1218-z.6. Agency Medical Directors Group (AMDG). Interagency Guideline on Opioid Dosing for Chronic Non-cancer Pain: An educational aid to improve care and safety with opioid therapy. 2010 Update. www.cdc.gov/HomeandRecreationalSafety/ Poisoning/brief.htm 7. Nathaniel Katz, Lee Panas, MeeLee Kim, Adele D. Audet, Arnold Bilansky, John Eadie, Peter Kreiner, Florence C Paillard, Cindy Thomas and Grant Carrow. Usefulness of prescription monitoring programs for surveillance – analysis of Schedule II opioid prescription data in Massachusetts, 1996–2006y. Pharmacoepidemiology and drug safety 2010; 19: 115–123.8. M. Soledad Cepeda, Daniel Fife, Wing Chow, Gregory Mastrogiovanni and Scott C. Henderson. Assessing Opioid Shopping Behaviour - A Large Cohort Study from a Medication Dispensing Database in the US. Drug Saf 2012.9. Han H, Kass PH, Wilsey BL, Li C-S (2012) Individual and County-Level Factors Associated with Use of Multiple Prescribers and Multiple Pharmacies to Obtain Opioid Prescriptions in California. PLoS ONE 7(9): e46246. doi:10.1371/journal.pone.0046246.

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: 1a. Evidence: 0-H;17-M; 1-L; 0-I; 1b. Performance Gap: 10-H; 6-M; 0-L; 0-IRationale:• The benefits for high dose opioids for chronic pain are not established and the risks for seriousharms related to opioid therapy increase at higher opioid dosage. The use of multipleprescribers and pharmacies are associated with increased risks for opioid overdose. The risk foroverdose increases with the number of prescribers and pharmacies.• The measure’s performance was tested in three different health plan data sources – theMedicare population (mean was 3.03 per 1,000 and the median was 2.89 per 1,000), onecommercial heath plan (mean rate 1.45 per 1,000), and the Medicaid population (mean was2.68 per 1,000 and the median was 2.38 per 1,000).
  
  
• Review for Scientific Acceptability: 2a. Reliability: 11-H; 5-M; 0-L;0-I 2b. Validity: 16-M; 2-L; 0-IRationale:• The measure was tested at the score level. The developer used several data sets for reliabilitytesting:• For Medicare testing, the analysis included a convenience sample of over 700 Medicare Part Dprescription drug plans (comprising a total of 7,067,445 individuals aged 18 and older)• Testing was also conducted in one Commercial health plan (comprising a total of 209,191individuals age 18 and older)39• For Medicaid testing, the analysis included 8 state-based prescription drug plans covering 6states (comprising a total of 1,437,410 individuals age 18 and older)• The mean reliability score across all plans is 0.9208.• The developer assessed the face validity (only) of the measure using a technical expert panelfrom the Pharmacy Quality Alliance (PQA). 83.3 percent strongly agreed that the measureresults reflected quality of care. Five PQA member organizations also tested the measure usingtheir own data, and all strongly agreed that the measure reflected the quality of care providedfor their populations.
  
  
• Review for Feasibility: 3. Feasibility: 15-H; 2-M; 0-L; 0-I(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 field in electronic claims• Pilot test sites indicated the measure was feasible and results were able to be reportedefficiently and accurately.
  
  
• Review for Usability: 4. Usability and Use: 10-H; 9-M; 1-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:• The measure is currently being used in the Medicare Part D Overutilization Monitoring Systemto monitor the utilization of opioids for members with the Medicare drug benefit.• Although no unintended negative consequences to individuals or populations were identifiedduring testing, , concerns have been raised that prescribing changes such as dose reduction(without offering or arranging evidence-based treatment for patients with opioid use disorder)might be associated with unintended negative consequences, such as patients seeking heroin orother illicitly obtained opioids (1,2) or interference with appropriate pain treatment.(3) Dataindicate that if access to prescription opioids is limited, some users of opioid analgesics willtransition to heroin or other illicitly obtained opioids, leading to increased overdose deathcoincident with prescribing restrictions
  
  
• Review for Related and Competing Measures: 5. Related and Competing Measures• Measure 2950: Use of Opioids from Multiple Providers in Persons without Cancer- Theproportion (XX out of 1,000) of individuals without cancer receiving prescriptions for opioidsfrom four (4) or more prescribers AND four (4) or more pharmacies.• Measure 2940: Use of Opioids at high Dosage in Persons without Cancer- The proportion (XX outof 1,000) of individuals without cancer receiving prescriptions for opioids with a daily dosagegreater than 120mg morphine equivalent dose (MED) for 90 consecutive days or longer.• These measures are also being considered for endorsement. The Committee determined thatthey are related but not competing.
  
