NQF

Version Number: 8.2
Meeting Date: December 8-9, 2016

Measure Applications Partnership
Hospital Workgroup Discussion Guide

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Agenda

Agenda Synopsis

Day 1  
8:30 am   Breakfast
9:00 am   Welcome, Introductions, Disclosures of Interest, and Review of Meeting Objectives
9:15 am   CMS Opening Remarks
9:45 am   NQF Strategic Plan
10:00 am   Overview of Pre-Rulemaking Approach
10:15 am   Overview of the End-Stage Renal Disease Quality Incentive Program (ESRD QIP) Program
10:25 am   Opportunity for Public Comment on Measures Under Consideration for End-Stage Renal Disease Quality Incentive Program (ESRD QIP)
10:35 am   Pre-Rulemaking Input Measure Sets on End-Stage Renal Disease Quality Incentive Program (ESRD QIP)—Consent Calendar 1
11:15 am   Break
11:30 am   Overview of the PPS-Exempt Cancer Hospital Quality Reporting (PCHQR) Program
11:40 am   Opportunity for Public Comment on Measures Under Consideration for PCHQR
11:50 am   Pre-Rulemaking Input for Prospective Payment System (PPS)-Exempt Cancer Hospital Quality Reporting (PCHQR)—Consent Calendar 2
12:30 pm   Lunch
1:00 pm   Overview of the Ambulatory Surgery Center Quality Reporting (ASCQR) Program
1:10 pm   Opportunity for Public Comment on Measures Under Consideration for ASCQR
1:20 pm   Pre-Rulemaking Input Ambulatory Surgical Center Quality Reporting (ASCQR)—Consent Calendar 3
1:50 pm   Overview of the Inpatient Psychiatric Facilities Quality Reporting (IPFQR) Program
2:00 pm   Opportunity for Public Comment on Measures Under Consideration for IPFQR
2:10 pm   Inpatient Psychiatric Facility Quality Reporting (IPFQR)—Consent Calendar 4
2:45 pm   Break
3:00 pm   Overview of the Hospital Outpatient Quality Reporting Program (HOQR)
3:10 pm   Opportunity for Public Comment on Measures Under Consideration for HOQR
3:20 pm   Pre-Rulemaking Input Measure on Hospital Outpatient Quality Reporting (OQR)—Consent Calendar 5
3:40 pm   Feedback on Current Measure Sets for ESRD QIP, PCHQR, ASCQR, IPFQR, Readmissions, HACs, and OQR
4:40 pm   Opportunity for Public Comment
4:55 pm   Summary of Day
5:00 pm   Adjourn
Day 2  
8:30 am   Breakfast
9:00 am   Welcome and Review of Day 1
9:15 am   PROMIS Discussion
10:15 am   Overview of the Hospital Inpatient Quality Reporting (HIQR) Program and Medicare and Medicaid EHR Incentive Program for Hospitals and Critical Access Hospitals (CAHs) (Meaningful Use)
10:25 am   Opportunity for Public Comment on Measures Under Consideration for HIQR and Medicare and Medicaid EHR Incentive Program for Hospitals and Critical Access Hospitals (CAHs) (Meaningful Use)
10:35 am   Pre-Rulemaking Input Measure Sets: Hospital Inpatient Quality Reporting (IQR) and Medicare and Medicaid EHR Incentive Program for Hospitals and Critical Access Hospitals (CAHs) (Meaningful Use)—Consent Calendar 6
11:00 am   Break
11:15 am   IQR Continued—Consent Calendar 7
   IQR Continued—Consent Calendar 8
12:30 pm   Lunch
1:00 pm   IQR Continued—Consent Calendar 9
2:00 pm   Feedback on Current Measure Sets for IQR and VBP
2:45 pm   Opportunity for Public Comment
2:55 pm   Wrap Up and Next Steps
3:00 pm   Adjourn


Full Agenda

Day 1  
8:30 am   Breakfast
9:00 am   Welcome, Introductions, Disclosures of Interest, and Review of Meeting Objectives
Cristie Upshaw Travis, MAP Hospital Workgroup Co-Chair Ronald Walters, MAP Hospital Workgroup Co-Chair Melissa Mariñelarena, Senior Director, NQF Ann Hammersmith, General Counsel, NQF

9:15 am   CMS Opening Remarks
Pierre Yong, Director, Quality Measurement and Value-Based Incentives Group, CMS

9:45 am   NQF Strategic Plan
Helen Burstin, Chief Scientific Officer, NQF

10:00 am   Overview of Pre-Rulemaking Approach
Melissa Mariñelarena, Senior Director, NQF Kate McQueston, Project Manager, NQF

10:15 am   Overview of the End-Stage Renal Disease Quality Incentive Program (ESRD QIP) Program
10:25 am   Opportunity for Public Comment on Measures Under Consideration for End-Stage Renal Disease Quality Incentive Program (ESRD QIP)
10:35 am   Pre-Rulemaking Input Measure Sets on End-Stage Renal Disease Quality Incentive Program (ESRD QIP)—Consent Calendar 1
Allen Nissenson, Kidney Care Partners Elizabeth Evans, Individual Subject Matter Expert
Programs under consideration:
  1. Hemodialysis Vascular Access: Long-term Catheter Rate (MUC ID: MUC16-309)
    • Description: Percentage of adult hemodialysis patient-months using a catheter continuously for three months or longer for vascular access. (Measure Specifications; Summary of NQF Endorsement Review)
    • Public comments received: 3
    • Preliminary analysis summary (Full Preliminary Analysis)
      • Contribution to program measure set:This measure is intended to replace the existing vascular access type measure in the ESRD QIP. The measure is currently under review by the Renal Standing Committee. The Standing Committee and CSAC recommended the measure for endorsement
      • Impact on quality of care for patients:This measure provides dialysis patients with information about the long-term use of catheters for vascular access.
    • Preliminary analysis result: Support for Rulemaking


  2. Hemodialysis Vascular Access: Standardized Fistula Rate (MUC ID: MUC16-308)
    • Description: Adjusted percentage of adult hemodialysis patient-months using an autogenous arteriovenous fistula (AVF) as the sole means of vascular access. (Measure Specifications; Summary of NQF Endorsement Review)
    • Public comments received: 2
    • Preliminary analysis summary (Full Preliminary Analysis)
      • Contribution to program measure set:This measure is intended to replace the existing vascular access type measure in the ESRD QIP. The measure is currently under review by the Renal Standing Committee. The Standing Committee and CSAC recommended the measure for endorsement.
      • Impact on quality of care for patients:This measure provides dialysis patients with information about the use of autogenous arteriovenous fistula (AVF) as the sole means of vascular access.
    • Preliminary analysis result: Support for Rulemaking


  3. Standardized Transfusion Ratio for Dialysis Facilities (MUC ID: MUC16-305)
    • Description: The risk adjusted facility level transfusion ratio “STrR” is specified for all adult dialysis patients. It is a ratio of the number of eligible red blood cell transfusion events observed in patients dialyzing at a facility, to the number of eligible transfusion events that would be expected under a national norm, after accounting for the patient characteristics within each facility. Eligible transfusions are those that do not have any claims pertaining to the comorbidities identified for exclusion, in the one year look back period prior to each observation window. (Measure Specifications; Summary of NQF Endorsement Review)
    • Public comments received: 2
    • Preliminary analysis summary (Full Preliminary Analysis)
      • Contribution to program measure set:This measure has undergone substantial changes but details of the changes to the measure are not provided. The measure is currently under review by the Renal Standing Committee. The Standing Committee and CSAC recommended the measure for endorsement.
      • Impact on quality of care for patients:This measure encourages dialysis facilities to avoid blood transfusions when managing patients with anemia.
    • Preliminary analysis result: Support for Rulemaking


11:15 am   Break
11:30 am   Overview of the PPS-Exempt Cancer Hospital Quality Reporting (PCHQR) Program
11:40 am   Opportunity for Public Comment on Measures Under Consideration for PCHQR
11:50 am   Pre-Rulemaking Input for Prospective Payment System (PPS)-Exempt Cancer Hospital Quality Reporting (PCHQR)—Consent Calendar 2
R. Sean Morrison, Individual Subject Matter Expert Sarah Nolan, Service Employees International Union Heather Lewis, Geisinger Health System
Programs under consideration: Prospective Payment System-Exempt Cancer Hospital Quality Reporting Program
  1. PRO utilization in in non-metastatic prostate cancer patients (MUC ID: MUC16-393)
    • Description: Use of a validated patient-reported outcome (PRO) instrument to measure functional status in adult, non-metastatic prostate cancer patients during the 12-month measurement period. (Measure Specifications)
    • Public comments received: 2
    • Preliminary analysis summary (Full Preliminary Analysis)
      • Contribution to program measure set:It is unclear if the value of this measure to patients/consumers outweighs the burden of implementation. There is limited information regarding how the measure can be operationalized and the measure is not fully specified and tested.
      • Impact on quality of care for patients:This measure would encourage facilities measure functional status in adult patients with non-metastatic prostate cancer using a validated survey instrument.
    • Preliminary analysis result: Do Not Support for Rulemaking


  2. Proportion of patients who died from cancer admitted to hospice for less than 3 days (MUC ID: MUC16-274)
    • Description: Proportion of patients who died from cancer admitted to hospice for less than 3 days (Measure Specifications; Summary of NQF Endorsement Review)
    • Public comments received: 2
    • Preliminary analysis summary (Full Preliminary Analysis)
      • Contribution to program measure set:The measure is not specified and tested at the facility level in the hospital setting. The Palliative Care and End-of-Life Standing Committee, CSAC and the NQF Executive Committee recommended the measure for endorsement at the group/clinician level in the ambulatory care setting. The measure should be specified, tested and NQF endorsed at the facility level in the hospital setting for the PPS-Exempt Cancer Hospital Quality Reporting Program.
      • Impact on quality of care for patients:This measure provides the proportion of patients who died from cancer and were admitted to hospice for less than 3 days.
    • Preliminary analysis result: Refine and Resubmit


  3. Proportion of patients who died from cancer admitted to the ICU in the last 30 days of life (MUC ID: MUC16-273)
    • Description: Proportion of patients who died from cancer admitted to the ICU in the last 30 days of life (Measure Specifications; Summary of NQF Endorsement Review)
    • Public comments received: 1
    • Preliminary analysis summary (Full Preliminary Analysis)
      • Contribution to program measure set:The measure has not been specified and tested at the facility level in the hospital setting. The Palliative Care and End-of-Life Standing Committee, CSAC and the NQF Executive Committee recommended the measure for endorsement at the group/clinician level in the ambulatory care setting. The measure should be specified, tested and NQF endorsed at the facility level in the hospital setting for the PPS-Exempt Cancer Hospital Quality Reporting Program.
      • Impact on quality of care for patients:This measure provides the proportion of patients who died from cancer and were admitted to the ICU in the last 30 days of life.
    • Preliminary analysis result: Support for Rulemaking


  4. Proportion of patients who died from cancer not admitted to hospice (MUC ID: MUC16-275)
    • Description: Proportion of patients who died from cancer not admitted to hospice (Measure Specifications; Summary of NQF Endorsement Review)
    • Public comments received: 3
    • Preliminary analysis summary (Full Preliminary Analysis)
      • Contribution to program measure set:The measure is not specified and tested at the facility level in the hospital setting. The Palliative Care and End-of-Life Standing Committee, CSAC and the NQF Executive Committee recommended the measure for endorsement at the group/clinician level in the ambulatory care setting. The measure should be specified, tested and NQF endorsed at the facility level in the hospital setting for the PPS-Exempt Cancer Hospital Quality Reporting Program.
      • Impact on quality of care for patients:This measure provides the proportion of patients who died from cancer and were not admitted to hospice.
    • Preliminary analysis result: Refine and Resubmit for Rulemaking


  5. Proportion of patients who died from cancer receiving chemotherapy in the last 14 days of life (MUC ID: MUC16-271)
    • Description: Proportion of patients who died from cancer receiving chemotherapy in the last 14 days of life (Measure Specifications; Summary of NQF Endorsement Review)
    • Public comments received: 1
    • Preliminary analysis summary (Full Preliminary Analysis)
      • Contribution to program measure set:The measure has not been specified and tested at the facility level in the hospital setting. The Palliative Care and End-of-Life Standing Committee, CSAC and the NQF Executive Committee recommended the measure for endorsement at the group/clinician level in the ambulatory care setting. The measure should be specified, tested and NQF endorsed at the facility level in the hospital setting for the PPS-Exempt Cancer Hospital Quality Reporting Program.
      • Impact on quality of care for patients:The measure provides patients with the proportion of cancer patients who receive chemotherapy in the last 14 days of life.
    • Preliminary analysis result: Refine and Resubmit


12:30 pm   Lunch
1:00 pm   Overview of the Ambulatory Surgery Center Quality Reporting (ASCQR) Program
1:10 pm   Opportunity for Public Comment on Measures Under Consideration for ASCQR
1:20 pm   Pre-Rulemaking Input Ambulatory Surgical Center Quality Reporting (ASCQR)—Consent Calendar 3
Jeff Jacobs, The Society of Thoracic Surgeons Marisa Valdes, Baylor Scott & White Health
Programs under consideration: Ambulatory Surgical Center Quality Reporting Program
  1. Ambulatory Breast Procedure Surgical Site Infection (SSI) Outcome Measure (MUC ID: MUC16-155)
    • Description: This measure is for the risk-adjusted Standardized Infection Ratio (SIR) for all Surgical Site Infections (SSIs) following breast procedures conducted at ambulatory surgery centers (ASCs) among adult patients (ages 18 - 108 years) and reported to the Centers for Disease Control and Prevention (CDC) National Healthcare Safety Network (NHSN). The measure compares the reported number of surgical site infections observed at an ASC with a predicted value based on nationally aggregated data. The measure was developed collaboratively by the CDC, the Ambulatory Surgery Center Quality Collaboration (ASC QC), and the Colorado Department of Public Health and Environment. CDC is the measure steward. (Measure Specifications)
    • Public comments received: 1
    • Preliminary analysis summary (Full Preliminary Analysis)
      • Contribution to program measure set:This is a fully developed measure and is currently under review by the Patient Safety Standing Committee for NQF endorsement. The Committee recommended the measure for endorsement. The measure should complete the consensus development process (CDP) and receive NQF endorsement.
      • Impact on quality of care for patients:Improved care and a decrease in the number of surgical site infections (SSIs) for patients undergoing breast procedures at ambulatory surgical care centers.
    • Preliminary analysis result: Conditional Support for Rulemaking


  2. Hospital Visits after Orthopedic Ambulatory Surgical Center Procedures (MUC ID: MUC16-152)
    • Description: **As of 12/2 testing for this measure has been completed**** The measure score is an ASC-level rate of unplanned hospital visits within 7 days of an orthopedic procedure performed at an ASC. (Measure Specifications)
    • Public comments received: 1
    • Preliminary analysis summary (Full Preliminary Analysis)
      • Contribution to program measure set:This measure is fully developed and specified andaligns with NQF #2539: Rate of Risk-Standardized, All-Cause, Unplanned Hospital Visits within 7 Days of an Outpatient Colonoscopy Among Medicare Fee-for-Service (FFS) Patients Aged 65 Years and Older and MUC16-153: Hospital Visits following Urology Ambulatory Surgical Center Procedures. Testing results should demonstrate reliability and validity at the facility level in the ambulatory surgical setting. This measure should be submitted to NQF for review and endorsement.
      • Impact on quality of care for patients:Improved care, care transitions and minimal unplanned hospital visits within 7 days following orthopedic procedures performed in the ambulatory surgical care setting.
    • Preliminary analysis result: Refine and Resubmit Prior to Rulemaking


  3. Hospital Visits after Urology Ambulatory Surgical Center Procedures (MUC ID: MUC16-153)
    • Description: **As of 12/2 testing for this measure has been completed**** The measure score is an ASC-level rate of unplanned hospital visits within 7 days of a urology procedure performed at an ASC. (Measure Specifications)
    • Public comments received: 1
    • Preliminary analysis summary (Full Preliminary Analysis)
      • Contribution to program measure set:This measure is fully developed and specified andaligns with NQF #2539: Rate of Risk-Standardized, All-Cause, Unplanned Hospital Visits within 7 Days of an Outpatient Colonoscopy Among Medicare Fee-for-Service (FFS) Patients Aged 65 Years and Older and MUC16-152: Hospital Visits following Orthopedic Ambulatory Surgical Center Procedures. Testing results should demonstrate reliability and validity at the facility level in the ambulatory surgical setting. This measure should be submitted to NQF for review and endorsement.
      • Impact on quality of care for patients:Improved care, care transitions and minimal unplanned hospital visits within 7 days following urology procedures performed in the ambulatory surgical care setting.
    • Preliminary analysis result: Refine and Resubmit Prior to Rulemaking


1:50 pm   Overview of the Inpatient Psychiatric Facilities Quality Reporting (IPFQR) Program
2:00 pm   Opportunity for Public Comment on Measures Under Consideration for IPFQR
2:10 pm   Inpatient Psychiatric Facility Quality Reporting (IPFQR)—Consent Calendar 4
Frank Ghinassi, National Association of Psychiatric Health Systems (NAPHS) Ann Marie Sullivan, Individual Subject Matter Expert Woody Eisenberg, Pharmacy Quality Alliance
Programs under consideration: Inpatient Psychiatric Facility Quality Reporting Program
  1. Medication Continuation following Inpatient Psychiatric Discharge (MUC ID: MUC16-048)
    • Description: **As of 12/2 testing for this measure has been completed**** This measure assesses whether psychiatric patients admitted to an inpatient psychiatric facility (IPF) for major depressive disorder (MDD), schizophrenia, or bipolar disorder (BD) were dispensed a prescription for evidence-based medication within 30 days of discharge. The performance period for the measure is two years. (Measure Specifications)
    • Public comments received: 0
    • Preliminary analysis summary (Full Preliminary Analysis)
      • Contribution to program measure set:This measure is fully developed, specified and undergoing testing. The testing results should demonstrate reliability and validity at the facility level in the hospital setting. The measure should be submitted to NQF for review and endorsement.
      • Impact on quality of care for patients:This measure encourages inpatient psychiatric facilities to ensure that patients with major depressive disorder (MDD), schizophrenia, or bipolar disorder (BD) are dispensed a prescription for evidence-based medication within 30 days of discharge.
    • Preliminary analysis result: Refine and Resubmit Prior to Rulemaking


  2. Identification of Opioid Use Disorder (MUC ID: MUC16-428)
    • Description: The measure assesses the percentage of patients admitted to an inpatient psychiatric facility who were screened and evaluated for opioid use disorder. (Measure Specifications)
    • Public comments received: 4
    • Preliminary analysis summary (Full Preliminary Analysis)
      • Contribution to program measure set:This measure is fully developed, specified and undergoing field testing. The testing results should demonstrate reliability and validity at the facility level in the hospital setting. This measure should be submitted to NQF for review and endorsement.
      • Impact on quality of care for patients:This measure encourages inpatient psychiatric facilities to screen patients for an opioid use disorder.
    • Preliminary analysis result: Refine and Resubmit Prior to Rulemaking


  3. Medication Reconciliation at Admission (MUC ID: MUC16-049)
    • Description: **As of 12/2 testing for this measure has been completed**** ****Changed from requiring reconciliation within 24 hours to requiring reconciliation within 48 hours as of 12/1/16**** This measure assesses the average completeness of medication reconciliations conducted within 24 hours of admission to an inpatient facility. (Measure Specifications)
    • Public comments received: 0
    • Preliminary analysis summary (Full Preliminary Analysis)
      • Contribution to program measure set:This measure is fully developed, specified and undergoing field testing. The testing results should demonstrate reliability and validity at the facility level in the hospital setting. This measure should be submitted to NQF for review and endorsement.
      • Impact on quality of care for patients:This measure encourages inpatient psychiatric facilities to complete adequate medication reconciliation within 24 hours of admission.
    • Preliminary analysis result: Refine and Resubmit Prior to Rulemaking


2:45 pm   Break
3:00 pm   Overview of the Hospital Outpatient Quality Reporting Program (HOQR)
3:10 pm   Opportunity for Public Comment on Measures Under Consideration for HOQR
3:20 pm   Pre-Rulemaking Input Measure on Hospital Outpatient Quality Reporting (OQR)—Consent Calendar 5
Lee Fleisher, Individual Subject Matter Expert Jack Jordan, Individual Subject Matter Expert
Programs under consideration: Hospital Outpatient Quality Reporting Program
  1. Median Time from ED Arrival to ED Departure for Discharged ED Patients (MUC ID: MUC16-055)
    • Description: Median elapsed time from emergency department arrival to emergency room departure for patients discharged from the emergency department (Measure Specifications)
    • Public comments received: 7
    • Preliminary analysis summary (Full Preliminary Analysis)
      • Contribution to program measure set:This eMeasure is fully developed, tested and currently implemented in IQR. This eMeasure uses EHR data rather than chart abstracted data to determine patient arrival and discharge times in the emergency department. Testing data should be provided demonstrating that this eMeasure more accurately determines patient arrival and discharge times compared to the chart abstracted version of the measure (NQF #0496) currently in the HOQR and HIQR programs. This eMeasure should be submitted to NQF for review and endorsement.
      • Impact on quality of care for patients:Reducing the median time from ED arrival to the time of departure from the emergency room potentially improves access to care specific to the patient condition and increases the capability to provide additional treatment.
    • Preliminary analysis result: Conditional Support for Rulemaking


  2. Median Time to Pain Management for Long Bone Fracture (MUC ID: MUC16-056)
    • Description: Median time from emergency department arrival to time of initial oral, nasal or parenteral pain medication administration for emergency department patients with a principal diagnosis of long bone fracture (LBF) (Measure Specifications; Summary of NQF Endorsement Review)
    • Public comments received: 2
    • Preliminary analysis summary (Full Preliminary Analysis)
      • Contribution to program measure set:The measure is currently in the Hospital Outpatient Quality Reporting (HOQR) program. The NQF Musculoskeletal Steering Committee agreed that the evidence supporting this measure is insufficient. The measure was de-endorsed in 2014.
      • Impact on quality of care for patients:This measure captures the median time to pain medication administration for long bone fractures for patients in the ED. Median time for pain medication administration does not indicate adequate pain management in the ED related to long bone fractures.
    • Preliminary analysis result: Do Not Support for Rulemaking


  3. Safe Use of Opioids – Concurrent Prescribing (MUC ID: MUC16-167)
    • Description: Patients age 18 years and older with active, concurrent prescriptions for opioids at discharge, or patients with active, concurrent prescriptions for an opioid and benzodiazepine at discharge from a hospital-based encounter (inpatient, ED, outpatient) (Measure Specifications)
    • Public comments received: 4
    • Preliminary analysis summary (Full Preliminary Analysis)
      • Contribution to program measure set:This newly developed eMeasure was tested at the facility level in the emergency department setting and currently undergoing field testing. The testing results should demonstrate reliability and validity in the outpatient setting for HOQR. The measure should be submitted to NQF for review and endorsement.
      • Impact on quality of care for patients:This measure encourages outpatient facilities to identify patients discharged with concurrent prescriptions of opioids or opioids and benzodiazepines and discourage prescriptions for two or more different opioids or opioids and benzodiazepines concurrently.
    • Preliminary analysis result: Refine and Resubmit Prior to Rulemaking


3:40 pm   Feedback on Current Measure Sets for ESRD QIP, PCHQR, ASCQR, IPFQR, Readmissions, HACs, and OQR
4:40 pm   Opportunity for Public Comment
4:55 pm   Summary of Day
Kate McQueston, Project Manager

5:00 pm   Adjourn
Day 2  
8:30 am   Breakfast
9:00 am   Welcome and Review of Day 1
Cristie Upshaw Travis, MAP Hospital Workgroup Co-Chair; Ronald Walters, MAP Hospital Workgroup Co-Chair; Melissa Mariñelarena, Senior Director, NQF

9:15 am   PROMIS Discussion
Ashley Wilder Smith, PhD, MPH, National Cancer Institute

10:15 am   Overview of the Hospital Inpatient Quality Reporting (HIQR) Program and Medicare and Medicaid EHR Incentive Program for Hospitals and Critical Access Hospitals (CAHs) (Meaningful Use)
10:25 am   Opportunity for Public Comment on Measures Under Consideration for HIQR and Medicare and Medicaid EHR Incentive Program for Hospitals and Critical Access Hospitals (CAHs) (Meaningful Use)
10:35 am   Pre-Rulemaking Input Measure Sets: Hospital Inpatient Quality Reporting (IQR) and Medicare and Medicaid EHR Incentive Program for Hospitals and Critical Access Hospitals (CAHs) (Meaningful Use)—Consent Calendar 6
Marsha Manning, University of Michigan David Engler, America's Essential Hospitals Jennifer Eames Huff, Mothers Against Medical Error
Programs under consideration:
  1. Alcohol & Other Drug Use Disorder Treatment Provided or Offered at Discharge and Alcohol & Other Drug Use Disorder Treatment at Discharge (MUC ID: MUC16-180)
    • Description: The measure is reported as an overall rate which includes all hospitalized patients 18 years of age and older to whom alcohol or drug use disorder treatment was provided, or offered and refused, at the time of hospital discharge, and a second rate, a subset of the first, which includes only those patients who received alcohol or drug use disorder treatment at discharge. The Provided or Offered rate (SUB-3) describes patients who are identified with alcohol or drug use disorder who receive or refuse at discharge a prescription for FDA-approved medications for alcohol or drug use disorder, OR who receive or refuse a referral for addictions treatment. (Measure Specifications; Summary of NQF Endorsement Review)
    • Public comments received: 3
    • Preliminary analysis summary (Full Preliminary Analysis)
      • Contribution to program measure set:This measure is NQF-endorsed at the facility level in the hospital/acute care setting. This measure is currently in the IPFQR program. However, no scientific evidence provided to demonstrate that patients who received a prescription at discharge for the treatment of alcohol or drug use disorder or a referral for addictions treatment received treatment after discharge.
      • Impact on quality of care for patients:This measure encourages hospitals to provide patients with a prescription for the treatment of alcohol or drug use disorder or a referral for addictions treatment.
    • Preliminary analysis result: Do Not Support for Rulemaking


  2. Alcohol Use Brief Intervention Provided or Offered and Alcohol Use Brief Intervention (MUC ID: MUC16-178)
    • Description: The measure is reported as an overall rate which includes all hospitalized patients 18 years of age and older to whom a brief intervention was provided, or offered and refused, and a second rate, a subset of the first, which includes only those patients who received a brief intervention. The Provided or Offered rate (SUB-2), describes patients who screened positive for unhealthy alcohol use who received or refused a brief intervention during the hospital stay. The Alcohol Use Brief Intervention (SUB-2a) rate describes only those who received the brief intervention during the hospital stay. Those who refused are not included. These measures are intended to be used as part of a set of 4 linked measures addressing Substance Use (SUB-1 Alcohol Use Screening ; SUB-2 Alcohol Use Brief Intervention Provided or Offered; SUB-3 Alcohol and Other Drug Use Disorder Treatment Provided or Offered at Discharge; SUB-4 Alcohol and Drug Use: Assessing Status after Discharge [temporarily suspended]). (Measure Specifications; Summary of NQF Endorsement Review)
    • Public comments received: 3
    • Preliminary analysis summary (Full Preliminary Analysis)
      • Contribution to program measure set:This measure is NQF-endorsed at the facility level in the hospital/acute care setting. This measure is currently in the IPFQR program; no implementation issues have been identified.
      • Impact on quality of care for patients:This measure encourages hospitals to provide brief interventions to patients with unhealthy alcohol use.
    • Preliminary analysis result: Support for Rulemaking


  3. Alcohol Use Screening (MUC ID: MUC16-179)
    • Description: Hospitalized patients 18 years of age and older who are screened within the first three days of admission using a validated screening questionnaire for unhealthy alcohol use. This measure is intended to be used as part of a set of 4 linked measures addressing Substance Use (SUB-1 Alcohol Use Screening; SUB-2 Alcohol Use Brief Intervention Provided or Offered; SUB-3 Alcohol and Other Drug Use Disorder Treatment Provided or Offered at Discharge; SUB-4 Alcohol and Drug Use: Assessing Status after Discharge [temporarily suspended]). (Measure Specifications; Summary of NQF Endorsement Review)
    • Public comments received: 4
    • Preliminary analysis summary (Full Preliminary Analysis)
      • Contribution to program measure set:This measure is NQF-endorsed at the facility level in the hospital/acute care setting. This measure is currently in the IPFQR program and publicly reported on Hospital Compare. No implementation issues have been identified.
      • Impact on quality of care for patients:This measure encourages hospitals to screen patients for unhealthy alcohol use.
    • Preliminary analysis result: Support for Rulemaking


  4. Patient Panel Smoking Prevalence IQR (MUC ID: MUC16-068)
    • Description: Percentage of hospital patient panel who currently smoke according to the EHR structured data (Measure Specifications)
    • Public comments received: 1
    • Preliminary analysis summary (Full Preliminary Analysis)
      • Contribution to program measure set:This measure is not fully developed and tested in the acute inpatient setting. A more comprehensive measure, MUC16-50 Tobacco Use Screening (TOB-1), that likely captures smoking status has been proposed for IQR.
      • Impact on quality of care for patients:A more comprehensive measure that focuses on tobacco screening improves the quality of tobacco-cessation interventions patients receive while hospitalized.
    • Preliminary analysis result: Do Not Support for Rulemaking


11:00 am   Break
11:15 am   IQR Continued—Consent Calendar 7
Kimberly Glassman, Nursing Alliance for Quality CareNancy Foster, American Hospital Association Martin Hatlie, Project Patient Care Mimi Huizinga, Premier, Inc.
Programs under consideration:
  1. Follow-Up After Hospitalization for Mental Illness (MUC ID: MUC16-165)
    • Description: The percentage of discharges for patients 6 years of age and older who were hospitalized for treatment of selected mental illness diagnoses and who had an outpatient visit, an intensive outpatient encounter or partial hospitalization with a mental health practitioner. Two rates are reported: - The percentage of discharges for which the patient received follow-up within 30 days of discharge - The percentage of discharges for which the patient received follow-up within 7 days of discharge. (Measure Specifications; Summary of NQF Endorsement Review)
    • Public comments received: 0
    • Preliminary analysis summary (Full Preliminary Analysis)
      • Contribution to program measure set:This measure, NQF #0576, is specified and tested at the health plan level; therefore, performance on the measure cannot be attributed to the facility as currently specified. Additionally, problems encountered with the initial measure results in the IPFQR program should be resolved prior to implementing the measure in additional programs.
      • Impact on quality of care for patients:This measure can help bridge the gap between the inpatient setting and outpatient treatment services for individuals with serious mental illness.
    • Preliminary analysis result: Refine and Resubmit Prior to Rulemaking


  2. Measure of Quality of Informed Consent Documents for Hospital-Performed, Elective Procedures (MUC ID: MUC16-262)
    • Description: The measure estimates the hospital-level quality of informed consent documents for elective procedures for fee-for-service (FFS) Medicare patients. The outcome is defined as the quality of the informed consent document, as evaluated using an instrument developed for this purpose, the Abstraction Tool. A sample of hospitals’ informed consent documents are evaluated and hospital-level performance will be derived by aggregating these individual informed consent document quality scores. The measure is broadly applicable to a range of procedures, including elective cardiac, orthopedic, and urological procedures, that are performed in the hospital. (Measure Specifications)
    • Public comments received: 1
    • Preliminary analysis summary (Full Preliminary Analysis)
      • Contribution to program measure set:The measure is the first step towards improving the practice of informed consent through quality measurement, and may compliment or serve as a platform for other measures of high-quality, patient-centered decision making. The reliability testing results should demonstrate hospital-level reliability. In addition, the measure should be submitted to NQF for review and endorsement.
      • Impact on quality of care for patients:Consistent and patient-centered standards based on existing guidelines for informed consent can lead to improved patient autonomy, patient safety, and high-quality decision making.
    • Preliminary analysis result: Refine and Resubmit Prior to Rulemaking


IQR Continued—Consent Calendar 8
Brock Slabach, National Rural Health Association Andrea Benin, Children's Hospital Association Wei Ying, Blue Cross Blue Shield of Massachusetts Karen Shehade, Medtronic-Minimally Invasive Therapy Group
Programs under consideration:
  1. Appropriate Documentation of a Malnutrition Diagnosis (MUC ID: MUC16-344)
    • Description: Appropriate documentation of a malnutrition diagnosis for patients age 65 and older admitted to inpatient care who are found to be malnourished based on a nutrition assessment. (Measure Specifications)
    • Public comments received: 3
    • Preliminary analysis summary (Full Preliminary Analysis)
      • Contribution to program measure set:This measure is currently under review in NQF’s Health and Well-Being 2015-2017 project. The Standing Committee agreed that the evidence provided to support the measure was not sufficient. The measure did not pass the Evidence criterion and was not recommended for NQF endorsement.
      • Impact on quality of care for patients:This measure encourages documentation of a malnutrition diagnosis for patients = 65 years who have a completed nutrition assessment in their medical record.
    • Preliminary analysis result: Do Not Support for Rulemaking


  2. Completion of a Malnutrition Screening within 24 Hours of Admission (MUC ID: MUC16-294)
    • Description: Completion of a malnutrition screening using a validated screening tool to determine if a patient is at-risk for malnutrition, within 24 hours of admission to the hospital. (Measure Specifications)
    • Public comments received: 4
    • Preliminary analysis summary (Full Preliminary Analysis)
      • Contribution to program measure set:This measure is currently under review in NQF’s Health and Well-Being 2015-2017 project. The Standing Committee did not reach consensus on the Evidence Criterion during the in-person meeting in September. The measure must pass the evidence criterion and be recommended for endorsement.
      • Impact on quality of care for patients:This measure encourages documentation of a malnutrition screening within 24 hours for patients >18 years admitted into the acute inpatient care setting, which is the first step in nutrition care.
    • Preliminary analysis result: Conditional Support for Rulemaking


  3. Completion of a Nutrition Assessment for Patients Identified as At-Risk for Malnutrition within 24 Hours of a Malnutrition Screening (MUC ID: MUC16-296)
    • Description: Patients age 65 years and older identified as at-risk for malnutrition based on a malnutrition screening who have a nutrition assessment documented in the medical record within 24 hours of the most recent malnutrition screening. (Measure Specifications)
    • Public comments received: 2
    • Preliminary analysis summary (Full Preliminary Analysis)
      • Contribution to program measure set:This measure is currently under review in NQF’s Health and Well-Being 2015-2017 project. The Standing Committee did not reach consensus on the Evidence Criterion during the in-person meeting in September. The measure must pass the evidence criterion and be recommended for endorsement.
      • Impact on quality of care for patients:This measure encourages documentation of a malnutrition assessment within 24 hours of the most recent malnutrition screening for patients = 65 years so that a dietitian can subsequently recommend a nutrition care plan that includes appropriate interventions to address the patient's malnutrition.
    • Preliminary analysis result: Conditional Support for Rulemaking


  4. Nutrition Care Plan for Patients Identified as Malnourished after a Completed Nutrition Assessment (MUC ID: MUC16-372)
    • Description: Documentation of a nutrition care plan for those patients age 65 and older admitted to inpatient care who are found to be malnourished based on a completed nutrition assessment (Measure Specifications)
    • Public comments received: 14
    • Preliminary analysis summary (Full Preliminary Analysis)
      • Contribution to program measure set:This measure is currently under review in NQF’s Health and Well-Being 2015-2017 project. The Standing Committee did not reach consensus on the Validity Criterion during the in-person meeting in September. The measure must pass the validity criterion and be recommended for endorsement.
      • Impact on quality of care for patients:This measure encourages documentation of a nutrition care plan for patients = 65 years with a finding of malnutrition. The Nutrition care plan may include completed assessment results, treatment goals, prioritization based on treatment severity, prescribed treatment/intervention, identification of members of the care team and a timeline for patient follow-up.
    • Preliminary analysis result: Conditional Support for Rulemaking


12:30 pm   Lunch
1:00 pm   IQR Continued—Consent Calendar 9
Gregory Alexander, Individual Subject Matter Expert Lindsey Wisham, Individual Subject Matter Expert Lee Fleisher, Individual Subject Matter Expert Jack Jordan, Individual Subject Matter Expert
Programs under consideration:
  1. Safe Use of Opioids – Concurrent Prescribing (MUC ID: MUC16-167)
    • Description: Patients age 18 years and older with active, concurrent prescriptions for opioids at discharge, or patients with active, concurrent prescriptions for an opioid and benzodiazepine at discharge from a hospital-based encounter (inpatient, ED, outpatient) (Measure Specifications)
    • Public comments received: 2
    • Preliminary analysis summary (Full Preliminary Analysis)
      • Contribution to program measure set:This newly developed eMeasure was tested at the facility level in the emergency department setting and currently undergoing field testing. The testing results should demonstrate reliability and validity in the hospital setting for IQR. The measure should be submitted to NQF for review and endorsement.
      • Impact on quality of care for patients:This measure encourages hospitals to identify patients discharged with concurrent prescriptions of opioids or opioids and benzodiazepines and discourage prescriptions for two or more different opioids or opioids and benzodiazepines concurrently.
    • Preliminary analysis result: Refine and Resubmit Prior to Rulemaking


  2. Influenza Immunization (IMM-2) (MUC ID: MUC16-053)
    • Description: Inpatients age 6 months and older discharged during October, November, December, January, February or March who are screened for influenza vaccine status and vaccinated prior to discharge if indicated. (Measure Specifications)
    • Public comments received: 1
    • Preliminary analysis summary (Full Preliminary Analysis)
      • Contribution to program measure set:The Health and Well-Being Standing Committee acknowledged the importance of this hospital-based measure, but did not believe the narrowing performance gaps were clinically significant in the chart-abstracted version of the measure (#1659). No data/evidence provided demonstrating that this eMeasure addresses a performance gap in IQR.
      • Impact on quality of care for patients:Approximately 94.0% of acute-care hospitalized patients are screened and vaccinated for influenza prior to discharge based on data from the chart-abstracted version of this measure (#1659). No evidence was provided that this eMeasure will increase the percentage of patients receiving influenza vaccine.
    • Preliminary analysis result: Do Not Support for Rulemaking


  3. Tobacco Use Screening (TOB-1) (MUC ID: MUC16-050)
    • Description: This measure assesses the proportion of hospitalized adult patients who were comprehensively screened (or refused screening) within 3 days prior through 1 day after admission for tobacco use within the 30 days prior to the screening. (Measure Specifications)
    • Public comments received: 1
    • Preliminary analysis summary (Full Preliminary Analysis)
      • Contribution to program measure set:This eMeasure is fully developed and specified at the facility level in the hospital setting. The measure is undergoing field testing. The testing results should demonstrate reliability and validity in the acute care setting. The eMeasure should be submitted to NQF for review and endorsement.
      • Impact on quality of care for patients:This measure encourages hospitals to ask all patients if they use tobacco and document their tobacco use status on a regular basis.
    • Preliminary analysis result: Refine and Resubmit Prior to Rulemaking


  4. Use of Antipsychotics in Older Adults in the Inpatient Hospital Setting (MUC ID: MUC16-041)
    • Description: Proportion of inpatient hospitalizations for patients 65 years of age and older who do not demonstrate a threat to themselves or others but who receive antipsychotic medication therapy. (Measure Specifications)
    • Public comments received: 0
    • Preliminary analysis summary (Full Preliminary Analysis)
      • Contribution to program measure set:This newly developed eMeasure is fully developed and specified. The measure is currently undergoing field testing. The testing results should demonstrate reliability and validity at the facility level in the hospital setting. In addition, the measure should be submitted to NQF for review and endorsement.
      • Impact on quality of care for patients:This measure encourages hospitals against using antipsychotics as a standard first line of treatment for patients experiencing aggressive behavior unless they present a threat to themselves or their caregivers.
    • Preliminary analysis result: Refine and Resubmit Prior to Rulemaking