  
• Endorsement Committee Recommendation: Steering Committee Recommendation for Endorsement: 18-Y; 0-N
  
  

Program Index

• Ambulatory Surgical Center Quality Reporting Program 
  
  
• End-Stage Renal Disease Quality Incentive Program 
  
  
• Hospital Acquired Condition Reduction Program 
  
  
• Hospital Inpatient Quality Reporting (IQR) Program and Medicare and Medicaid Promoting Interoperability Program for Eligible Hospitals and Critical Access Hospitals (CAHs) 
  
  
• 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 
  
  
• Merit-Based Incentive Payment System 
  
  
• Medicare Shared Savings Program 
  
  
• Part C and D Star Ratings 
  
  
• Inpatient Rehabilitation Facility Quality Reporting Program 
  
  
• Skilled Nursing Facility Quality Reporting Program 
  
  
• Home Health Quality Reporting Program 
  
  
• Long-Term Care Hospital Quality Reporting Program 
  
  
• Hospice Quality Reporting Program 
  
  

Full Program Summaries

Program History and Structure: The 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:

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

1. Making Care Safer
2. Person and Family Engagement
3. Best Practice of Healthy Living
4. Effective Prevention and Treatment a. Surgical outcome measures
5. Making Care Affordable
6. Communication/Care Coordination

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 spreadsheet 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.

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 spreadsheet organized according to concepts.

Program History and Structure: Section 3008 of the Patient Protection and Affordable Care Act of 2010 (ACA) established the Hospital- Acquired 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 2019 IPPS/LTCH PPS final rule a scoring methodology change that removed domains and assigns equal weighting to each measure for which a hospital has a measure beginning with the FY 2020 HACRP. The program currently uses the CMS Patient Safety Indicator 90 (CMS PSI 90) and five Healthcare-Associated Infections (HAI) as collected by the Centers for Disease Control and Prevention (CDC) National Healthcare Safety Network (NHSN). The measures in HACRP are categorized under the Meaningful Measure area of “Make Care Safer by Reducing Harm Caused in the Delivery of Care.” The Total HAC Score is the sum of the equally weighted average of the hospital’s measure scores.

High Priority Domains for Future Measure Consideration:

For FY 2018 federal rulemaking, CMS may propose the adoption, removal, and/or suspensionof measures for fiscal years 2019 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 meet the Measure Requirements below that are electronic Clinical Quality Measures (eCQMs)
2. Measures that address adverse drug events during the inpatient stay
3. Measures that address ventilator-associated events
4. Additional surgical site infection locations that are not already covered within an existing measure in the program
5. 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)
6. 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
7. Measure that address causes of hospital harm such as an all-cause harm measure or a measure that encompasses multiple harms
8. Measures that demonstrate safety and/or high reliability practices and outcomes

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 spreadsheet organized according to concepts.

Program History and Structure: The Hospital Inpatient Quality Reporting (IQR) 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, outcome, patient experience of care, efficiency, and cost of care measures. Failure to meet the requirements of the Hospital IQR Program will result in a reduction by one-fourth to a hospital’s 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 Hospital IQR Program, 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 Promoting Interoperability, 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 Promoting Interoperability Program. All Promoting Interoperability 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 Hospital IQR Program and the Medicare and Medicaid Promoting Interoperability Program for EHs and CAHs, and information regarding the eCQMs. Based on current alignment efforts, hospitals that successfully submit eCQM data to meet Hospital IQR Program requirements fulfill the Medicare and Medicaid Promoting Interoperability Program requirement for reporting of eCQMs with one submission.

High Priority Domains for Future Measure Consideration:

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

1. Strengthen Person and Family Engagement as Partners in their Care:
   1. Functional Outcomes
   2. Care is Personalized and Aligned with Patient's Goals
2. Promote Effective Communication and Coordination of Care:
   1. Seamless Transfer of Health Information
3. Promote Effective Prevention and Treatment of Chronic Disease:
   1. Prevention and Treatment of Opioid and Substance Use Disorders
   2. Make Care Safer by Reducing Harm Caused in the Delivery of Care:
      1. Preventable Healthcare Harm

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. If feasible, 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 address a Meaningful Measure area, with preference for measures addressing the high priority domains and/or measurement gaps for future measure consideration
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 spreadsheet organized according to concepts.