2:00 pm   Feedback on Current Measure Sets for IQR and VBP
2:45 pm   Opportunity for Public Comment
2:55 pm   Wrap Up and Next Steps
Kate McQueston, Project Manager

3:00 pm   Adjourn

Appendix A: Measure Information

Measure Index

Ambulatory Surgical Center Quality Reporting Program

Hospital Inpatient Quality Reporting and EHR Incentive Program

Hospital Outpatient Quality Reporting Program

Hospital Value-Based Purchasing Program

Inpatient Psychiatric Facility Quality Reporting Program

Prospective Payment System-Exempt Cancer Hospital Quality Reporting Program


Full Measure Information

Ambulatory Breast Procedure Surgical Site Infection (SSI) Outcome Measure (Program: Ambulatory Surgical Center Quality Reporting Program; MUC ID: MUC16-155)

Measure Specifications

Preliminary Analysis of Measure

Rationale for measure provided by HHS
Breast SSIs contribute a substantial portion of SSI in inpatient settings, and also have the one of the highest risk of any procedure type in outpatient settings. In the Netherlands, the rate of SSI following mastectomies in 2006 was 61% as determined by a study in 2006 (Mannien, 2006). A case control study performed in 2004 reported SSI rates following breast surgeries to be 25.8% (Vilar-Compte, 2004). One study of breast SSI risk in an HOPD reported an overall risk of 5.2%, with procedure-specific risks of 12.4% following mastectomy with immediate implant reconstruction, 6.2% following mastectomy with immediate reconstruction using a transverse rectus abdominis myocutaneous flap, 4.4% following mastectomy only, and 1.1% following breast reduction surgery (Olsen, 2008). Another study of SSI following breast cancer-related procedures reported a risk of 18.9% (Vilar-Compte, 2009). The cost incurred by each breast SSI attributable to the SSI was estimated by one analysis to be $4,901 per patient (Olsen, 2008). Though these estimates of risk vary from 1% to over 30% depending on procedure type, sample population, and definition of SSI, it is clear that breast procedure-related SSIs are a large burden to outpatient healthcare facilities. From 1980-1995, a significant trend in surgery was the transition from inpatient settings to outpatient ambulatory surgery settings due to advances in surgical techniques and economic incentives for ambulatory surgery (Kozak, 1999). In the current literature, the rates of SSI in ambulatory surgery centers is relatively low—however, aggregate numbers of infections can still cause a substantial burden, as those often result in post-surgical visits and morbidity. ASCs have been shown to have a lower SSI rate than inpatient settings; in one study, SSI morbidity and recurrence rates in ambulatory surgery were half the rates in inpatient surgery. A 5-year study of SSIs in ambulatory surgery centers showed a rate of 2.8 SSI per 100 surgeries (Vilar-Compte, 2001). These rates are relatively consistent- another study reported a risk of SSI after outpatient surgery to be 3.5% (Grøgaard, 2001). Aside from morbidity alone, postsurgical visits due to SSI acquired during surgery contribute much to the cost burden on healthcare facilities. A study on postsurgical acute care visits for SSIs in ASCs demonstrated a rate of 3.09 SSI-related visits per 1000 procedures at 14 days after surgery and 4.84 per 1000 at 30 days after surgery (Owens, 2014). References Mannien, J., Wille, J. C., Snoeren, R. L., & Hof, S. V. (2006). Impact of Postdischarge Surveillance on Surgical Site Infection Rates for Several Surgical Procedures: Results From the Nosocomial Surveillance Network in The Netherlands. Infection Control and Hospital Epidemiology Infect Control Hosp Epidemiol, 27(8), 809-816. Volkow, P., Vilar-Compte, D., Jacquemin, B., & Robles-Vidal, C. (2004). Surgical Site Infections in Breast Surgery: Case-control Study. World Journal of Surgery, 28(3), 242-246. Vilar-Compte, D., Rosales, S., Hernandez-Mello, N., Maafs, E., & Volkow, P. (2009). Surveillance, Control, and Prevention of Surgical Site Infections in Breast Cancer Surgery: A 5-year Experience. American Journal of Infection Control, 37(8), 674-679. Olsen, M. A., et al. (2008). Hospital-Associated Costs Due to Surgical Site Infection After Breast Surgery. Arch Surg Archives of Surgery, 143(1), 53-60. Kozak LJ, McCarthy E, Pokras R. (1999). Changing Patterns of Surgical Care in the United States, 1980-1995. Health Care Financ Rev, 21(1), 31-49. Vilar-Compte D, Roldán R, Sandoval S, et al. (2001). Surgical Site Infections in Ambulatory Surgery: A 5-year Experience. American Journal of Infection Control, 29(2), 99-103. Grøgaard, B. (2001). Wound Infection in Day-surgery. Ambulatory Surgery, 9(2), 109-112. Owens PL, Barrett ML, Raetzman S, Maggard-Gibbons M, Steiner CA. (2014). Surgical Site Infections Following Ambulatory Surgery Procedures. Jama, 311(7), 709-716.


Hospital Visits after Orthopedic Ambulatory Surgical Center Procedures (Program: Ambulatory Surgical Center Quality Reporting Program; MUC ID: MUC16-152)

Measure Specifications

Preliminary Analysis of Measure

Rationale for measure provided by HHS
Improving the quality of care provided at ASCs is a key priority in the context of growth in the number of ASCs and procedures performed in this setting. More than 60% of all medical or surgical procedures were performed at ASCs in 2006 – a three-fold increase from the late 1990s (Cullen et al. 2009). In 2013, more than 3.4 million Fee-for-Service (FFS) Medicare beneficiaries were treated at 5,364 Medicare-certified ASCs, and spending on ASC services by Medicare and its beneficiaries amounted to $3.7 billion (Medicare Payment Advisory Commission 2015). The patient population served at ASCs has increased not only in volume but also in age and complexity, which can be partially attributed to improvements in anesthetic care and innovations in minimally invasive surgical techniques (Bettelli 2009; Fuchs 2002). ASCs have become the preferred setting for the provision of low-risk surgical and medical procedures in the US, as many patients experience shorter wait times, prefer to avoid hospitalization, and are able to return rapidly to work (Cullen et al. 2009). Therefore, in the context of growth in volume and diversity of procedures performed at ASCs, evaluating the quality of care provided at ASCs is increasingly important. As the number of orthopedic procedures increase in ASCs, it is important to evaluate the quality of care for patients undergoing these procedures. According to Medicare claims, approximately 7% of surgeries performed at ASCs were orthopedic in nature in 2007, which reflects a 77% increase in orthopedic procedures performed at ASCs from 2000 to 2007 (Goyal et al. 2016). Measuring and reporting seven-day unplanned hospital visits following orthopedic procedures will incentivize ASCs to improve care and care transitions. Many of the reasons for hospital visits are preventable. Patients often present to the hospital for complications of medical care, including infection, post-operative bleeding, urinary retention, nausea and vomiting, and pain. Martín-Ferrero et al. (2014) found that of 10,032 patients who underwent ambulatory orthopedic surgical procedures at an ambulatory surgery unit between June 1993 and June 2012, 121 (1.2%) patients needed attention in the emergency department during the first 24 hours after discharge because of pain (86 patients) or bleeding (35 patients). There were five subsequent hospitalizations for knee pain and swelling (Martín-Ferrero and Faour- Martín 2014). In conclusion, acute care visits following orthopedic surgery are an important and measurable outcome for surgeries and procedures performed at ASCs. Many of these unanticipated acute care visits occur at or after discharge and may not be readily visible to clinicians because patients often present to alternative facilities, such as emergency departments. Therefore, illuminating these events should facilitate efforts to improve patient outcomes following ASC procedures. Bettelli G. High risk patients in day surgery. Minerva anestesiologica. 2009;75(5):259-268. Cullen KA, Hall MJ, Golosinskiy A, Statistics NCfH. Ambulatory surgery in the United States, 2006. US Department of Health and Human Services, Centers for Disease Control and Prevention, National Center for Health Statistics; 2009. Fuchs K. Minimally invasive surgery. Endoscopy. 2002;34(2):154-159. Goyal KS, Jain S, Buterbaugh GA, et al. The safety of hang and upper-extremity surgical procedures at a freestanding ambulatory surgical center. The Journal of Bone and Joint Surgery. 2016;90:600-4. Martín-Ferrero MÁ, Faour- Martín O. Ambulatory surgery in orthopedics: experience of over 10,000 patients. Journal of Orthopaedic Surgery. 2014;19:332-338. Medicare Payment Advisory Commission (MedPAC). Report to Congress: Medicare Payment Policy. March 2015; http://www.medpac.gov/docs/default-source/reports/mar2015_entirereport_revised.pdf?sfvrsn=0.


Hospital Visits after Urology Ambulatory Surgical Center Procedures (Program: Ambulatory Surgical Center Quality Reporting Program; MUC ID: MUC16-153)

Measure Specifications

Preliminary Analysis of Measure

Rationale for measure provided by HHS
Improving the quality of care provided at ASCs is a key priority in the context of growth in the number of ASCs and procedures performed in this setting. More than 60% of all medical or surgical procedures are performed at ASCs in 2006 – a three-fold increase since the late 1990s (Cullen et al. 2009). In 2013, more than 3.4 million Fee-for-Service (FFS) Medicare beneficiaries were treated at 5,364 Medicare-certified ASCs, and spending on ASC services by Medicare and its beneficiaries amounted to $3.7 billion (Medicare Payment Advisory Commission 2015). The patient population served at ASCs has increased not only in volume but also in age and complexity, which can be partially attributed to improvements in anesthetic care and innovations in minimally invasive surgical techniques (Bettelli 2009; Fuchs 2002). ASCs have become the preferred setting for the provision of low-risk surgical and medical procedures in the US, as many patients experience shorter wait times, prefer to avoid hospitalization, and are able to return rapidly to work (Cullen et al. 2009). Therefore, in the context of growth in volume and diversity of procedures performed at ASCs, evaluating the quality of care provided at ASCs is increasingly important. As the number of urology procedures increases in ASCs, it is important to evaluate the quality of care for patients undergoing these procedures. A 1998 study found that urology procedures accounted for 4.8% of unanticipated admissions and was almost twice as likely as orthopedics, plastic surgery, or neurosurgery to have admissions (Fortier 1998). Similarly, a 2014 study found that outpatient urology surgery had an overall 3.7% readmission rate (Rambachan 2014). Using 5% national samples of Medicare FFS beneficiaries aged =65 years from 1998 to 2006, Hollingsworth et al. (2012) reported 30-day adjusted outcome rates for patients who underwent one of 22 common outpatient urologic procedures at ASCs. The 30-day adjusted rate of inpatient admission was 7.9% (0.4% same-day admission and 7.5% subsequent admission). Risk-adjustment variables included age, gender, race, comorbid status (assessed using an adaptation of the Charlson index), area of residence, and calendar year. Multivariable logistic regression analyses used robust variance estimators (Hollingsworth 2012). The study found that more frequent same-day admissions follow outpatient surgery at ASCs vs. hospitals. Since urology procedure in the ASC is a significant predictive factor for unanticipated admissions compared to other procedures (Fortier 1998), measuring and reporting seven-day unplanned hospital visits following urology procedures will incentivize ASCs to improve care and care transitions. Many of the reasons for hospital visits are preventable. Patients often present to the hospital for complications of medical care, including urinary tract infection, calculus of ureter, urinary retention, hematuria, and septicemia. However, patient and staff education is an opportunity to improve the success rate of urology procedures in the ASC (Paez 2007). Using data from the Agency for Healthcare Research and Quality (AHRQ) Healthcare Cost and Utilization Project (HCUP), Owens et al. (2014) reported unadjusted outcomes for low-risk patients undergoing five types of low- to moderate-risk surgical procedures, including urology procedures (Owens 2014). The outcomes of interest included 14- and 30-day all-cause acute care visit rates. Acute care visits included subsequent ambulatory surgery visits and inpatient admissions; the authors specifically excluded ED visits that did not result in hospitalization from the outcome. The 14- and 30-day rates of transurethral prostatectomy acute care visits were 0.11% and .18%, respectively. In conclusion, acute care visits following urology surgery are an important and measurable outcome for surgeries and procedures performed at ASCs. Many of these unanticipated acute care visits occur at or after discharge and may not be readily visible to clinicians because patients often present to alternative facilities, such as emergency departments. Therefore, illuminating these events should facilitate efforts to improve patient outcomes following ASC procedures. Bettelli G. High risk patients in day surgery. Minerva anestesiologica. 2009;75(5):259-268. Cullen KA, Hall MJ, Golosinskiy A, Statistics NCfH. Ambulatory surgery in the United States, 2006. US Department of Health and Human Services, Centers for Disease Control and Prevention, National Center for Health Statistics; 2009. Fortier J. Unanticipated admission after ambulatory surgery--a prospective study. Can J Anaesth. 1998;45(7):612-9. Fuchs K. Minimally invasive surgery. Endoscopy. 2002;34(2):154-159. Hollingsworth JM. Surgical quality among Medicare beneficiaries undergoing outpatient urological surgery. The Journal of Urology. 2012;188(4):1274-1278. Medicare Payment Advisory Commission (MedPAC). Report to Congress: Medicare Payment Policy. March 2015; http://www.medpac.gov/docs/default-source/reports/mar2015_entirereport_revised.pdf?sfvrsn=0. Owens PLPL. Surgical site infections following ambulatory surgery procedures. JAMA : the journal of the American Medical Association. 2014;311(7):709-716. Paez A. Adverse events and readmissions after day-case urological surgery. International Braz J Urol. 2007;33(3):330-8. Rambachan A. Predictors of readmission following outpatient urological surgery Annals of the Royal College of Surgeons of England. Journal of Urology. 2014; 192(1):183-188.


Hemodialysis Vascular Access: Long-term Catheter Rate (Program: ; MUC ID: MUC16-309)

Measure Specifications

Preliminary Analysis of Measure

Rationale for measure provided by HHS
The 2006 Clinical Practice Guidelines for Vascular Access is an update to the original vascular access guidelines published in 1997 by the National Kidney Foundation. In the eight years that the literature review included for the update, there have been no randomized controlled trials for type of vascular access. Specifically, for the guideline used to support this measure, a total of 84 peer-reviewed publications are included in the body of evidence presented. While these are all observational studies, some are based on either national data such as the United States Renal Data System (USRDS) that includes all patients with end stage kidney disease in the US, or international data, such as the Dialysis Outcomes Practice Pattern Study (DOPPS) that provides a global perspective for US vascular access outcomes. The overall quality of evidence is moderately strong. All studies are in the target population of hemodialysis patients. Some studies have evaluated health outcomes such as patient mortality, but have limitations due to the observational nature of the design. Other studies have more rigorous design, but use surrogate outcomes such as access thrombosis. The 12 studies listed below highlight the core benefits associated with using an AV fistula or graft such as reduced mortality and morbidity relative to using a tunneled catheter. Specifically, AV fistula have: • Lowest Cost1-3: Compared to catheters, Medicare expenditures for AVF are approximately $17,000 less per person per year. • Lowest rates of infection: AV fistula have the lowest rates of infection followed by AV grafts and then tunneled dialysis catheters4. Vascular access infections are common, and represent the second most common cause of death for patients receiving hemodialysis.5 • Lowest mortality and hospitalization: Patients using catheters (RR=2.3) and grafts (RR=1.47) have a greater mortality risk than patients dialyzed with fistulae6-9. Other studies have also found that use of fistulae reduces mortality and morbidity10-12 compared to AV grafts or catheters. References: 1. Mehta S: Statistical summary of clinical results of vascular access procedures for haemodialysis, in Sommer BG, Henry ML (eds): Vascular Access for Hemodialysis-II (ed 2). Chicago, IL, Gore, 1991, pp 145-157 2. The Cost Effectiveness of Alternative Types of Vascular access and the Economic Cost of ESRD. Bethesda, MD, National Institutes of Health, National Institute of Diabetes and Digestive and Kidney Diseases, 1995, pp 139-157 3. Eggers P, Milam R: Trends in vascular access procedures and expenditures in Medicare’s ESRD program, in Henry ML (ed): Vascular Access for Hemodialysis-VII. Chicago, IL, Gore, 2001, pp 133-143 4. Nassar GM, Ayus JC: Infectious complications of the hemodialysis access. Kidney Int 60:1-13, 2001 5. Gulati S, Sahu KM, Avula S, Sharma RK, Ayyagiri A, Pandey CM: Role of vascular access as a risk factor for infections in hemodialysis. Ren Fail 25:967-973, 2003 6. Dhingra RK, Young EW, Hulbert-Shearon TE, Leavey SF, Port FK: Type of vascular access and mortality in U.S. hemodialysis patients. Kidney Int 60:1443-1451, 2001 7. Woods JD, Port FK: The impact of vascular access for haemodialysis on patient morbidity and mortality. Nephrol Dial Transplant 12:657-659, 1997 8. Xue JL, Dahl D, Ebben JP, Collins AJ: The association of initial hemodialysis access type with mortality outcomes in elderly Medicare ESRD patients. Am J Kidney Dis 42:1013-1019, 2003 9. Polkinghorne KR, McDonald SP, Atkins RC, Kerr PG: Vascular access and all-cause mortality: A propensity score analysis. J Am Soc Nephrol 15:477-486, 2004 10. Huber TS, Carter JW, Carter RL, Seeger JM: Patency of autogenous and polytetrafluoroethylene upper extremity arteriovenous hemodialysis accesses: A systematic review. J Vasc Surg 38(5):1005-11, 2003 11. Perera GB, Mueller MP, Kubaska SM, Wilson SE, Lawrence PF, Fujitani RM: Superiority of autogenous arteriovenous hemodialysis access: Maintenance of function with fewer secondary interventions. Ann Vasc Surg 18:66-73, 2004 12. Pisoni RL, Young EW, Dykstra DM, et al: Vascular access use in Europe and the United States: Results from the DOPPS. Kidney Int 61:305-316, 2002

Summary of NQF Endorsement Review




Hemodialysis Vascular Access: Standardized Fistula Rate (Program: ; MUC ID: MUC16-308)

Measure Specifications

Preliminary Analysis of Measure

Rationale for measure provided by HHS
The 2006 Clinical Practice Guidelines for Vascular Access is an update to the original vascular access guidelines published in 1997 by the National Kidney Foundation. In the eight years that the literature review included for the update, there have been no randomized controlled trials for type of vascular access. Specifically, for the guideline used to support this measure, a total of 84 peer-reviewed publications are included in the body of evidence presented. While these are all observational studies, some are based on either national data such as the United States Renal Data System (USRDS) that includes all patients with end stage kidney disease in the US, or international data, such as the Dialysis Outcomes Practice Pattern Study (DOPPS) that provides a global perspective for US vascular access outcomes. The overall quality of evidence is moderately strong. All studies are in the target population of hemodialysis patients. Some studies have evaluated health outcomes such as patient mortality, but have limitations due to the observational nature of the design. Other studies have more rigorous design, but use surrogate outcomes such as access thrombosis. The 12 studies listed below highlight the core benefits such as reduced mortality and morbidity associated with using an AV fistula relative to either an AV graft or a tunneled catheter. Specifically, AV fistulae have: • Lowest risk of thrombosis: in a systematic review of 34 studies evaluating access patency, AVF were found to have superior primary patency at 18 months compared to AV grafts (51% vs. 33%).1 • Lowest rate of angioplasty/intervention: Procedure rates have been reported as 0.53 procedures/patient/year for AV fistula compared to 0.92 procedures/patient/year for AV grafts.2 • Longest survival: Case-mix adjusted survival analysis indicated substantially better survival of AV fistula compared with AV grafts in the US [risk ratios (RR) of failure 0.56, P < 0.0009]3 • Lowest Cost4-6: Based on 1990 costs to Medicare, graft recipients cost HCFA (CMS) $3,700 more than fistula patients when pro-rating graft reimbursements to the median fistula survival time.5 • Lowest rates of infection: AV fistula have the lowest rates of infection followed by AV grafts and then tunneled dialysis catheters7. Vascular access infections are common, and represent the second most common cause of death for patients receiving hemodialysis.8 • Lowest mortality and hospitalization: Patients using catheters (RR=2.3) and grafts (RR=1.47) have a greater mortality risk than patients dialyzed with fistulae9. Other studies have also found that use of fistulae reduces mortality and morbidity10-12 compared to AV grafts or catheters. References: 1. Huber TS, Carter JW, Carter RL, Seeger JM: Patency of autogenous and polytetrafluoroethylene upper extremity arteriovenous hemodialysis accesses: A systematic review. J Vasc Surg 38(5):1005-11, 2003 2. Perera GB, Mueller MP, Kubaska SM, Wilson SE, Lawrence PF, Fujitani RM: Superiority of autogenous arteriovenous hemodialysis access: Maintenance of function with fewer secondary interventions. Ann Vasc Surg 18:66-73, 2004 3. Pisoni RL, Young EW, Dykstra DM, et al: Vascular access use in Europe and the United States: Results from the DOPPS. Kidney Int 61:305-316, 2002 4. Mehta S: Statistical summary of clinical results of vascular access procedures for haemodialysis, in Sommer BG, Henry ML (eds): Vascular Access for Hemodialysis-II (ed 2). Chicago, IL, Gore, 1991, pp 145-157 5. The Cost Effectiveness of Alternative Types of Vascular access and the Economic Cost of ESRD. Bethesda, MD, National Institutes of Health, National Institute of Diabetes and Digestive and Kidney Diseases, 1995, pp 139-157 6. Eggers P, Milam R: Trends in vascular access procedures and expenditures in Medicare’s ESRD program, in Henry ML (ed): Vascular Access for Hemodialysis-VII. Chicago, IL, Gore, 2001, pp 133-143 7. Nassar GM, Ayus JC: Infectious complications of the hemodialysis access. Kidney Int 60:1-13, 2001 8. Gulati S, Sahu KM, Avula S, Sharma RK, Ayyagiri A, Pandey CM: Role of vascular access as a risk factor for infections in hemodialysis. Ren Fail 25:967-973, 2003 9. Dhingra RK, Young EW, Hulbert-Shearon TE, Leavey SF, Port FK: Type of vascular access and mortality in U.S. hemodialysis patients. Kidney Int 60:1443-1451, 2001 10. Woods JD, Port FK: The impact of vascular access for haemodialysis on patient morbidity and mortality. Nephrol Dial Transplant 12:657-659, 1997 11. Xue JL, Dahl D, Ebben JP, Collins AJ: The association of initial hemodialysis access type with mortality outcomes in elderly Medicare ESRD patients. Am J Kidney Dis 42:1013-1019, 2003 12. Polkinghorne KR, McDonald SP, Atkins RC, Kerr PG: Vascular access and all-cause mortality: A propensity score analysis. J Am Soc Nephrol 15:477-486, 2004

Summary of NQF Endorsement Review




Standardized Transfusion Ratio for Dialysis Facilities (Program: ; MUC ID: MUC16-305)

Measure Specifications

Preliminary Analysis of Measure

Rationale for measure provided by HHS
The Medicare ESRD Program requires Medicare certified dialysis facilities to manage the anemia of CKD as one of their responsibilities under the Conditions for Coverage (1). In addition, the Medicare ESRD Program has included payment for ESAs in dialysis facility reimbursement since 1989. It is notable that inclusion of ESAs in dialysis program payment was associated with a dramatic reduction in the use of blood transfusions in the US chronic dialysis population (2-3). Recently, reliance on achieved hemoglobin concentration as an indicator of successful anemia management in this population has been de-emphasized and use of other clinically meaningful outcomes, such as transfusion avoidance, have been recommended as alternate measures of anemia management (4-7). Best dialysis provider practice should include effective anemia management algorithms that focus on 1) prevention and treatment of iron deficiency, inflammation and other causes of ESA resistance, 2) use of the lowest dose of ESAs that achieves an appropriate target hemoglobin that is consistent with FDA guidelines and current best practices, and 3) education of patients, their families and medical providers to avoid unnecessary blood transfusion so that risk of allosensitization is minimized, eliminating or reducing one preventable barrier to successful kidney transplantation. The decision to transfuse blood is intended to improve or correct the pathophysiologic consequences of severe anemia, defined by achieved hemoglobin or hematocrit%, in a specific clinical context for each patient situation (8). Consensus guidelines in the U.S. and other consensus guidelines defining appropriate use of blood transfusions are based, in large part, on the severity of anemia (9-11). Given the role of hemoglobin as a clinical outcome that defines anemia as well as forms a basis for consensus recommendations regarding use of blood transfusion, it is not surprising that the presence of decreased hemoglobin concentration is a strong predictor of subsequent risk for blood transfusion in multiple settings, including chronic dialysis (12-21). For example, Gilbertson, et al found a nearly four-fold higher risk-adjusted transfusion rate in dialysis patients with achieved hemoglobin <10 gm/dl compared to those with >10 gm/dl hemoglobin. (19) In addition to achieved hemoglobin, other factors related to dialysis facility practices, including the facility’s response to their patients achieved hemoglobin, may influence blood transfusion risk in the chronic dialysis population (22, 25). In an observational study recently published by Molony, et al (2016) comparing different facility level titration practices, among patients with hemoglobin <10 and those with hemoglobin>11, they found increased transfusion risk in patients with larger ESA dose reductions and smaller dose escalations, and reduced transfusion risk in patients with larger ESA dose increases and smaller dose reductions (25). The authors reported no clinically meaningful differences in all-cause or cause-specific hospitalization events across groups. The Food and Drug Administration position defining the primary indication of ESA use in the CKD population is for transfusion avoidance, reflecting the assessment of the relative risks and benefits of ESA use versus blood transfusion. Several historical studies, and one recent research study reviewed by Obrador and Macdougall, document the specific risks of allosensitization after blood transfusion and the potential for transfusion-associated allosensitization to interfere with timely kidney transplantation. (23) A recent analysis demonstrated increased odds ratios for allosensitization associated with transfusion, particularly for men and parous women. That study also demonstrated a 28% reduction in likelihood of transplantation in transfused individuals, based on a multivariate risk-adjusted statistical model. (24)

Summary of NQF Endorsement Review




Alcohol & Other Drug Use Disorder Treatment Provided or Offered at Discharge and Alcohol & Other Drug Use Disorder Treatment at Discharge (Program: Hospital Inpatient Quality Reporting and EHR Incentive Program; MUC ID: MUC16-180)

Measure Specifications

Preliminary Analysis of Measure

Rationale for measure provided by HHS
In a study on the provision of evidence-based care and preventive services provided in hospitals for 30 different medical conditions, quality varied substantially according to diagnosis. Adherence to recommended practices for treatment of substance use ranked last, with only 10% of patients receiving proper care (McGlynn 2003, Gentilello 2005). Currently, less than one in twenty patients with an addiction are referred for treatment (Gentilello 1999). Unfortunately, many physicians mistakenly believe that substance use problems are largely confined to the young. They are significantly less likely to recognize an alcohol problem in an older patient than in a younger one. (Curtis 1989) As a result, these problems usually go undetected, resulting in harmful, expensive, and sometimes even catastrophic consequences. This is demonstrated by the fact that few older adults who need substance use treatment actually receive it. In 2005, persons 65 years and older made up only 11,344 out of 1.8 million substance use treatment episodes recorded.(SAMHSA 2007) • Gentilello LM, Ebel BE, Wickizer TM, Salkever DS Rivera FP. Alcohol interventions for trauma patients treated in emergency departments and hospitals: A cost benefit analysis. Ann Surg. 2005 Apr;241(4):541-50. • Gentilello LM, Villaveces A, Ries RR, Nason KS, Daranciang E, Donovan DM Copass M, Jurkovich GJ Rivara FP. Detection of acute alcohol intoxication and chronic alcohol dependence by trauma center staff. J Trauma. 1999 Dec;47(6):1131-5; discussion 1135-9. • McGlynn, EA, Asch SM, Adams J, Keesey J, et al. The New England Journal of Medicine. Boston: Jun 26, 2003. Vol. 348, Iss.26; pg. 2635, 11pgs. • Curtis, J.R.; Geller, G.; Stokes, E.J. ; et al. Characteristics, diagnosis, and treatment of alcoholism in elderly patients. J Am Geriatr Soc 37:310-316, 1989. • SAMHSA. Office of Applied Studies. Older adults in substance abuse treatment: 2005. The DASIS Report. Rockville MD, November 8, 2007.

Summary of NQF Endorsement Review




Alcohol Use Brief Intervention Provided or Offered and Alcohol Use Brief Intervention (Program: Hospital Inpatient Quality Reporting and EHR Incentive Program; MUC ID: MUC16-178)

Measure Specifications

Preliminary Analysis of Measure

Rationale for measure provided by HHS
Excessive use of alcohol has a substantial harmful impact on health and society in the United States. It is a drain on the economy and a source of enormous personal tragedy. In 2010, excessive alcohol use cost the US economy $249 billion, or $2.05 a drink, and $2 of every $5 of these costs were paid by the public. More than 537,000 persons died as a consequence of alcohol, drug, and tobacco use, making them the cause of more than one out of four deaths in the United States.1 Excessive alcohol use places drinkers, their families, and their communities at risk for many harmful health effects, including: Chronic conditions. Over time, excessive drinking can lead to high blood pressure, various cancers, heart disease, stroke, and liver disease. Sexual risk behaviors. Excessive drinking increases sexual risk behaviors, which can result in unintended pregnancy, HIV infection, and other sexually transmitted diseases. Motor vehicle crashes. Excessive drinking can lead to motor vehicle crashes, resulting in injuries and deaths. Binge drinkers are responsible for most of the alcohol-impaired driving episodes involving US adults. Violence and injuries. Excessive alcohol use can lead to falls, drowning, homicide, suicide, intimate partner violence, and sexual assault. Fetal alcohol spectrum disorders. Any alcohol use by a pregnant woman can harm a developing fetus, resulting in physical, behavioral, and learning problems later in life. Hospitalization provides a prime opportunity to address substance use, and for many patients, controlling their other health problems requires addressing their substance use.2 Approximately 8% of general hospital inpatients and 40 to 60% of traumatically injured inpatients and psychiatric inpatients have substance use disorders. 3 1. Mokdad AH, Marks JS, Stroup DS, Geberding JL. Actual Causes of Death in the United States, 2000. JAMA 2004;291:128-1245. 2. Fleming MF, Mundt MP, French MT, Manwell LB, Stauffacher EA, Barry KL. Brief physician advice for problem drinkers: Long-term efficacy and cost-benefit analysis. Alcohol Clin Exp Res. 2002 Jan;26(1):36-43. 3. Gentilello LM, Villaveces A, Ries RR, Nason KS, Daranciang E, Donovan DM, Copass M, Jurkovick GJ, Rivara FP. Detection of acute intoxication and chronic alcohol dependence by trauma center staff. J Trauma. 1999 Dec;47(6):1131-5; discussion 1135-9.

Summary of NQF Endorsement Review




Alcohol Use Screening (Program: Hospital Inpatient Quality Reporting and EHR Incentive Program; MUC ID: MUC16-179)

Measure Specifications

Preliminary Analysis of Measure

Rationale for measure provided by HHS
It was the expert opinion of our advisory panel that implementation of this measure would lead to the provision of brief interventions for patients at risk for excessive alcohol use and alcohol-related harms. Evidence-based screening instruments exist that can detect harmful alcohol use. Brief interventions that can be delivered during a single primary care office visit have been tested in multiple randomized trials, including a multi-center one in the Medicare eligible age group. They demonstrate that screening and intervention significantly reduce health risks, and generate cost savings of approximately $4 dollars for every dollar invested in providing them. (Fleming 1999) Clinical trials have demonstrated that brief interventions, especially prior to the onset of addiction, significantly improve health and reduce costs, and that similar benefits occur in those with addictive disorders who are referred to treatment (SAMHSA 2007, NIAAA 2005, Fleming 2002). Yet, according to a recent study by CDC and SAMHSA, 9 in 10 excessive drinkers are not alcohol dependent (Esser MB, Hedden SL, Kanny D, Brewer RD, Gfroerer JC, Naimi TS. Prevalence of Alcohol Dependence Among US Adult Drinkers, 2009–2011. Prev Chronic Dis 2014;11:140329).

Summary of NQF Endorsement Review




Appropriate Documentation of a Malnutrition Diagnosis (Program: Hospital Inpatient Quality Reporting and EHR Incentive Program; MUC ID: MUC16-344)

Measure Specifications

Preliminary Analysis of Measure

Rationale for measure provided by HHS
The diagnosis of malnutrition via the completion of a nutrition assessment in patients at-risk of malnutrition can assist clinicians in identifying the appropriate interventions addressing patients’ malnourished state (White, 2011; Mueller, 2011; Kruizenga, 2005). Current estimates of the prevalence of adult malnutrition range from 15%-60% depending on the patient population and criteria used to identify its occurrence (Mueller, 2011). While this reflects a large portion of the population, only around 3 percent of patients are diagnosed with malnutrition; in turn, it is estimated that 4-19 million cases are left undiagnosed and untreated (White, 2012). An analysis of nationally representative, cross-sectional data indicate that hospitalized patients diagnosed with malnutrition tend to be older and sicker and also incur increased healthcare costs compared to non-malnourished patients (Corkins, 2014). A diagnosis of malnutrition has been associated with increased length-of-stay, readmissions, and risk of mortality in the hospital (Lew, 2016). An analysis of the 2010 HealthCare Cost and Utilization Project (HCUP), which provides a broad and nationally-representative dataset describing U.S. hospital discharges, reported that mortality was more than 5 times as common among patients with a malnutrition diagnosis (Corkins, 2014). Furthermore, malnutrition in hospitalized patients is also associated with higher post-operative complications such as infections and pressure ulcers (Fry, 2010; Banks, 2010). Early identification and subsequent intervention in particular can have a positive impact on those same patient outcomes (Somanchi, 2011). Additionally, documentation of malnutrition diagnoses has been associated with significant healthcare cost savings per hospital day per patient (Amaral, 2007). Lew CC, Yandell R, Fraser RJ, Chua AP, Chong MF, Miller M. Association Between Malnutrition and Clinical Outcomes in the Intensive Care Unit: A Systematic Review. JPEN J Parenter Enteral Nutr. 2016. Corkins MR, Guenter P, Dimaria-ghalili RA, et al. Malnutrition diagnoses in hospitalized patients: United States, 2010. JPEN J Parenter Enteral Nutr. 2014;38(2):186-95. White JV, et al. Consensus statement: Academy of Nutrition and Dietetics and American Society for Parenteral and Enteral Nutrition: characteristics recommended for the identification and documentation of adult malnutrition (undernutrition). JPEN. 2012;36(3):275–283. Mueller C, Compher C & Druyan ME and the American Society for Parenteral and Enteral Nutrition (A.S.P.E.N.) Board of Directors. A.S.P.E.N. Clinical Guidelines: Nutrition Screening, Assessment, and Intervention in Adults. J Parenter Enteral Nutr. 2011;35: 16-24. Somanchi et al., The Facilitated Early Enteral and Dietary Management Effectiveness Trial in Hospitalized Patients with Malnutrition. JPEN J Parenteral Enteral Nutr 2011 35:209. Banks M, Bauer J, Graves N, Ash S. Malnutrition and pressure ulcer risk in adults in Australian health care facilities. Nutrition. 2010;26(9):896-901. Fry DE, Pine M, Jones BL, Meimban RJ. Patient characteristics and the occurrence of never events. Arch Surg. 2010;145(2):148-51. Amaral TF, Matos LC, Tavares MM, Subtil A, Martins R, Nazaré M, et al. The economic impact of disease-related malnutrition at hospital admission. Clin Nutr. 2007 Dec;26(6):778–84. Kruizenga HM et al., Effectiveness and cost-effectiveness of early screening and treatment of malnourished patients. AM J Clin Nutrition. 2005 Nov 82(5): 1082-9.


Communication about Pain During the Hospital Stay (Program: Hospital Inpatient Quality Reporting and EHR Incentive Program; MUC ID: MUC16-263)

Measure Specifications

Preliminary Analysis of Measure

Rationale for measure provided by HHS
In response to concerns expressed by physicians, hospitals and others about the current Pain Management items on the HCAHPS Survey, CMS is considering new survey items for the HCAHPS Survey that focus on patients’ communication about pain with hospital staff. These items would replace the 3 Pain Management items on the HCAHPS Survey, which comprise the current Pain Management measure. CMS is currently evaluating data on the items as well as focus groups and interviews about the new pain items. A measure based on these items would be similar to the Pain Management composite measure currently used, which is based on the current HCAHPS Survey items The new measure, Communication about Pain During the Hospital Stay, focusses on communication about pain during the patient’s hospital stay, rather than on how well pain was controlled Different from the other measures in the HCAHPS Survey, this new measure uniquely focusses on communication about pain during the patient’s hospital stay The Communication about Pain During the Hospital Stay measure would replace the current Pain Management measure in the HCAHPS Survey, which is part of the IQR Program.  CMS is testing this new measure in a large-scale HCAHPS mode experiment.  CMS is currently collecting data for the Communication about Pain During the Hospital Stay measure from discharged patients at 50 hospitals that participated in the HCAHPS mode experiment, January-March 2016.


Communication about Treating Pain Post-Discharge (Program: Hospital Inpatient Quality Reporting and EHR Incentive Program; MUC ID: MUC16-264)

Measure Specifications

Preliminary Analysis of Measure

Rationale for measure provided by HHS
In response to concerns expressed by physicians, hospitals and others about the current Pain Management items on the HCAHPS Survey, CMS is considering new survey items for the HCAHPS Survey that focus on patients’ communication about pain with hospital staff. These items would replace the 3 Pain Management items on the HCAHPS Survey, which comprise the current Pain Management measure. CMS is currently evaluating data on the items as well as focus groups and interviews about the news pain items. A measure based on these items would be similar to the Pain Management composite measure currently used, which is based on the current HCAHPS Survey items The new measure, Communication about Treating Pain Post-Discharge, focusses on communication about pain that the patient may experience after discharge from the hospital, rather than on how well pain was controlled Different from the other measures in the HCAHPS Survey, this new measure uniquely focusses on communication about pain that the patient may experience after discharge from the hospital The Communication about Treating Pain Post-Discharge measure would replace the current Pain Management measure in the HCAHPS Survey, which is part of the IQR Program.  CMS is testing this new measure in a large-scale HCAHPS mode experiment.  CMS is currently collecting data for the Communication about Treating Pain Post-Discharge measure from discharged patients at 50 hospitals that participated in the HCAHPS mode experiment, January-March 2016.


Completion of a Malnutrition Screening within 24 Hours of Admission (Program: Hospital Inpatient Quality Reporting and EHR Incentive Program; MUC ID: MUC16-294)

Measure Specifications

Preliminary Analysis of Measure

Rationale for measure provided by HHS
The peer reviewed evidence supporting this measure is centered on the concept that malnutrition screening is an important first step in identifying malnutrition risk. Identifying patients at-risk of malnutrition allows clinicians to then complete a nutrition assessment that can confirm malnutrition and initiate a care plan recommending appropriate interventions. The evidence supports rapid recognition and treatment (as well as prevention) of malnutrition which is associated with lower costs of care, lower readmission rates, length of stay and hospital-acquired conditions. Malnutrition risk identified in patients through a malnutrition screening was able to predict certain patient outcomes including length of stay, mortality, and post-operative complications. (Mueller C, Compher C & Druyan ME and the American Society for Parenteral and Enteral Nutrition (A.S.P.E.N.) Board of Directors. A.S.P.E.N. Clinical Guidelines: Nutrition Screening, Assessment, and Intervention in Adults. J Parenter Enteral Nutr. 2011;35: 16-24.) Retrospective analysis of administrative data for years 2013 and 2014 from a university hospital, in which being nutritionally 'at-risk' was defined as a Nutritional risk screening-2002 score = 3, reinforces the association between risk of malnutrition and rates of mortality, as well as cost of care. After multivariate adjustment, 'at-risk' patients had a 3.7-fold (95% confidence interval: 1.91; 7.03) higher in-hospital mortality and higher costs (excess 5642.25 ± 1479.80 CHF in 2013 and 5529.52 ± 847.02 CHF in 2014, p < 0.001) than 'not at-risk' patients, while no difference was found for LOS. It also indicates that being nutritionally 'at-risk' affects three in every five patients. (Khalatbari-soltani S, Marques-vidal P. Impact of nutritional risk screening in hospitalized patients on management, outcome and costs: A retrospective study. Clin Nutr. 2016; pii: S0261-5614(16)00069-8.) 543 patients were recruited from consecutive admissions at 2 hyperacute stroke units in London and were screened for risk of malnutrition (low, medium, and high) according to MUST. Six-month outcomes were obtained for each patient through a national database. Results of the study among stroke patients showed a highly significant increase in mortality with increasing risk of malnutrition (P?