Program History and Structure: The Hospital Outpatient Quality Reporting (HOQR) 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
2. Person and Family Engagement
3. Best Practice of Healthy Living
4. Effective Prevention and Treatment a. Surgical outcome measures
5. Making Care Affordable
6. Communication/Care Coordination

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 spreadsheet 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 HRRP program currently include measures for acute myocardial infarction, heart failure, pneumonia, chronic obstructive pulmonary disease, elective total knee and total hip arthroplasty, and coronary artery bypass graft surgery. Planned readmissions are excluded from the excess readmission calculation. In the (FY) 2018 IPPS final rule, CMS changed the methodology to calculate the payment adjustment factor in accordance with the 21st Century Cures Act to assess penalties based on a hospital’s performance relative to other hospitals treating a similar proportion of Medicare patients who are also eligible for full Medicaid benefits (i.e. dual eligible) beginning with the (FY) 2019 program.

High Priority Domains for Future Measure Consideration:

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

• Promote Effective Communication and Coordination of Care:
  • Admissions and Readmissions to Hospitals

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 spreadsheet 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, a measure of Medicare Spending Per Beneficiary 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 (IQR) 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. Strengthen Person and Family Engagement as Partners in their Care:
   1. Functional Outcomes
2. Promote Effective Prevention and Treatment of Chronic Disease:
1. Prevention and Treatment of Opioid and Substance Use Disorders
2. Risk Adjusted Mortality

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 Meaningful Measure area, 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 spreadsheet 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 13 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) 2020 and beyond.

High Priority Domains for Future Measure Consideration:

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

1. Strengthen Person and Family Engagement as Partners in their Care
   1. Patient Experience and Functional Outcomes
   2. Depression Measure
   3. Patient's Experience of Care
2. Make Care Safer by Reducing Harm Caused in the Delivery of Care
   1. Preventable Healthcare Harm

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 Meaningful Measure area, 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 spreadsheet 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 program for 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 voluntary quality reporting program, in which data will be publicly reported on a CMS website. In the FY 2012 IPPS rule, 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 was adopted. Twelve new measures were adopted in the FY 2014 IPPS rule and one measure was adopted in theFY 2015 IPPS rule. Three new measures were adopted and six were removed in the FY 2016 IPPS rule. One measure was adopted in the FY 2017 IPPS rule. In the FY 2018 IPPS rule, four measures were adopted and three measures were removed. One measure was adopted and four measures were removed in the FY 2019 IPPS rule.

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.
4. Promote Effective Prevention and Treatment of Chronic Disease
   • Measures related to appropriate opioid prescribing and pain management best practices for cancer patients

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 spreadsheet organized according to concepts.

Program History and Structure: The Medicare Access and CHIP Reauthorization Act of 2015 (MACRA) ended the Sustainable Growth Rate (SGR) formula, which would have resulted in a significant cut to payment rates for clinicians participating in Medicare. MACRA requires CMS by law to implement an incentive program for clinicians. This program, referred to as the Quality Payment Program, provides two participation pathways for clinicians: (1) The Merit-based Incentive Payment System (MIPS), and (2) Advanced Alternative Payment Models (Advanced APMs). MIPS combines three Medicare “legacy” programs – the Physician Quality Reporting System (PQRS), Value-based Payment Modifier (VM), and the Medicare EHR Incentive Program for Eligible Professionals – into a single program. Under MIPS, there are four connected performance categories that will affect a clinician’s future Medicare payments. Each performance category is scored independently and has a specific weight, indicating its contribution towards the MIPS Final Score. The MIPS performance categories and their 2018 weights towards the final score are: Quality (45%); Advancing Care information (25%); Improvement Activities (15%); and Cost (15%). The final score (100%) will be the basis for the MIPS payment adjustment assessed for MIPS eligible clinicians.

High Priority Domains for Future Measure Consideration:

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

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

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

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

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

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

Measure Requirements: CMS applies criteria for measures that may be considered for potential inclusion in the MIPS. At a minimum, the following criteria and requirements must be met for selection in the MIPS: CMS is statutorily required to select measures that reflect consensus among affected parties, and to the extent feasible, include measures set forth by one or more national consensus building entities. To the extent practicable, quality measures selected for inclusion on the final list will address at least one of the following quality domains: Effective Prevention and Treatment, Making Care Safer, Communication and Coordination of Care, Best Practices of Healthy Living, Making Care Affordable or Person and Family Engagement. In addition, before including a new measure in MIPS, CMS is required to submit for publication in an applicable specialty-appropriate, peer-reviewed journal the measure and the method for developing the measure, including clinical and other data supporting the measure. Measures implemented in MIPS may be available for public reporting on Physician Compare. Measures must be fully developed, with completed testing results at the clinician level and ready for implementation at the time of submission (CMS’ internal evaluation). Preference will be given to measures that are endorsed by the National Quality Forum (NQF). Measures should not duplicate other measures currently in the MIPS. Duplicative measures are assessed to see which would be the better measure for the MIPS measure set. Measure performance and evidence should identify opportunities for improvement. CMS does not intend to implement measures in which evidence identifies high levels of performance with little variation or opportunity for improvement, e.g., measures that are “topped out.” Claims measures must also be reportable via another data submission mechanism (e.g. registry, eCQM). MIPS is not accepting claims only measures. Section 101(c)(1) of the MACRA requires submission of new measures for publication in applicable specialty-appropriate, peer-reviewed journals prior to implementing in MIPS. The Peer-Review Journal template provided by CMS, must accompany each measures submission. Please see the template for additional information. eCQMs must meet EHR system infrastructure requirements, as defined by MIPS regulation. Beginning with calendar year 2019, eCQMs will use clinical quality language (CQL) as the expression logic used in the Health Quality Measure Format (HQMF). CQL replaces the logic expressions currently defined in the Quality Data Model (QDM). The data collection mechanisms must be able to transmit and receive requirements as identified in MIPS regulation. For example, eCQMs being submitted as Quality Reporting Data Architecture (QRDA) III must meet QRDA – III standards as defined in the CMS QRDA III Implementation Guide. eCQMs must have HQMF output from the Measure Authoring Tool (MAT), using MAT v5.4, or more recent, with implementation of the clinical quality language logic. Additional information on the MAT can be found at https://ecqi.healthit.gov/ecqm-tools/tool-library/measure-authoring-tool Bonnie test cases must accompany each measure submission. Additional information on eCQM Tools and resources can be found at https://ecqi.healthit.gov/ecqm-tools-key-resources. Reliability and validity testing must be conducted for measures. In addition to the above, feasibility testing must be conducted for eCQMs. Testing data must accompany submission. For example, if a measure is being reported as registry and eCQM, testing data for both versions must be submitted.

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

Program History and Structure: Section 3022 of the Affordable Care Act (ACA) requires the Centers for Medicare & Medicaid Services (CMS) to establish a Shared Savings Program that promotes accountability for a patient population, coordinates items and services under Medicare Parts A and B, and encourages investment in infrastructure and redesigned care processes for high-quality and efficient service delivery. The Medicare Shared Savings Program (Shared Savings Program) was designed to facilitate coordination and cooperation among providers to improve the quality of care for Medicare Fee-For-Service (FFS) beneficiaries and reduce the rate of growth in health care costs. Eligible providers, hospitals, and suppliers may voluntarily participate in the Shared Savings Program by creating or participating in an Accountable Care Organization (ACO). On December 31, 2018 CMS released the Medicare Shared Savings Program: Accountable Care Organizations – Pathways to Success final rule. “Pathways to Success” refers to a combination of policy changes which include: redesigning the participation options available under the program to encourage ACOs to transition to two-sided models (in which they may share in savings and are accountable for repaying shared losses); providing new tools to support coordination of care across settings and strengthen beneficiary engagement; ensuring rigorous benchmarking; promoting interoperable electronic health record technology among ACO providers/suppliers; and improving information sharing on opioid use to combat opioid addiction.

Measure Requirements: Specific measure requirements include: Outcome measures that address conditions that are high-cost and affect a high volume of Medicare patients. Measures that are targeted to the needs and gaps in care of Medicare fee-for-service patients and their caregivers. Measures that align with CMS quality reporting initiatives, such as the Quality Payment Program. Measures that support improved individual and population health. Measures addressing high-priority healthcare issues, such as opioid use. Measures that align with recommendations from the Core Quality Measures Collaborative.

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

Program History and Structure: Program Type: Quality Payment Program & Public Reporting Incentive Structure: Medicare Part C: Public reporting & Quality bonus payments—5% if 4 Stars or higher Medicare Part D: Public reporting Program Goal: Provide information about plan quality and performance indicators be provided to beneficiaries to help them make informed plan choices. Incentivize high performing plans (Part C).

High Priority Domains for Future Measure Consideration: Promote Effective Communication and Coordination of Care. A primary goal is to coordinate care for beneficiaries in the effort to provide quality care. The Medicare population includes a large number of individuals and older adults with high-risk multiple chronic conditions (MCC) who often receive care from multiple providers and settings and, as a result, are more likely to experience fragmented care and adverse healthcare outcomes. Promote Effective Prevention and Treatment of Chronic Disease. Medicare beneficiaries with multiple high-risk chronic conditions are at increased risk for fragmented care and poor health outcomes so attention to effectively preventing and treating chronic disease is important.