Completion of a Nutrition Assessment for Patients Identified as At-Risk for Malnutrition within 24 Hours of a Malnutrition Screening (Program: Hospital Inpatient Quality Reporting and EHR Incentive Program; MUC ID: MUC16-296)

Measure Specifications

Preliminary Analysis of Measure

Rationale for measure provided by HHS
The peer reviewed evidence supporting this measure supports the assessment of patients at-risk of malnutrition via the completion of a nutrition assessment that can confirm malnutrition and initiate a care plan recommending appropriate interventions. The evidence supports rapid recognition and treatment (as well as prevention) of malnutrition which is associated with lower costs of care, lower readmission rates, length of stay and hospital-acquired conditions. Nutrition assessments conducted for at-risk patients identified by malnutrition screening using a validated screening tool was associated with key patient outcomes including less weight loss, reduced length of stay, improved muscle function, better nutritional intake, and fewer readmissions. (Mueller C, Compher C & Druyan ME and the American Society for Parenteral and Enteral Nutrition (A.S.P.E.N.) Board of Directors. A.S.P.E.N. Clinical Guidelines: Nutrition Screening, Assessment, and Intervention in Adults. J Parenter Enteral Nutr. 2011;35: 16-24.) A systematic review found that patient outcomes associated with malnutrition that was first identified by the use of a nutrition assessment was independently associated with poorer patient outcomes. Malnutrition was identified using two different assessment tools, the Subjective Global Assessment (SGA), this patient cohort was associated with higher hospital mortality, higher incidence of infection, and an increased risk of readmission. Using the Mini Nutritional Assessment (MNA), those identified as malnourished also experienced increased risk of postoperative complications. Additionally, fewer malnourished patients are discharged to their own homes compared to well-nourished patients. (Lew CC, Yandell R, Fraser RJ, Chua AP, Chong MF, Miller M. Association Between Malnutrition and Clinical Outcomes in the Intensive Care Unit: A Systematic Review. JPEN. Journal of parenteral and enteral nutrition. 2016.) A prospective, matched case control study supports statistically significant associations of malnutrition (assessed using the Subjective Global Assessment) with increased lengths of stay, mortality, and hospitalization costs. Malnourished patients were also more likely to be readmitted within 15 days. (Lim SL, Ong KC, Chan YH, Loke WC, Ferguson M, Daniels L. Malnutrition and its impact on cost of hospitalization, length of stay, readmission and 3-year mortality. Clin Nutr. 2012;31(3):345-50.)


Follow-Up After Hospitalization for Mental Illness (Program: Hospital Inpatient Quality Reporting and EHR Incentive Program; MUC ID: MUC16-165)

Measure Specifications

Preliminary Analysis of Measure

Rationale for measure provided by HHS
This measure assesses whether health plan members who were hospitalized for a mental illness received timely follow-up visits. Studies suggest that patients who start treatment soon after diagnosis are less likely to have negative health and social outcomes. A plan’s ability to improve its seven- and 30-day follow-up rates may result in better overall health outcomes. As studies have shown, efforts to facilitate treatment following a hospital discharge also lead to less attrition in the initial period of treatment. Thus, this time period may be an important opportunity for health plans to implement strategies aimed at establishing strong relationships with mental health providers and facilitate long-term engagement in treatment. Low-intensity interventions that can be applied widely are typically implemented at periods of high risk for treatment dropout, such as following an emergency room or hospital discharge or the time of entry into outpatient treatment (Kreyenbuhl 2009). Emerging evidence suggests that brief, low-intensity case management interventions are effective in bridging the gap between inpatient and outpatient treatment (Dixon 2009). For example, Boyer et al evaluated strategies aimed at increasing attendance at outpatient appointments following hospital discharge. They found that the most common factor in a patient’s medical history that was linked to a patient having a follow-up visit was a discussion about the discharge plan between the inpatient staff and outpatient clinicians. Other strategies they found that increased attendance at appointments included having the patient meet with outpatient staff and visit the outpatient program prior to discharge (Boyer 2000). Although rates vary across studies, reviews of the literature suggest that up to one-third of individuals with serious mental illnesses who have had some contact with the mental health service system disengage from care. Younger age, male gender, ethnic minority background, and low social functioning have been consistently associated with disengagement from mental health treatment. Individuals with co-occurring psychiatric and substance use disorders, as well as those with early onset psychosis, are at particularly high risk of treatment dropout. Studies suggest that engagement strategies that specifically target these high-risk groups, as well as high-risk periods, including following an emergency room or hospital admission and the initial period of treatment, can improve outcomes (Kreyenbuhl 2009).

Summary of NQF Endorsement Review




Hospital-Wide Risk Standardized Mortality Measure (Program: Hospital Inpatient Quality Reporting and EHR Incentive Program; MUC ID: MUC16-260)

Measure Specifications

Preliminary Analysis of Measure

Rationale for measure provided by HHS
Hospital-wide mortality has been the focus of a number of previous quality reporting initiatives in the U.S. and other countries. Prior efforts have met with some success and a number of challenges. Through our environmental scan and literature review, we identified multiple hospital-wide mortality measures reported at the state-level, and several at the health-system level. There is no HWM measure reported at the national-level in the United States. The vast majority of patients admitted to the hospital have survival as a primary goal, and this outcome is already the focus of existing CMS condition- and procedure-specific mortality quality measures. We know from these existing measures of risk-standardized mortality rates that there is variation across hospitals in risk-adjusted mortality, supporting variation in the quality of care received at these hospitals1. Furthermore, we also know that these existing mortality measures provide specificity for targeted quality improvement work and may have contributed to national declines in hospital mortality rates for measured conditions2. However, these measures do not allow broader statements about a hospital’s performance for those admitted, nor do they meaningfully capture performance for small-volume hospitals. By creating a hospital-wide mortality measure, we will be able to capture cross-cutting hospital-wide characteristics that may contribute to quality of care such as a culture of safety, good communication across teams, multidisciplinary care teams, coordination with community services and efforts, and effective care transitions. While avoiding mortality is a primary outcome for most patients, we do recognize that this is not true for all patients, and that there are also some patients for which the quality of care at a hospital may not impact the outcome. In order to create a measure that is meaningful and accurately reflects patient’s goals of care, we have worked with a broad range of stakeholders, including patients, family caregivers, and clinicians to best identify those admissions which should not be included in the measure, such as patients that have been enrolled in hospice before or on admission. While limitation of treatment orders (such as DNR, or comfort care only) are important for understanding patient wishes, for this measure we only have data from claims. The code for DNR is unreliable and not appropriately captured in claims3. In addition, there are no claims for other limitation of treatment orders. Because of this, our stakeholders have agreed that it should not be used in this measure. 1. Render ML, Kim HM, Deddens J, et al. Variation in outcomes in Veterans Affairs intensive care units with a computerized severity measure. Critical care medicine. May 2005;33(5):930-939. 2. Suter LG, Li SX, Grady JN, et al. National patterns of risk-standardized mortality and readmission after hospitalization for acute myocardial infarction, heart failure, and pneumonia: update on publicly reported outcomes measures based on the 2013 release. Journal of general internal medicine. Oct 2014;29(10):1333-1340. 3. Goldman LE, Chu PW, Osmond D, Bindman A. Accuracy of do not re-suscitate (DNR) in administrative data. Med Care Res Rev. 2013;70:98–112. doi: 10.1177/1077558712458455


Measure of Quality of Informed Consent Documents for Hospital-Performed, Elective Procedures (Program: Hospital Inpatient Quality Reporting and EHR Incentive Program; MUC ID: MUC16-262)

Measure Specifications

Preliminary Analysis of Measure

Rationale for measure provided by HHS
The goal of this measure of informed consent document quality is to support national strategies to promote patient-centered decision making. In evaluating hospitals' informed consent document quality, CMS seeks to increase the attention and effort that hospitals dedicate to providing high-quality informed consent, thereby supporting patient autonomy. This measure evaluates the quality of informed consent documents using items, developed through a consensus process, that are firmly based in the ethical and legal principles of informed consent, and are supported by patients as being meaningful improvements to the informed consent process. The measure aims to transform the informed consent document from a transactional form used to attain a patients’ signature to a meaningful document and resource that supports patients in the decision-making process. This informed consent document measure is a first step towards improving the practice of informed consent through quality measurement, and may compliment or serve as a platform for other measures of high-quality, patient-centered decision making. There are significant gaps in informed consent document quality and highly variable compliance with informed consent guidelines.[1-3] Hospitals often follow legal precedent, which results in perfunctory consent documents that convey the minimum amount of information necessary for compliance without providing patient-centered information that fosters patient autonomy or choice.[4-8] Prior studies, lawsuits and patient testimonies reflect a process that is broken, void of meaningful information for patients to develop informed preferences, and sometimes jeopardizing patient safety.[4,9-10] The implementation of a new quality measure that establishes a consistent and patient-centered standard based on existing guidelines for informed consent can lead to improved patient autonomy, patient safety, and high-quality decision making. The goal of this measure focuses on supporting the national efforts of CMS and NQF to improve patient-centered care and to fill several quality gaps in both the informed consent document and the measurement of these documents. References: 1. Bottrell MM, Alpert H, Fischbach RL, Emanuel LL. Hospital informed consent for procedure forms: facilitating quality patient-physician interaction. Archives of surgery (Chicago, Ill. : 1960). 2000;135(1):26-33. 2. Falagas ME, Korbila IP, Giannopoulou KP, Kondilis BK, Peppas G. Informed consent: how much and what do patients understand? American journal of surgery. 2009;198(3):420-435. 3. O'Neill O. Some limits of informed consent. Journal of medical ethics. 2003;29(1):4-7. 4. Oster, RR. Questioning Protocol, a Family's Perspective. Available at: http://www.engagingpatients.org/redesigning-the-care-experience/questioning-protocol-familys-perspective/. Accessed: July 5, 2015. 5. Habiba M, Jackson C, Akkad A, Kenyon S, Dixon-Woods M. Women's accounts of consenting to surgery: is consent a quality problem? Quality & safety in health care. 2004;13(6):422-427. 6. Childers R, Lipsett PA, Pawlik TM. Informed consent and the surgeon. Journal of the American College of Surgeons. 2009;208(4):627-634. 7. Mulley A, Trimble C, Elwyn G. Patients' preferences matter: stop the silent misdiagnosis. Bmj. 2012. 8. Krumholz HM, Schwartz J, Eddy E, et al. Surveillance Report: New Measure Probe. Not published: Prepared for: Centers for Medicare & Medicaid Services; Prepared by: Yale New Haven Health Services Corporation/Center for Outcomes Research and Evaluation (YNHHSC/CORE); 2014. 9. Montgomery v Lanarkshire Health Board. In: Court TS, ed. UKSC 11. United Kingdom. 2015. Available at: https://www.supremecourt.uk/decided-cases/docs/UKSC_2013_0136_Judgment.pdf. Accessed: July 5, 2016. 10. Statements on Principles. Relation of the Surgeon to the Patient: Informed Consent: American College of Surgeons; 2008:1-12.


Nutrition Care Plan for Patients Identified as Malnourished after a Completed Nutrition Assessment (Program: Hospital Inpatient Quality Reporting and EHR Incentive Program; MUC ID: MUC16-372)

Measure Specifications

Preliminary Analysis of Measure

Rationale for measure provided by HHS
Patients who are malnourished while in the hospital have an increased risk of complications, readmissions, and length of stay, which is associated with a significant increase in costs. Malnutrition is also associated with many adverse outcomes including depression of the immune system, impaired wound healing, muscle wasting, and increased mortality. Referral rates for dietetic assessment and treatment of malnourished patients have proven to be suboptimal, thereby increasing the likelihood of developing such aforementioned complications (Corkins, 2014), (Barker et al., 2011), (Amaral, et al., 2007), (Kruizenga et al. 2005). Presence of malnutrition/weight loss in hospitalized older adult patients is associated with higher odds of post-operative complications (including infections such as MRSA, C. diff, surgical site infections, and pneumonia) and decubitus ulcers (Fry, 2011). Nutritional status and progress are often not adequately documented in the medical record. It can be difficult to tell when (or if) patients are consuming food and supplements. In addition, nutritional procedures and EHR-triggered care are often lacking in the hospital. Similarly, nutritional care plans and patient issues are poorly communicated to post-acute facilities and PCPs (Corkins, 2014). Nutrition support intervention in patients identified by screening and assessment as at risk for malnutrition or malnourished may improve clinical outcomes (Mueller, 2011). Two research studies associated early nutritional care after risk identification with improved outcomes such as reduced length of stay, reduction in risk of readmissions, and cost of care (Lew, 2016), (Meehan, 2016). A systematic review of 62 studies with 10,187 randomized participants reported evidence for the effectiveness of nutritional supplements containing protein and energy. Overall, the review demonstrated that nutrition supplementation provided a significant reduction in mortality (RR 0.79, 95% CI 0.64 – 0.97) when patients were originally identified as “undernourished” (another term for malnourished). The risk of complications was reduced in 24 trials (RR 0.86, 95% CI 0.75-0.99) (Milne, 2009). A randomized controlled trial of 652 hospitalized, malnourished older adults over the age of 65 evaluated the use of a high-protein oral nutrition supplement for its impact on patient outcomes of non-elective readmission and mortality. The study found no effects towards improving 90-day readmission rate compared to placebo, but saw a significant reduction of 90-day mortality (p = 0.018) (Deutz, 2016). Finally, documentation of malnutrition diagnoses has been associated with significant healthcare cost savings per hospital day per patient (Amaral, 2007). Amaral TF, Matos LC, Tavares MM, Subtil A, Martins R, Nazaré M, et al. The economic impact of disease-related malnutrition at hospital admission. Clin Nutr. 2007 Dec;26(6):778–84. Barker LA, Gout BS, Crowe TC. Hospital malnutrition: prevalence, identification and impact on patients and the healthcare system. Int J Environ Res Public Health. 2011;8(2):514-27. Corkins MR, Guenter P, Dimaria-ghalili RA, et al. Malnutrition diagnoses in hospitalized patients: United States, 2010. JPEN J Parenter Enteral Nutr. 2014;38(2):186-95. Fry DE, Pine M, Jones BL, Meimban RJ. Patient characteristics and the occurrence of never events. Arch Surg. 2010;145(2):148-51. Kruizenga HM et al., Effectiveness and cost-effectiveness of early screening and treatment of malnourished patients. AM J Clin Nutrition. 2005 Nov 82(5): 1082-9. Lew CC, Yandell R, Fraser RJ, Chua AP, Chong MF, Miller M. Association Between Malnutrition and Clinical Outcomes in the Intensive Care Unit: A Systematic Review. JPEN J Parenter Enteral Nutr. 2016. Meehan A, Loose C, Bell J, Partridge J, Nelson J, Goates S. Health System Quality Improvement: Impact of Prompt Nutrition Care on Patient Outcomes and Health Care Costs. J Nurs Care Qual. 2016. Milne AC, Potter J, Vivanti A, Avenell A. Protein and energy supplementation in elderly people at risk from malnutrition. Cochrane Database Syst Rev. 2009;(2):CD003288. Mueller C, Compher C & Druyan ME and the American Society for Parenteral and Enteral Nutrition (A.S.P.E.N.) Board of Directors. A.S.P.E.N. Clinical Guidelines: Nutrition Screening, Assessment, and Intervention in Adults. JPEN J Parenter Enteral Nutr. 2011;35: 16-24. White JV, et al. Consensus statement: Academy of Nutrition and Dietetics and American Society for Parenteral and Enteral Nutrition: characteristics recommended for the identification and documentation of adult malnutrition (undernutrition). JPEN J Parenter Enteral Nutr. 2012;36(3):275–283. Somanchi et al., The Facilitated Early Enteral and Dietary Management Effectiveness Trial in Hospitalized Patients with Malnutrition. JPEN J Parenteral Enteral Nutr 2011 35:209.


Patient Panel Smoking Prevalence IQR (Program: Hospital Inpatient Quality Reporting and EHR Incentive Program; MUC ID: MUC16-068)

Measure Specifications

Preliminary Analysis of Measure

Rationale for measure provided by HHS
Cigarette smoking is still the leading preventable cause of death and disease in the U.S. and costs the U.S. health care system nearly $170 billion in direct medical care for adults each year (CDC 2014a; HHS 2014; Xu et al. 2014). Currently more than 16 million US residents are living with a smoking-related illness (HHS 2014). Smoking harms nearly every organ in the body and has been causally linked to numerous cancers, heart disease and stroke, chronic obstructive pulmonary disease, pneumonia, other respiratory diseases, aortic aneurysm, peripheral vascular disease, cataracts and blindness, age-related macular degeneration, periodontitis, diabetes, pregnancy and reproductive complications, bone fractures, arthritis, and reduced immune function (HHS, 2014). Mortality among current smokers is two to three times that of persons who never smoked (Jha et al. 2013). Since the first Surgeon General’s Report on Smoking and Health in 1964, cigarette smoking has killed more than 20 million people in the U.S. (HHS 2014). Between 2005-2009, 87% of lung cancer deaths, 61% of all pulmonary disease deaths, and 32% of all coronary heart disease deaths were attributable to smoking and secondhand smoke exposure (HHS, 2014), making it an essential risk factor to address to reduce both disease burden and health care costs. The toll smoking takes on health extends beyond the smokers. Since 1964, almost 2.5 million nonsmoking adults have died from heart disease and lung cancer caused by exposure to secondhand smoke, and 100,000 babies have died of sudden infant death syndrome or complications from prematurity, low birth weight, or other conditions caused by parental smoking, particularly smoking by the mother (HHS, 2014). Reducing cigarette smoking in the community can impact the health and health care costs of nonsmokers as well. CDC (Centers for Disease Control and Prevention). (2014a). CDC’s Tips from Former Smokers campaign provided outstanding return on investment. Atlanta, GA. Available at: http://www.cdc.gov/media/releases/2014/p1210-tips-roi.html. (Accessed 27 October, 2015). HHS (US Department of Health and Human Services). (2014). The Health Consequences of Smoking—50 Years of Progress: A Report of the Surgeon General. Atlanta, GA: US Department of Health and Human Services, Centers for Disease Control and Prevention, National Center for Chronic Disease Prevention and Health Promotion, Office on Smoking and Health. Available at: http://www.surgeongeneral.gov/library/reports/50-years-of-progress/full-report.pdf. (Accessed 23 September, 2015). Xu X, Bishop EE, Kennedy SM, Simpson SA, Pechacek TF. (2014) Annual Healthcare Spending Attributable to Cigarette Smoking: An Update. American Journal of Preventive Medicine, 48(3), p.326-333. Available at: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4603661/ (Accessed 24 September, 2015). Jha, P. and Peto, R. (2014). Global effects of smoking, of quitting, and of taxing tobacco. New England Journal of Medicine, 2014(370), p.60-68. Available at: http://www.nejm.org/doi/full/10.1056/nejmra1308383. (Accessed 22 October, 2015). doi: 10.1056/NEJMra1308383


Safe Use of Opioids – Concurrent Prescribing (Program: Hospital Inpatient Quality Reporting and EHR Incentive Program; MUC ID: MUC16-167)

Measure Specifications

Preliminary Analysis of Measure

Rationale for measure provided by HHS
Unintentional opioid overdose fatalities have become an epidemic in the last 20 years and a major public health concern in the United States (Rudd 2016). Reducing the number of unintentional overdoses has become a priority for numerous federal organizations including the Centers for Disease Control and Prevention (CDC), the Federal Interagency Workgroup for Opioid Adverse Drug Events, and the Substance Abuse and Mental Health Services Administration. The U.S. Food and Drug Administration recently announced new requirements calling for class-wide changes to drug labeling, to help inform health care providers and patients of the serious risks associated with the combined use of certain opioid medications and benzodiazepines. Concurrent prescriptions of opioids or opioids and benzodiazepines puts patients at a greater risk of unintentional overdose due to the increased risk of respiratory depression (Dowell 2016). An analysis of national prescribing patterns shows that more than half of patients who received an opioid prescription in 2009 had filled another opioid prescription within the previous 30 days (NIDA 2011). Another analysis of more than 1 million hospital admissions in the United States found that over 43% of all patients with nonsurgical admissions were exposed to multiple opioids during their hospitalization (Herzig 2013). Studies of multiple claims and prescription databases have shown that between 5%-15% percent of patients receive concurrent opioid prescriptions and 5%-20% of patients receive concurrent opioid and benzodiazepine prescriptions across various settings (Liu 2013, Mack 2015, Park 2015). Patients who have multiple opioid prescriptions have an increased risk for overdose (Jena 2014). Rates of fatal overdose are ten times higher in patients who are co-dispensed opioid analgesics and benzodiazepines than opioids alone (Dasgupta 2015). Furthermore, concurrent use of benzodiazepines with opioids was prevalent in 31%-51% of fatal overdoses (Dowell 2016). Emergency Department (ED) visit rates involving both opioid analgesics and benzodiazepines increased from 11.0 in 2004 to 34.2 per 100,000 population in 2011 (Jones 2015). Adopting a measure that calculates the proportion of patients prescribed two or more different opioids or opioids and benzodiazepines concurrently, has the potential to reduce preventable mortality and reduce the costs associated with adverse events related to opioid use by 1) encouraging providers to identify patients with concurrent prescriptions of opioids or opioids and benzodiazepines and 2) discouraging providers from prescribing two or more different opioids or opioids and benzodiazepines concurrently. References: Dasgupta, N., et al. "Cohort Study of the Impact of High-dose Opioid Analgesics on Overdose Mortality", Pain Medicine, Wiley Periodicals, Inc., Sep 2015. http://onlinelibrary.wiley.com/doi/10.1111/pme.12907/abstract Dowell, D., Haegerich, T., Chou, R. "CDC Guideline for Prescribing Opioids for Chronic Pain - United States, 2016". MMWR Recomm Rep 2016;65. http://www.cdc.gov/media/dpk/2016/dpk-opioid-prescription-guidelines.html Herzig, S., Rothberg, M., Cheung, M., et al. "Opioid utilization and opioid-related adverse events in nonsurgical patients in US hospitals". Nov 2013. DOI: 10.1002/jhm.2102. http://onlinelibrary.wiley.com/doi/10.1002/jhm.2102/abstract Jena, A., et al. "Opioid prescribing by multiple providers in Medicare: retrospective observational study of insurance claims", BMJ 2014; 348:g1393 doi: 10.1136/bmj.g1393. http://www.bmj.com/content/348/bmj.g1393 Jones, CM., McAninch, JK. "Emergency Department Visits and Overdose Deaths From Combined Use of Opioids and Benzodiazepines". Am J Prev Med. 2015 Oct;49(4):493-501. doi: 10.1016/j.amepre.2015.03.040. Epub 2015 Jul 3. http://www.ncbi.nlm.nih.gov/pubmed/26143953 Liu, Y., Logan, J., Paulozzi, L., et al. "Potential Misuse and Inappropriate Prescription Practices Involving Opioid Analgesics". Am J Manag Care. 2013 Aug;19(8):648-65. http://www.ajmc.com/journals/issue/2013/2013-1-vol19-n8/Potential-Misuse-and-Inappropriate-Prescription-Practices-Involving-Opioid-Analgesics/ Mack, K., Zhang, K., et al. "Prescription Practices involving Opioid Analgesics among Americans with Medicaid, 2010", J Health Care Poor Underserved. 2015 Feb; 26(1): 182-198. http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4365785/ National Institute on Drug Abuse. "Analysis of opioid prescription practices finds areas of concern". April 2011. Retrieved from https://www.drugabuse.gov/news-events/news-releases/2011/04/analysis-opioid-prescription-practices-finds-areas-concern Park, T., et al. "Benzodiazepine Prescribing Patterns and Deaths from Drug Overdose among US Veterans Receiving Opioid Analgesics: Case-cohort Study", BMJ 2015; 350:h2698. http://www.bmj.com/content/350/bmj.h2698 Rudd, R., Aleshire, N., Zibbell, J., et al. "Increases in Drug and Opioid Overdose Deaths - United States, 2000-2014". MMWR, Jan 2016. 64(50);1378-82 http://www.cdc.gov/mmwr/preview/mmwrhtml/mm6450a3.htm U.S. Food and Drug Administration. “FDA requires strong warnings for opioid analgesics, prescription opioid cough products, and benzodiazepine labeling related to serious risks and death from combined use”. Aug 31, 2016. http://www.fda.gov/NewsEvents/Newsroom/PressAnnouncements/ucm518697.htm


Influenza Immunization (IMM-2) (Program: ; MUC ID: MUC16-053)

Measure Specifications

Preliminary Analysis of Measure

Rationale for measure provided by HHS
Up to 1 in 5 people in the United States get influenza every season (CDC Key Facts 2015). Each year an average of approximately 226,000 people in the US are hospitalized with complications from influenza and between 3,000 and 49,000 die from the disease and its complications (Thompson 2003). Combined with pneumonia, influenza is the nation's 8th leading cause of death (Heron 2012). Up to two-thirds of all deaths attributable to pneumonia and influenza occur in the population of patients that have been hospitalized during flu season regardless of age (Fedson 2000). The Advisory Committee on Immunization Practices (ACIP) recommends seasonal influenza vaccination for all persons 6 months of age and older to highlight the importance of preventing influenza. Vaccination is associated with reductions in influenza among all age groups (Kostova 2013). The influenza vaccination is the most effective method for preventing influenza virus infection and its potentially severe complications. Screening and vaccination of inpatients is recommended, but hospitalization is an underutilized opportunity to provide vaccination to persons 6 months of age or older. References: Centers for Disease Control and Prevention. Key facts about influenza and the influenza vaccine, October 2015. Available at: http://www.cdc.gov/flu/keyfacts.htm. Accessed October 14, 2015. Fedson DS, Houck PM, Bratzler DW. Hospital-based influenza and pneumococcal vaccination: Sutton's Law applied to prevention. Infect Control Hosp Epi. 2000;21:692-699. Heron M. Deaths: Leading Causes for 2012. National Vital Statistics Reports; vol 64 no 10. Hyattsville, MD: National Center for Health Statistics. 2015. Kostova D, Reed C, Finelli L, Cheng P, Gargiullo PM, Shay DK, Singleton JA, Meltzer MI, Lu P,2 and Joseph S. Bresee1 Influenza Illness and Hospitalizations Averted by Influenza Vaccination in the United States, 2005-2011. PLoS One. 2013; 8(6): e66312 Thompson WW, Shay DK, Weintraub E, Brammer L, Cox N, Anderson LJ, Fukuda. Mortality associated with influenza and respiratory syncytial virus in the United States. JAMA. 2003 January 8; 289 (2): 179-186.


Tobacco Use Screening (TOB-1) (Program: ; MUC ID: MUC16-050)

Measure Specifications

Preliminary Analysis of Measure

Rationale for measure provided by HHS
Tobacco use is the single greatest cause of disease in the United States today and accounts for more than 480,000 deaths each year (CDC MMWR 2014). Smoking is a known cause of multiple cancers, heart disease, stroke, complications of pregnancy, chronic obstructive pulmonary disease, other respiratory problems, poorer wound healing, and many other diseases (DHHS 2014). Tobacco use creates a heavy cost to society as well as to individuals. Smoking-attributable health care expenditures are estimated to be at least $130 billion per year in direct medical expenses for adults, and over $150 billion in lost productivity (DHHS 2014). There is strong and consistent evidence that tobacco dependence interventions, if delivered in a timely and effective manner, significantly reduce the user's risk of suffering from tobacco-related disease and improve outcomes for those already suffering from a tobacco-related disease (DHHS 2000; Baumeister 2007; Lightwood 2003 and 1997; Rigotti 2012). Effective, evidence-based tobacco dependence interventions have been clearly identified and include brief clinician advice, individual, group, or telephone counseling, and use of FDA-approved medications. These treatments are clinically effective and extremely cost-effective relative to other commonly used disease prevention interventions and medical treatments. Hospitalization (both because hospitals are a tobacco-free environment and because patients may be more motivated to quit as a result of their illness) offers an ideal opportunity to provide cessation assistance that may promote the patient's medical recovery. Patients who receive even brief advice and intervention from their care providers are more likely to quit than those who receive no intervention (DHHS, 2008). References: Baumeister SE, Schumann A, Meyer C, et al. Effects of smoking cessation on health care use: is elevated risk of hospitalization among former smokers attributable to smoking-related morbidity? Drug Alcohol Depend. 2007 May 11;88(2-3):197-203. Epub 2006 Nov 21. Centers for Disease Control and Prevention. Current Cigarette Smoking Among Adults — United States, 2005–2013. Morbidity and Mortality Weekly Report (MMWR) 2014. 63(47); 1108-1112. Available at: http://www.cdc.gov/mmwr/preview/mmwrhtml/mm6347a4.htm?s_cid=mm6347a4_w Lightwood JM. The economics of smoking and cardiovascular disease. Prog Cardiovasc Dis. 2003 Jul-Aug;46(1):39-78. Lightwood JM, Glantz SA. Short-term economic and health benefits of smoking cessation: myocardial infarction and stroke. Circulation. 1997 Aug 19;96 (4):1089-96. Rigotti, et al. Interventions for smoking cessation in hospitalized patients. Cochrane Database of Systematic Reviews. 2012. Available from: http://onlinelibrary.wiley.com/doi/10.1002/14651858.CD001837.pub3/abstract U.S. Department of Health and Human Services. Reducing tobacco use: a report of the Surgeon General. Atlanta, GA, U.S. Department of Health and Human Services, Centers for Disease Control and Prevention, National Center for Chronic Disease Prevention and Health Promotion, Office on Smoking and Health, 2000. US Department of Health and Human Services. The health consequences of smoking—50 years of progress: a report of the Surgeon General. Atlanta, GA: US Department of Health and Human Services, CDC; 2014. Available at http://www.surgeongeneral.gov/library/reports/50-years-of-progress/full-report.pdf U.S. Department of Health and Human Services. Tobacco Use and Dependence Guideline Panel. Treating Tobacco Use and Dependence: 2008 Update. Rockville, MD, U.S. Department of Health and Human Services; 2008 May. Available from: http://www.ncbi.nlm.nih.gov/books/NBK63952/


Tobacco Use Treatment Provided or Offered (TOB-2)/Tobacco Use Treatment (TOB-2a) (Program: ; MUC ID: MUC16-051)

Measure Specifications

Preliminary Analysis of Measure

Rationale for measure provided by HHS
Tobacco use is the single greatest cause of disease in the United States today and accounts for more than 480,000 deaths each year (CDC MMWR 2014). Smoking is a known cause of multiple cancers, heart disease, stroke, complications of pregnancy, chronic obstructive pulmonary disease, other respiratory problems, poorer wound healing, and many other diseases (DHHS 2014). Tobacco use creates a heavy cost to society as well as to individuals. Smoking-attributable health care expenditures are estimated to be at least $130 billion per year in direct medical expenses for adults, and over $150 billion in lost productivity (DHHS 2014). There is strong and consistent evidence that tobacco dependence interventions, if delivered in a timely and effective manner, significantly reduce the user's risk of suffering from tobacco-related disease and improve outcomes for those already suffering from a tobacco-related disease (DHHS 2000; Baumeister 2007; Lightwood 2003 and 1997; Rigotti 2012). Effective, evidence-based tobacco dependence interventions have been clearly identified and include brief clinician advice, individual, group, or telephone counseling, and use of FDA-approved medications. These treatments are clinically effective and extremely cost-effective relative to other commonly used disease prevention interventions and medical treatments. Hospitalization (both because hospitals are a tobacco-free environment and because patients may be more motivated to quit as a result of their illness) offers an ideal opportunity to provide cessation assistance that may promote the patient's medical recovery. Patients who receive even brief advice and intervention from their care providers are more likely to quit than those who receive no intervention (DHHS, 2008). References: Baumeister SE, Schumann A, Meyer C, et al. Effects of smoking cessation on health care use: is elevated risk of hospitalization among former smokers attributable to smoking-related morbidity? Drug Alcohol Depend. 2007 May 11;88(2-3):197-203. Epub 2006 Nov 21. Centers for Disease Control and Prevention. Current Cigarette Smoking Among Adults — United States, 2005–2013. Morbidity and Mortality Weekly Report (MMWR) 2014. 63(47); 1108-1112. Available at: http://www.cdc.gov/mmwr/preview/mmwrhtml/mm6347a4.htm?s_cid=mm6347a4_w Lightwood JM. The economics of smoking and cardiovascular disease. Prog Cardiovasc Dis. 2003 Jul-Aug;46(1):39-78. Lightwood JM, Glantz SA. Short-term economic and health benefits of smoking cessation: myocardial infarction and stroke. Circulation. 1997 Aug 19;96 (4):1089-96. Rigotti, et al. Interventions for smoking cessation in hospitalized patients. Cochrane Database of Systematic Reviews. 2012. Available from: http://onlinelibrary.wiley.com/doi/10.1002/14651858.CD001837.pub3/abstract U.S. Department of Health and Human Services. Reducing tobacco use: a report of the Surgeon General. Atlanta, GA, U.S. Department of Health and Human Services, Centers for Disease Control and Prevention, National Center for Chronic Disease Prevention and Health Promotion, Office on Smoking and Health, 2000. US Department of Health and Human Services. The health consequences of smoking—50 years of progress: a report of the Surgeon General. Atlanta, GA: US Department of Health and Human Services, CDC; 2014. Available at http://www.surgeongeneral.gov/library/reports/50-years-of-progress/full-report.pdf U.S. Department of Health and Human Services. Tobacco Use and Dependence Guideline Panel. Treating Tobacco Use and Dependence: 2008 Update. Rockville, MD, U.S. Department of Health and Human Services; 2008 May. Available from: http://www.ncbi.nlm.nih.gov/books/NBK63952/


Tobacco Use Treatment Provided or Offered at Discharge (TOB-3)/Tobacco Use Treatment at Discharge (TOB-3a) (Program: ; MUC ID: MUC16-052)

Measure Specifications

Preliminary Analysis of Measure

Rationale for measure provided by HHS
Tobacco use is the single greatest cause of disease in the United States today and accounts for more than 480,000 deaths each year (CDC MMWR 2014). Smoking is a known cause of multiple cancers, heart disease, stroke, complications of pregnancy, chronic obstructive pulmonary disease, other respiratory problems, poorer wound healing, and many other diseases (DHHS 2014). Tobacco use creates a heavy cost to society as well as to individuals. Smoking-attributable health care expenditures are estimated to be at least $130 billion per year in direct medical expenses for adults, and over $150 billion in lost productivity (DHHS 2014). There is strong and consistent evidence that tobacco dependence interventions, if delivered in a timely and effective manner, significantly reduce the user's risk of suffering from tobacco-related disease and improve outcomes for those already suffering from a tobacco-related disease (DHHS 2000; Baumeister 2007; Lightwood 2003 and 1997; Rigotti 2012). Effective, evidence-based tobacco dependence interventions have been clearly identified and include brief clinician advice, individual, group, or telephone counseling, and use of FDA-approved medications. These treatments are clinically effective and extremely cost-effective relative to other commonly used disease prevention interventions and medical treatments. Hospitalization (both because hospitals are a tobacco-free environment and because patients may be more motivated to quit as a result of their illness) offers an ideal opportunity to provide cessation assistance that may promote the patient's medical recovery. Patients who receive even brief advice and intervention from their care providers are more likely to quit than those who receive no intervention (DHHS, 2008). References: Baumeister, S. E., Schumann, A., Meyer, C., John, U., Volzke, H., & Alte, D. (2007). Effects of smoking cessation on health care use: Is elevated risk of hospitalization among former smokers attributable to smoking-related morbidity? Drug and Alcohol Dependence, 88(2–3), 197–203. Centers for Disease Control and Prevention. (2014). Current cigarette smoking among adults—United States, 2005–2013. Morbidity and Mortality Weekly Report (MMWR), 63(47), 1108–1112. Retrieved from http://www.cdc.gov/mmwr/preview/mmwrhtml/mm6347a4.htm?s_cid=mm6347a4_w. Lightwood, J. M. (2003). The economics of smoking and cardiovascular disease. Progress in Cardiovascular Diseases, 46(1), 39–78. Lightwood, J. M., & Glantz, S. A. (1997). Short-term economic and health benefits of smoking cessation: Myocardial infarction and stroke. Circulation, 96(4), 1089–1096. Rigotti, N. A., Clair, C., Munafo, M. R., & Stead, L. F. (2012). Interventions for smoking cessation in hospitalized patients. Cochrane Database of Systematic Reviews. Retrieved from http://onlinelibrary.wiley.com/doi/10.1002/14651858.CD001837.pub3/abstract. U.S. Department of Health and Human Services. (2014). The health consequences of smoking—50 years of progress: A report of the Surgeon General. Atlanta, GA: U.S. Department of Health and Human Services. Reducing tobacco use: a report of the Surgeon General. Atlanta, GA, U.S. Department of Health and Human Services, Centers for Disease Control and Prevention, National Center for Chronic Disease Prevention and Health Promotion, Office on Smoking and Health, 2000. US Department of Health and Human Services. The health consequences of smoking—50 years of progress: a report of the Surgeon General. Atlanta, GA: US Department of Health and Human Services, CDC; 2014. Available at http://www.surgeongeneral.gov/library/reports/50-years-of-progress/full-report.pdf U.S. Department of Health and Human Services. Tobacco Use and Dependence Guideline Panel. Treating Tobacco Use and Dependence: 2008 Update. Rockville, MD, U.S. Department of Health and Human Services; 2008 May. Available from: http://www.ncbi.nlm.nih.gov/books/NBK63952/


Use of Antipsychotics in Older Adults in the Inpatient Hospital Setting (Program: ; MUC ID: MUC16-041)

Measure Specifications

Preliminary Analysis of Measure

Rationale for measure provided by HHS
Hospitalized patients are at risk for delirium, or "acute confusional state," which is a common clinical syndrome that is associated with increased mortality in ICU patients as well as the advancement of cognitive impairment. Antipsychotics are often used off-label as a method of treating patients in an acute confusional state despite conflicting evidence regarding the effectiveness of antipsychotics in treating these disorders. Clinical guidelines recommend against using antipsychotics as a standard first line of treatment for patients experiencing aggressive behavior unless they present a threat to themselves or their caregivers. References: American Geriatrics Society updated Beers Criteria for potentially inappropriate medication use in older adults. Journal of the American Geriatrics Society. Oct 2015 ;63:2227-2246; 2015. Practice guideline for the treatment of patients with delirium. American Psychiatric Association. The American journal of psychiatry. May 1999;156(5 Suppl):1-20. Barr J, Fraser GL, Puntillo K, et al. Clinical practice guidelines for the management of pain, agitation, and delirium in adult patients in the intensive care unit. Crit Care Med. Jan 2013;41(1):263-306. Barr J, Pandharipande PP. The pain, agitation, and delirium care bundle: synergistic benefits of implementing the 2013 Pain, Agitation, and Delirium Guidelines in an integrated and interdisciplinary fashion. Crit Care Med. Sep 2013;41(9 Suppl 1):S99-115. Campbell N, Boustani MA, Ayub A, et al. Pharmacological management of delirium in hospitalized adults--a systematic evidence review. Journal of general internal medicine. Jul 2009;24(7):848-853. Flaherty JH, Gonzales JP, Dong B. Antipsychotics in the treatment of delirium in older hospitalized adults: a systematic review. Journal of the American Geriatrics Society. Nov 2011;59 Suppl 2:S269-276. NICE (National Institute for Health and Clinical Excellence) Dementia: Supporting people with dementia and their careers in health and social care. 2015 (Issued November 2006, Modified March 2015). Rooney S, Qadir M, Adamis D, McCarthy G. Diagnostic and treatment practices of delirium in a general hospital. Aging Clin Exp Res. Dec 2014;26(6):625-633. Sampson EL, White N, Leurent B, et al. Behavioural and psychiatric symptoms in people with dementia admitted to the acute hospital: prospective cohort study. The British journal of psychiatry: the journal of mental science. Sep 2014;205(3):189-196. Sampson EL, White N, Lord K, et al. Pain, agitation, and behavioural problems in people with dementia admitted to general hospital wards: a longitudinal cohort study. Pain. Apr 2015;156(4):675-683. Tjia J, Briesacher BA, Peterson D, Liu Q, Andrade SE, Mitchell SL. Use of medications of questionable benefit in advanced dementia. JAMA internal medicine. Nov 2014;174(11):1763-1771.