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

Program History and Structure: The Inpatient Rehabilitation Facilities Quality Reporting Program (IRF QRP) was established in accordance with section 1886(j) of the Social Security Act as amended by section 3004(b) of the Affordable Care Act. Inpatient Rehabilitation Facilities that receive the IRF Prospective Payment System (PPS) are required to participate in the IRF QRP (e.g., IRF hospitals, IRF units that are co-located with affiliated acute care facilities, and IRF units affiliated with critical access hospitals [CAHs]). Data sources for IRF QRP measures include Medicare FFS claims, the Center for Disease Control’s National Healthcare Safety Network (CDC NHSN) data submissions, and Inpatient Rehabilitation Facility - Patient Assessment instrument (IRFPAI) assessment data. The IRF QRP measure development and selection activities take into account established national priorities and input from multi-stakeholder groups. Beginning in FY 2014, IRFs that fail to submit data are subject to a 2.0 percentage point reduction of the applicable IRF PPS payment update. Public reporting of IRF QRP measures on IRF Compare (https://www.medicare.gov/inpatientrehabilitationfacilitycompare/) began in December 2016. Further, the Improving Medicare Post-Acute Care Transformation of 2014 (IMPACT Act) amends title XVIII (Medicare) of the Social Security Act (the Act) to direct the Secretary of the Department of Health and Human Services (HHS) to require Long-term Care Hospitals (LTCHs), Inpatient Rehabilitation Facilities (IRFs), Skilled Nursing Facilities (SNFs) and Home Health Agencies (HHAs) to report standardized patient assessment data, and data on quality measures including resource use measures. The IMPACT Act requires CMS to develop and implement quality measures to satisfy at least five measure domains: functional status, cognitive function, and changes in function and cognitive function; skin integrity and changes in skin integrity; medication reconciliation; incidence of major falls; and the transfer of health information when the individual transitions from the hospital/critical access hospital to PAC provider or home, or from PAC provider to another setting. The IMPACT Act also requires the implementation of resource use and other measures in satisfaction of at least these following domains: total estimated Medicare spending per beneficiary; discharge to the community; and all condition risk adjusted potentially preventable hospital readmission rates.

High Priority Domains for Future Measure Consideration:

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

CExchange of Electronic Health Information and Interoperability measure concept: CMS believes that IRF provider health information exchange supports the goals of high quality, personalized, and efficient healthcare, care coordination and person-centered care, and supports real-time, data driven, clinical decision making. The interoperability of health information across health care systems is key to achieving safe, efficient, and high-quality health care. It is also necessary for IRF patients/residents to fully participate in their health care.

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

Program History and Structure: The Skilled Nursing Facility Quality Reporting Program (SNF QRP) was established in accordance with the IMPACT Act of 2014, which amended 1888(e) of the SSA requiring data submission by SNFs. Skilled Nursing Facilities that submit data under the SNF PPS are required to participate in the SNF QRP, excluding units that are affiliated with critical access hospitals (CAHs). Data sources for SNF QRP measures include Medicare FFS claims as well as Minimum Data Set (MDS) assessment data. The SNF QRP measure development and selection activities take into account established national priorities and input from multistakeholder groups. Beginning in FY 2018, providers that fail to submit required quality data to CMS will have their annual updates reduced by 2.0 percentage points. Further, the IMPACT Act amends title XVIII (Medicare) of the Social Security Act (the Act) to direct the Secretary of the Department of Health and Human Services (HHS) to require Long-term Care Hospitals (LTCHs), Inpatient Rehabilitation Facilities (IRFs), Skilled Nursing Facilities (SNFs), and Home Health Agencies (HHAs) to report standardized patient assessment data, and data on quality measures including resource use measures. The IMPACT Act requires CMS to develop and implement quality measures to satisfy at least five measure domains: functional status, cognitive function, and changes in function and cognitive function; skin integrity and changes in skin integrity; medication reconciliation; incidence of major falls; and the transfer of health information when the individual transitions from the hospital/critical access hospital to PAC provider or home, or from PAC provider to another setting. The IMPACT Act also requires the implementation of resource use and other measures in satisfaction of at least these following domains: total estimated Medicare spending per beneficiary; discharge to the community; and all condition risk adjusted potentially preventable hospital readmission rates.

High Priority Domains for Future Measure Consideration:

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

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

High Priority Domains for Future Measure Consideration:

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