Median Time from ED Arrival to ED Departure for Discharged ED Patients (Program: Hospital Outpatient Quality Reporting Program; MUC ID: MUC16-055)

Measure Specifications

Preliminary Analysis of Measure

Rationale for measure provided by HHS
In recent times, EDs have experienced significant overcrowding. Although once only a problem in large, urban, teaching hospitals, the phenomenon has spread to other suburban and rural healthcare organizations. According to a 2002 national U.S. survey, more than 90 percent of large hospitals report EDs operating "at" or "over" capacity. Overcrowding and heavy emergency resource demand have led to a number of problems, including ambulance refusals, prolonged patient waiting times, increased suffering for those who wait, rushed and unpleasant treatment environments, and potentially poor patient outcomes. Approximately one third of hospitals in the U.S. report increases in ambulance diversion in a given year, whereas up to half report crowded conditions in the ED. In a recent national survey, 40 percent of hospital leaders viewed ED crowding as a symptom of workforce shortages. ED crowding may result in delays in the administration of medication such as antibiotics for pneumonia and has been associated with perceptions of compromised emergency care. For patients with non-ST-segment-elevation myocardial infarction, long ED stays were associated with decreased use of guideline-recommended therapies and a higher risk of recurrent myocardial infarction. When EDs are overwhelmed, their ability to respond to community emergencies and disasters may be compromised. References: Derlet RW, Richards JR. Emergency department overcrowding in Florida, New York, and Texas. South Med J. 2002;95:846-9. Derlet RW, Richards JR. Overcrowding in the nation's emergency departments: complex causes and disturbing effects. Ann Emerg Med. 2000; 35:63-8. Fatovich DM, Hirsch RL. Entry overload, emergency department overcrowding, and ambulance bypass. Emerg Med J. 2003; 20:406-9. Hwang U, Richardson LD, Sonuyi TO, Morrison RS. The effect of emergency department crowding on the management of pain in older adults with hip fracture. J Am Geriatr Soc. 2006; 54:270-5. Pines JM, et al. ED crowding is associated with variable perceptions of care compromise. Acad Emerg Med. 2007;14:1176-81. Pines JM, et al. Emergency department crowding is associated with poor care for patients with severe pain. Ann Emerg Med. 2008;51:6-7. Schull MJ, et al. Emergency department crowding and thrombolysis delays in acute myocardial infarction. Ann Emerg Med. 2004;44:577-85. Trzeciak S, Rivers EP. Emergency department overcrowding in the United States: an emerging threat to patient safety and public health. Emerg Med J. 2003;20:402-5. Wilper AP, Woolhandler S, Lasser KE, McCormick D, Cutrona SL, Bor DH, Himmelstein DU. Waits to see an emergency department physician: U.S. trends and predictors, 1997-2004. Health Aff (Millwood). 2008;27:w84-95.


Median Time to Pain Management for Long Bone Fracture (Program: Hospital Outpatient Quality Reporting Program; MUC ID: MUC16-056)

Measure Specifications

Preliminary Analysis of Measure

Rationale for measure provided by HHS
Pain management in patients with long bone fractures is undertreated in emergency departments (Ritsema et al., 2007). Emergency department pain management has room for improvement (Ritsema et al., 2007). Patients with bone fractures continue to lack administration of pain medication as part of treatment regimens (Brown et al., 2003). When performance measures are implemented for pain management of these patients administration and treatment rates for pain improve (Herr & Titler, 2009). Disparities continue to exist in the administration of pain medication for minorities (Epps, Ware, & Packard, 2008; Todd, Samaroo, & Hoffman, 1993) and children as well (Brown et al., 2003; Friedland & Kulick, 1994). References: Brown JC, Klein EJ, Lewis CW, Johnston BD, Cummings P. Emergency department analgesia for fracture pain. Ann Emerg Med. 2003 Aug;42(2):197-205. Centers for Medicare and Medicaid Services (CMS). Hospital outpatient quality reporting specifications manual, version 9.0a. Baltimore (MD): Centers for Medicare and Medicaid Services (CMS); Effective 2016 Jan 1. various p. Epps CD, Ware LJ, Packard A. Ethnic wait time differences in analgesic administration in the emergency department. Pain Manag Nurs. 2008 Mar;9(1):26-32. Friedland LR, Kulick RM. Emergency department analgesic use in pediatric trauma victims with fractures. Ann Emerg Med. 1994 Feb;23(2):203-7. Herr K, Titler M. Acute pain assessment and pharmacological management practices for the older adult with a hip fracture: review of ED trends. J Emerg Nurs. 2009 Jul;35(4):312-20. Ritsema TS, Kelen GD, Pronovost PJ, Pham JC. The national trend in quality of emergency department pain management for long bone fractures. Acad Emerg Med. 2007 Feb;14(2):163-9. Todd KH, Samaroo N, Hoffman JR. Ethnicity as a risk factor for inadequate emergency department analgesia. JAMA. 1993 Mar 24-31;269(12):1537-9.

Summary of NQF Endorsement Review




Safe Use of Opioids – Concurrent Prescribing (Program: Hospital Outpatient Quality Reporting Program; MUC ID: MUC16-167)

Measure Specifications

Preliminary Analysis of Measure

Rationale for measure provided by HHS
Unintentional opioid overdose fatalities have become an epidemic in the last 20 years and a major public health concern in the United States (Rudd 2016). Reducing the number of unintentional overdoses has become a priority for numerous federal organizations including the Centers for Disease Control and Prevention (CDC), the Federal Interagency Workgroup for Opioid Adverse Drug Events, and the Substance Abuse and Mental Health Services Administration. The U.S. Food and Drug Administration recently announced new requirements calling for class-wide changes to drug labeling, to help inform health care providers and patients of the serious risks associated with the combined use of certain opioid medications and benzodiazepines. Concurrent prescriptions of opioids or opioids and benzodiazepines puts patients at a greater risk of unintentional overdose due to the increased risk of respiratory depression (Dowell 2016). An analysis of national prescribing patterns shows that more than half of patients who received an opioid prescription in 2009 had filled another opioid prescription within the previous 30 days (NIDA 2011). Another analysis of more than 1 million hospital admissions in the United States found that over 43% of all patients with nonsurgical admissions were exposed to multiple opioids during their hospitalization (Herzig 2013). Studies of multiple claims and prescription databases have shown that between 5%-15% percent of patients receive concurrent opioid prescriptions and 5%-20% of patients receive concurrent opioid and benzodiazepine prescriptions across various settings (Liu 2013, Mack 2015, Park 2015). Patients who have multiple opioid prescriptions have an increased risk for overdose (Jena 2014). Rates of fatal overdose are ten times higher in patients who are co-dispensed opioid analgesics and benzodiazepines than opioids alone (Dasgupta 2015). Furthermore, concurrent use of benzodiazepines with opioids was prevalent in 31%-51% of fatal overdoses (Dowell 2016). Emergency Department (ED) visit rates involving both opioid analgesics and benzodiazepines increased from 11.0 in 2004 to 34.2 per 100,000 population in 2011 (Jones 2015). Adopting a measure that calculates the proportion of patients prescribed two or more different opioids or opioids and benzodiazepines concurrently, has the potential to reduce preventable mortality and reduce the costs associated with adverse events related to opioid use by 1) encouraging providers to identify patients with concurrent prescriptions of opioids or opioids and benzodiazepines and 2) discouraging providers from prescribing two or more different opioids or opioids and benzodiazepines concurrently. References: Dasgupta, N., et al. "Cohort Study of the Impact of High-dose Opioid Analgesics on Overdose Mortality", Pain Medicine, Wiley Periodicals, Inc., Sep 2015. http://onlinelibrary.wiley.com/doi/10.1111/pme.12907/abstract Dowell, D., Haegerich, T., Chou, R. "CDC Guideline for Prescribing Opioids for Chronic Pain - United States, 2016". MMWR Recomm Rep 2016;65. http://www.cdc.gov/media/dpk/2016/dpk-opioid-prescription-guidelines.html Herzig, S., Rothberg, M., Cheung, M., et al. "Opioid utilization and opioid-related adverse events in nonsurgical patients in US hospitals". Nov 2013. DOI: 10.1002/jhm.2102. http://onlinelibrary.wiley.com/doi/10.1002/jhm.2102/abstract Jena, A., et al. "Opioid prescribing by multiple providers in Medicare: retrospective observational study of insurance claims", BMJ 2014; 348:g1393 doi: 10.1136/bmj.g1393. http://www.bmj.com/content/348/bmj.g1393 Jones, CM., McAninch, JK. "Emergency Department Visits and Overdose Deaths From Combined Use of Opioids and Benzodiazepines". Am J Prev Med. 2015 Oct;49(4):493-501. doi: 10.1016/j.amepre.2015.03.040. Epub 2015 Jul 3. http://www.ncbi.nlm.nih.gov/pubmed/26143953 Liu, Y., Logan, J., Paulozzi, L., et al. "Potential Misuse and Inappropriate Prescription Practices Involving Opioid Analgesics". Am J Manag Care. 2013 Aug;19(8):648-65. http://www.ajmc.com/journals/issue/2013/2013-1-vol19-n8/Potential-Misuse-and-Inappropriate-Prescription-Practices-Involving-Opioid-Analgesics/ Mack, K., Zhang, K., et al. "Prescription Practices involving Opioid Analgesics among Americans with Medicaid, 2010", J Health Care Poor Underserved. 2015 Feb; 26(1): 182-198. http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4365785/ National Institute on Drug Abuse. "Analysis of opioid prescription practices finds areas of concern". April 2011. Retrieved from https://www.drugabuse.gov/news-events/news-releases/2011/04/analysis-opioid-prescription-practices-finds-areas-concern Park, T., et al. "Benzodiazepine Prescribing Patterns and Deaths from Drug Overdose among US Veterans Receiving Opioid Analgesics: Case-cohort Study", BMJ 2015; 350:h2698. http://www.bmj.com/content/350/bmj.h2698 Rudd, R., Aleshire, N., Zibbell, J., et al. "Increases in Drug and Opioid Overdose Deaths - United States, 2000-2014". MMWR, Jan 2016. 64(50);1378-82 http://www.cdc.gov/mmwr/preview/mmwrhtml/mm6450a3.htm U.S. Food and Drug Administration. “FDA requires strong warnings for opioid analgesics, prescription opioid cough products, and benzodiazepine labeling related to serious risks and death from combined use”. Aug 31, 2016. http://www.fda.gov/NewsEvents/Newsroom/PressAnnouncements/ucm518697.htm


Communication about Pain During the Hospital Stay (Program: Hospital Value-Based Purchasing Program; MUC ID: MUC16-263)

Measure Specifications

Preliminary Analysis of Measure

Rationale for measure provided by HHS
In response to concerns expressed by physicians, hospitals and others about the current Pain Management items on the HCAHPS Survey, CMS is considering new survey items for the HCAHPS Survey that focus on patients’ communication about pain with hospital staff. These items would replace the 3 Pain Management items on the HCAHPS Survey, which comprise the current Pain Management measure. CMS is currently evaluating data on the items as well as focus groups and interviews about the new pain items. A measure based on these items would be similar to the Pain Management composite measure currently used, which is based on the current HCAHPS Survey items The new measure, Communication about Pain During the Hospital Stay, focusses on communication about pain during the patient’s hospital stay, rather than on how well pain was controlled Different from the other measures in the HCAHPS Survey, this new measure uniquely focusses on communication about pain during the patient’s hospital stay The Communication about Pain During the Hospital Stay measure would replace the current Pain Management measure in the HCAHPS Survey, which is part of the IQR Program.  CMS is testing this new measure in a large-scale HCAHPS mode experiment.  CMS is currently collecting data for the Communication about Pain During the Hospital Stay measure from discharged patients at 50 hospitals that participated in the HCAHPS mode experiment, January-March 2016.


Communication about Treating Pain Post-Discharge (Program: Hospital Value-Based Purchasing Program; MUC ID: MUC16-264)

Measure Specifications

Preliminary Analysis of Measure

Rationale for measure provided by HHS
In response to concerns expressed by physicians, hospitals and others about the current Pain Management items on the HCAHPS Survey, CMS is considering new survey items for the HCAHPS Survey that focus on patients’ communication about pain with hospital staff. These items would replace the 3 Pain Management items on the HCAHPS Survey, which comprise the current Pain Management measure. CMS is currently evaluating data on the items as well as focus groups and interviews about the news pain items. A measure based on these items would be similar to the Pain Management composite measure currently used, which is based on the current HCAHPS Survey items The new measure, Communication about Treating Pain Post-Discharge, focusses on communication about pain that the patient may experience after discharge from the hospital, rather than on how well pain was controlled Different from the other measures in the HCAHPS Survey, this new measure uniquely focusses on communication about pain that the patient may experience after discharge from the hospital The Communication about Treating Pain Post-Discharge measure would replace the current Pain Management measure in the HCAHPS Survey, which is part of the IQR Program.  CMS is testing this new measure in a large-scale HCAHPS mode experiment.  CMS is currently collecting data for the Communication about Treating Pain Post-Discharge measure from discharged patients at 50 hospitals that participated in the HCAHPS mode experiment, January-March 2016.


Identification of Opioid Use Disorder (Program: Inpatient Psychiatric Facility Quality Reporting Program; MUC ID: MUC16-428)

Measure Specifications

Preliminary Analysis of Measure

Rationale for measure provided by HHS
Opioid use disorder and opioid overdose are latent risks with the use of opioid medications. These adverse drug events (ADE) are potentially preventable and current policy and literature has made a call to make a continuous effort to reduce morbidity and mortality secondary to opioids, which has achieved epidemic levels.[1-4] Opioid related ADEs including opioid use disorder (OUD) have led to an increase of deaths. Between 1999 to 2014, more than 165,000 persons died from overdose related to opioid use in the United States.[5, 6] Monitoring for any indicators of substance use allows clinicians to prevent or treat OUD and prevent related ADEs. The Diagnostic and Statistical Manual of Mental Disorders noted that “routine urine toxicology test results are often positive for opioid drugs in individuals with opioid use disorder.”[7] Urine drug testing has been consistently recommended by clinical guidelines for monitoring patients on opioid therapy and regarded as a useful marker for evaluating compliance to the therapy and detecting the misuse of prescribed medications or use of illicit agents.[1, 8, 9] Studies have suggested that results from the urine drug testing are informative in making clinical assessment on aberrant drug-taking behaviors and determining the need for clinical referral to specialists.[10, 11] Monitoring adherence to the plan of care is also recommended by guidelines to ensure the effectiveness and safety of the prescribed treatment.[1, 7] The prescription drug monitoring program (PDMP) is a central data repository that collects statewide data on the controlled substance prescriptions and can be a useful tool to monitor prescription drug utilization.[12] Citations: 1. Dowell D, Haegerich TM, Chou R. CDC guideline for prescribing opioids for chronic pain. MMWR Recomm Rep 2016;65(1):1-49. Available at: https://www.cdc.gov/mmwr/volumes/65/rr/rr6501e1.htm. 2. Liu Y, Logan JE, Paulozzi LJ, Zhang K, Jones CM. Potential misuse and inappropriate prescription practices involving opioid analgesics. Am J Manag Care. 2013;19(8):648-65. 3. Mack KA, Zhang K, Paulozzi L, Jones C. Prescription practices involving opioid analgesics among Americans with Medicaid, 2010. J Health Care Poor Underserved. 2015;26(1):182-98. 4. Bohnert AS, Valenstein M, Bair MJ, et al. Association between opioid prescribing patterns and opioid overdose-related deaths. JAMA. 2011;305(13):1315-21. 5. Centers for Disease Control and Prevention (CDC). Wide-ranging online data for epidemiologic research (WONDER). Atlanta, GA: CDC, National Center for Health Statistics; 2016. Available at: http://wonder.cdc.gov/mcd.html. 6. Frenk SM, Porter KS, Paulozzi LJ. Prescription opioid analgesic use among adults: United States, 1999–2012. NCHS data brief, no 189. Hyattsville, MD: National Center for Health Statistics. 2015. 7. American Psychiatric Association. Substance use disorders. In: Diagnostic and Statistical Manual of Mental Disorders, 5th ed. Arlington, VA: American Psychiatric Association; 2013. 8. Chou R, Fanciullo GJ, Fine PG, et al. Clinical guidelines for the use of chronic opioid therapy in chronic noncancer pain. J Pain. Feb 2009;10(2):113-130. 9. Christo PJ, Manchikanti L, Ruan X, et al. Urine drug testing in chronic pain. Pain Physician. 2011;14:123-143. 10. Katz NP, Sherburne S, Beach M, et al. Behavioral monitoring and urine toxicology testing in patients receiving long-term opioid therapy. Anesth Analg. 2003;97:1096-1102. 11. Gilbert JW, Wheeler GR, Mick GE, et al. Urine drug testing in the treatment of chronic noncancer pain in a Kentucky private neuroscience practice: the potential effect of Medicare benefit changes in Kentucky. Pain Physician. 2010;13:187-194. 12. Sehgal N, Manchikanti L, Smith HS. Prescription opioid abuse in chronic pain: a review of opioid abuse predictors and strategies to curb opioid abuse. Pain Physician. 2012;15:eS67-ES92.


Medication Continuation following Inpatient Psychiatric Discharge (Program: Inpatient Psychiatric Facility Quality Reporting Program; MUC ID: MUC16-048)

Measure Specifications

Preliminary Analysis of Measure

Rationale for measure provided by HHS
The medications that constitute the numerator are evidence-based with demonstrated efficacy and safety for MDD, schizophrenia, and bipolar disorder. The continued use of effective medication is implicit and underscored by a 2010 meta-analysis of 54 double-blind placebo-controlled relapse prevention studies which found that, among patients with depression who initially responded to drug therapy, continuation of antidepressants significantly reduced relapse (odds ratios 0.35; 95% CI 0.32–0.39), and this reduction was not affected by patient age, drug class, depression subtype, or treatment duration (Glue, Donovan, Kolluri, Emir, 2010). Furthermore, among patients with bipolar disorder, medication adherence was significantly associated with the course of illness (Sylvia, 2014). Among patients with schizophrenia, those who were “good compliers” according to the Medication Adherence Rating Scale had better outcomes in terms of rehospitalization rates and medication maintenance (Jaeger, Pfiffner, Weiser, et al., 2012). A review of the medication adherence literature found that as patient medication adherence increases, the average annual healthcare spending levels decrease (Braithwaite, Shirkhorshidian, Jones, Johnsrud, 2013; Roebuck, Liberman, Gemmill-Toyama, Brennan, 2011). This measure focuses on medication continuation rather than adherence because IPFs can implement a variety of processes to improve medication continuation during the transition from inpatient to outpatient care. Examples that have been shown to increase medication compliance and prevent negative outcomes associated with nonadherence include patient education, enhanced therapeutic relationships, shared decision-making, and text-message reminders, with emphasis on multidimensional approaches (Douaihy, Kelly, Sullivan, 2013; Haddad, Brain, Scott, 2014; Hung, 2014; Kasckow and Zisook, 2008; Lanouette, Folsom, Sciolla, Jeste, 2009; Mitchell, 2007; Sylvia, Hay, Ostacher, et al., 2013). Citations: * Braithwaite, S., Shirkhorshidian, I., Jones, K., & Johnsrud, M. (2013). The role of medication adherence in the US healthcare system. Retrieved from http://avalere.com/research/docs/20130612_NACDS_Medication_Adherence.pdf * Douaihy, A. B., Kelly, T. M., & Sullivan, C. (2013). Medications for substance use disorders. Soc Work Public Health, 28(3-4), 264-278. doi: 10.1080/19371918.2013.759031 * Glue, P., Donovan, M. R., Kolluri, S., & Emir, B. (2010). Meta-analysis of relapse prevention antidepressant trials in depressive disorders. Australian and New Zealand Journal of Psychiatry, 44(8), 697-705. doi: 10.3109/00048671003705441 * Haddad, P. M., Brain, C., & Scott, J. (2014). Nonadherence with antipsychotic medication in schizophrenia: challenges and management strategies. Patient Relat Outcome Meas, 5, 43-62. doi: 10.2147/PROM.S42735 * Jaeger, S., Pfiffner, C., Weiser, P., Kilian, R., Becker, T., Langle, G., . . . Steinert, T. (2012). Adherence styles of schizophrenia patients identified by a latent class analysis of the Medication Adherence Rating Scale (MARS): a six-month follow-up study. Psychiatry Research, 200(2-3), 83-88. doi: 10.1016/j.psychres.2012.03.033 * Kasckow, J. W., & Zisook, S. (2008). Co-occurring depressive symptoms in the older patient with schizophrenia. Drugs and Aging, 25(8), 631-647. Retrieved from http://www.ncbi.nlm.nih.gov/pubmed/18665657 * Lanouette, N. M., Folsom, D. P., Sciolla, A., & Jeste, D. V. (2009). Psychotropic medication nonadherence among United States Latinos: a comprehensive literature review. Psychiatric Services, 60(2), 157-174. doi: 10.1176/appi.ps.60.2.157 * Roebuck, M. C., Liberman, J. N., Gemmill-Toyama, M., & Brennan, T. A. (2011). Medication adherence leads to lower health care use and costs despite increased drug spending. Health Affairs, 30(1), 91-99. doi: 10.1377/hlthaff.2009.1087 * Sylvia, L. G., Hay, A., Ostacher, M. J., Miklowitz, D. J., Nierenberg, A. A., Thase, M. E., . . . Perlis, R. H. (2013). Association between therapeutic alliance, care satisfaction, and pharmacological adherence in bipolar disorder. Journal of Clinical Psychopharmacology, 33(3), 343-350. doi: 10.1097/JCP.0b013e3182900c6f * Sylvia, L. G., Reilly-Harrington, N. A., Leon, A. C., Kansky, C. I., Calabrese, J. R., Bowden, C. L., . . . Nierenberg, A. A. (2014). Medication adherence in a comparative effectiveness trial for bipolar disorder. Acta Psychiatrica Scandinavica, 129(5), 359-365. doi: 10.1111/acps.12202


Medication Reconciliation at Admission (Program: Inpatient Psychiatric Facility Quality Reporting Program; MUC ID: MUC16-049)

Measure Specifications

Preliminary Analysis of Measure

Rationale for measure provided by HHS
A systematic review published in 2012 examined 26 controlled studies related to hospital-based medication reconciliation practices (Mueller, Sponsler, Kripalani, Schnipper, 2012). The studies “consistently demonstrated a reduction in medication discrepancies (17/17 studies), potential adverse drug events (5/6 studies), and adverse drug events (2/3 studies).” Of the 26 studies identified, six were rated as good quality; five as fair; and 15 as poor, using the United States Preventive Services Task Force (USPSTF) criteria. Although the heterogeneity of the study designs makes it difficult to identify the key elements of successful interventions, accurate pre-admission medication lists are critical to the medication reconciliation process as identified in the studies. Pre-admission medication reconciliation is further supported by two recent studies (MATCH and MARQUIS), which noted that most of the medication discrepancies or potential adverse drug events identified were the result of errors in obtaining the medication history (Gleason, McDaniel, Feinglass, et al., 2010; Salanitro, Kripalani, Resnic, et al., 2013). Five of the elements proposed by this measure concept are aligned with interventions from MATCH, MARQUIS, and the Joint Commission (2015). Specific to the IPF, a study indicated that 48% of patients had = 1 errors in their medication history and that the rate of ADEs is one-third higher in IPFs than in acute care hospitals (Cornish, Knowles, Marchesano, et al., 2005). Citations: * Cornish, P. L., Knowles, S. R., Marchesano, R., Tam, V., Shadowitz, S., Juurlink, D. N., & Etchells, E. E. (2005). Unintended medication discrepancies at the time of hospital admission. Archives of Internal Medicine, 165(4), 424-429. doi:10.1001/archinte.165.4.424 * Gleason, K. M., McDaniel, M. R., Feinglass, J., Baker, D. W., Lindquist, L., Liss, D., & Noskin, G. A. (2010). Results of the Medications at Transitions and Clinical Handoffs (MATCH) study: an analysis of medication reconciliation errors and risk factors at hospital admission. Journal of General Internal Medicine, 25(5), 441-447. doi:10.1007/s11606-010-1256-6 * Mueller, S. K., Sponsler, K. C., Kripalani, S., & Schnipper, J. L. (2012). Hospital-based medication reconciliation practices: a systematic review. Archives of Internal Medicine, 172(14), 1057-1069. doi: 10.1001/archinternmed.2012.2246 * Salanitro, A. H., Kripalani, S., Resnic, J., Mueller, S. K., Wetterneck, T. B., Haynes, K. T., . . . Schnipper, J. L. (2013). Rationale and design of the Multi-center Medication Reconciliation Quality Improvement Study (MARQUIS). BMC Health Services Research, 13, 230. doi:10.1186/1472-6963-13-230 * The Joint Commission. (2015). National Patient Safety Goals Effective January 1, 2015: Hospital Accreditation Program. Retrieved from http://www.jointcommission.org/assets/1/6/2015_NPSG_HAP.pdf


Localized Prostate Cancer: Bowel function (Program: Prospective Payment System-Exempt Cancer Hospital Quality Reporting Program; MUC ID: MUC16-375)

Measure Specifications

Preliminary Analysis of Measure

Rationale for measure provided by HHS
Stover A, Irwin DE, Chen RC, Chera BS, Mayer DK, Muss HB, Rosenstein DL, Shea TC, Wood WA, Lyons JC, Reeve BB; Integrating Patient-Reported Outcome Measures into Routine Cancer Care: Cancer Patients’ and Clinicians’ Perceptions of Acceptability and Value. eGEMS. 2015 Oct. 3(1): 1169. Available at: http://repository.edm-forum.org/egems/vol3/iss1/17 Skolarus TA, Dunn RL, Sanda MG, Chang P, Greenfield TK, Litwin MS, Wei JT; PROSTQA Consortium. Minimally important difference for the Expanded Prostate Cancer Index Composite Short Form. Urology. 2015 Jan;85(1):101-5. doi: 10.1016/j.urology.2014.08.044. PubMed PMID: 25530370; PubMed Central PMCID: PMC4274392. Martin NE, Massey L, Stowell C, et al. Defining a Standard Set of Patient-centered Outcomes for Men with Localized Prostate Cancer. European Urology. 2015 Mar; 67(3): 460-467. Available at http://europeanurology.com/article/S0302-2838(14)00845-8/fulltext/defining-a-standard-set-of-patient-centered-outcomes-for-men-with-localized-prostate-cancer.


Localized Prostate Cancer: Sexual function (Program: Prospective Payment System-Exempt Cancer Hospital Quality Reporting Program; MUC ID: MUC16-377)

Measure Specifications

Preliminary Analysis of Measure

Rationale for measure provided by HHS
Stover A, Irwin DE, Chen RC, Chera BS, Mayer DK, Muss HB, Rosenstein DL, Shea TC, Wood WA, Lyons JC, Reeve BB; Integrating Patient-Reported Outcome Measures into Routine Cancer Care: Cancer Patients’ and Clinicians’ Perceptions of Acceptability and Value. eGEMS. 2015 Oct. 3(1): 1169. Available at: http://repository.edm-forum.org/egems/vol3/iss1/17 Skolarus TA, Dunn RL, Sanda MG, Chang P, Greenfield TK, Litwin MS, Wei JT; PROSTQA Consortium. Minimally important difference for the Expanded Prostate Cancer Index Composite Short Form. Urology. 2015 Jan;85(1):101-5. doi: 10.1016/j.urology.2014.08.044. PubMed PMID: 25530370; PubMed Central PMCID: PMC4274392. Martin NE, Massey L, Stowell C, et al. Defining a Standard Set of Patient-centered Outcomes for Men with Localized Prostate Cancer. European Urology. 2015 Mar; 67(3): 460-467. Available at http://europeanurology.com/article/S0302-2838(14)00845-8/fulltext/defining-a-standard-set-of-patient-centered-outcomes-for-men-with-localized-prostate-cancer.


Localized Prostate Cancer: Urinary Frequency, Obstruction, and/or Irritation (Program: Prospective Payment System-Exempt Cancer Hospital Quality Reporting Program; MUC ID: MUC16-379)

Measure Specifications

Preliminary Analysis of Measure

Rationale for measure provided by HHS
Stover A, Irwin DE, Chen RC, Chera BS, Mayer DK, Muss HB, Rosenstein DL, Shea TC, Wood WA, Lyons JC, Reeve BB; Integrating Patient-Reported Outcome Measures into Routine Cancer Care: Cancer Patients’ and Clinicians’ Perceptions of Acceptability and Value. eGEMS. 2015 Oct. 3(1): 1169. Available at: http://repository.edm-forum.org/egems/vol3/iss1/17 Skolarus TA, Dunn RL, Sanda MG, Chang P, Greenfield TK, Litwin MS, Wei JT; PROSTQA Consortium. Minimally important difference for the Expanded Prostate Cancer Index Composite Short Form. Urology. 2015 Jan;85(1):101-5. doi: 10.1016/j.urology.2014.08.044. PubMed PMID: 25530370; PubMed Central PMCID: PMC4274392. Martin NE, Massey L, Stowell C, et al. Defining a Standard Set of Patient-centered Outcomes for Men with Localized Prostate Cancer. European Urology. 2015 Mar; 67(3): 460-467. Available at http://europeanurology.com/article/S0302-2838(14)00845-8/fulltext/defining-a-standard-set-of-patient-centered-outcomes-for-men-with-localized-prostate-cancer.


Localized Prostate Cancer: Urinary Incontinence (Program: Prospective Payment System-Exempt Cancer Hospital Quality Reporting Program; MUC ID: MUC16-380)

Measure Specifications

Preliminary Analysis of Measure

Rationale for measure provided by HHS
Stover A, Irwin DE, Chen RC, Chera BS, Mayer DK, Muss HB, Rosenstein DL, Shea TC, Wood WA, Lyons JC, Reeve BB; Integrating Patient-Reported Outcome Measures into Routine Cancer Care: Cancer Patients’ and Clinicians’ Perceptions of Acceptability and Value. eGEMS. 2015 Oct. 3(1): 1169. Available at: http://repository.edm-forum.org/egems/vol3/iss1/17 Skolarus TA, Dunn RL, Sanda MG, Chang P, Greenfield TK, Litwin MS, Wei JT; PROSTQA Consortium. Minimally important difference for the Expanded Prostate Cancer Index Composite Short Form. Urology. 2015 Jan;85(1):101-5. doi: 10.1016/j.urology.2014.08.044. PubMed PMID: 25530370; PubMed Central PMCID: PMC4274392. Martin NE, Massey L, Stowell C, et al. Defining a Standard Set of Patient-centered Outcomes for Men with Localized Prostate Cancer. European Urology. 2015 Mar; 67(3): 460-467. Available at http://europeanurology.com/article/S0302-2838(14)00845-8/fulltext/defining-a-standard-set-of-patient-centered-outcomes-for-men-with-localized-prostate-cancer.


Localized Prostate Cancer: Vitality (Program: Prospective Payment System-Exempt Cancer Hospital Quality Reporting Program; MUC ID: MUC16-381)

Measure Specifications

Preliminary Analysis of Measure

Rationale for measure provided by HHS
Stover A, Irwin DE, Chen RC, Chera BS, Mayer DK, Muss HB, Rosenstein DL, Shea TC, Wood WA, Lyons JC, Reeve BB; Integrating Patient-Reported Outcome Measures into Routine Cancer Care: Cancer Patients’ and Clinicians’ Perceptions of Acceptability and Value. eGEMS. 2015 Oct. 3(1): 1169. Available at: http://repository.edm-forum.org/egems/vol3/iss1/17 Skolarus TA, Dunn RL, Sanda MG, Chang P, Greenfield TK, Litwin MS, Wei JT; PROSTQA Consortium. Minimally important difference for the Expanded Prostate Cancer Index Composite Short Form. Urology. 2015 Jan;85(1):101-5. doi: 10.1016/j.urology.2014.08.044. PubMed PMID: 25530370; PubMed Central PMCID: PMC4274392. Martin NE, Massey L, Stowell C, et al. Defining a Standard Set of Patient-centered Outcomes for Men with Localized Prostate Cancer. European Urology. 2015 Mar; 67(3): 460-467. Available at http://europeanurology.com/article/S0302-2838(14)00845-8/fulltext/defining-a-standard-set-of-patient-centered-outcomes-for-men-with-localized-prostate-cancer.


PRO utilization in in non-metastatic prostate cancer patients (Program: Prospective Payment System-Exempt Cancer Hospital Quality Reporting Program; MUC ID: MUC16-393)

Measure Specifications

Preliminary Analysis of Measure

Rationale for measure provided by HHS
Neil E. Martin, Laura Massey, Caleb Stowell, et al. Defining a Standard Set of Patient-centered Outcomes for Men with Localized Prostate Cancer. Eur Urol 2015;67:460–7 Stover A, Irwin DE, Chen RC, Chera BS, Mayer DK, Muss HB, Rosenstein DL, Shea TC, Wood WA, Lyons JC, Reeve BB; Integrating Patient-Reported Outcome Measures into Routine Cancer Care: Cancer Patients’ and Clinicians’ Perceptions of Acceptability and Value. eGEMS. 2015 Oct. 3(1): 1169. Available at: http://repository.edm-forum.org/egems/vol3/iss1/17 Wei JT, Dunn RL, Litwin MS, Sandler HM, Sanda MG. "Development and Validation of the Expanded Prostate Cancer Index Composite (EPIC) for Comprehensive Assessment of Health-Related Quality of Life in Men with Prostate Cancer", Urology. 56: 899-905, 2000. Wei JT, Dunn RL, Sandler HM, McLaughlin PW, Montie JE, Litwin MS, Nyquist L, Sanda MG. Comprehensive comparison of health-related quality of life after contemporary therapies for localized prostate cancer ", Journal of Clinical Oncology. 20(2): 557-66, 2002. Hollenbeck BK, Dunn RL, Wei JT, McLaughlin PW, Han M, Sanda MG. Neoadjuvant hormonal therapy and older age are associated with adverse sexual health-related quality-of-life outcome after prostate brachytherapy ", Urology. 59: 480-4, 2002. Hollenbeck BK, Dunn RL, Wei JT, Montie JE, Sanda MG. Determinants of Long-Term Sexual HRQOL After Radical Prostatectomy Measured by a Validated Instrument", Journal of Urology. 169: 1453-7, 2003. Van Andel G, Bottomley A, Fossa SD, Efficace F, Coens C, Guerif S, Kynaston H, Gontero P, Thalmann G, Akdas A, D’Haese S, Aronson NK An international field study of the EORTC QLQ-PR25: a questionnaire for assessing the health-related quality of life of patients with prostate cancer. Eur J Cancer. 2008 Nov;44(16):2418-24. doi: 10.1016/j.ejca.2008.07.030. Epub 2008 Sep 5. Sonn GA, Sadetsky N, Presti JC, Litwin M. Differing perceptions of quality of life in patients with prostate cancer and their doctors. J Urol 2009; 182: 2296–2302. Justice AC, Rabeneck L, Hays RD, Wu AW, Bozzette SA. Sensitivity, specificity, reliability, and clinical validity of provider-reported symptoms: a comparison with self-reported symptoms. J Acquir Immune Defic Syndr 1999; 21: 126–133.


Proportion of patients who died from cancer admitted to hospice for less than 3 days (Program: Prospective Payment System-Exempt Cancer Hospital Quality Reporting Program; MUC ID: MUC16-274)

Measure Specifications

Preliminary Analysis of Measure

Rationale for measure provided by HHS
Langton, J. M., B. Blanch, et al. (2014). "Retrospective studies of end-of-life resource utilization and costs in cancer care using health administrative data: a systematic review." Palliat Med 28(10): 1167-1196. Lee, Y. J., J. H. Yang, et al. (2015). "Association between the duration of palliative care service and survival in terminal cancer patients." Support Care Cancer 23(4): 1057-1062. O´Connor, T. L., N. Ngamphaiboon, et al. (2015). "Hospice utilization and end-of-life care in metastatic breast cancer patients at a comprehensive cancer center." J Palliat Med 18(1): 50-55.

Summary of NQF Endorsement Review




Proportion of patients who died from cancer admitted to the ICU in the last 30 days of life (Program: Prospective Payment System-Exempt Cancer Hospital Quality Reporting Program; MUC ID: MUC16-273)

Measure Specifications

Preliminary Analysis of Measure

Rationale for measure provided by HHS
Zhang B, Nilsson ME, Prigerson HG. Factors important to patients´ quality of life at the end of life. Arch Intern Med 2012;172:1133-1142.Available at: http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3806298/ Wright AA, Keating NL, Balboni TA, et al. Place of death: correlations with quality of life of patients with cancer and predictors of bereaved caregivers’ mental health. J Clin Oncol 2010; 28:4457–4464. Available at: http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2988637/ Langton JM, Blanch B, Drew AK, et al. Retrospective studies of end of-life resource utilization and costs in cancer care using health administrative data: a systematic review. Palliat Med 2014;28:1167-1196. Available at: http://www.ncbi.nlm.nih.gov/pubmed/24866758. Kao YH, Chiang JK. Effect of hospice care on quality indicators of end-of-life care among patients with liver cancer: a national longitudinal population based study in Taiwan 2000-2011. BMC Palliat Care 2015: 14:39. Available at: http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4545784/#CR5 Barbera L, Seow H, et al. Quality of end-of-life cancer care in Canada: a retrospective four-province study using administrative health care data. Curr Oncol 2015 Oct: 22(5): 341-355. Available at: http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4608400/

Summary of NQF Endorsement Review




Proportion of patients who died from cancer not admitted to hospice (Program: Prospective Payment System-Exempt Cancer Hospital Quality Reporting Program; MUC ID: MUC16-275)

Measure Specifications

Preliminary Analysis of Measure

Rationale for measure provided by HHS
Smith, T. J., S. Temin, et al. (2012). "American Society of Clinical Oncology provisional clinical opinion: the integration of palliative care into standard oncology care." J Clin Oncol 30(8): 880-887. O´Connor, T. L., N. Ngamphaiboon, et al. (2015). "Hospice utilization and end-of-life care in metastatic breast cancer patients at a comprehensive cancer center." J Palliat Med 18(1): 50-55. Lee, Y. J., J. H. Yang, et al. (2015). "Association between the duration of palliative care service and survival in terminal cancer patients." Support Care Cancer 23(4): 1057-1062. Langton, J. M., B. Blanch, et al. (2014). "Retrospective studies of end-of-life resource utilization and costs in cancer care using health administrative data: a systematic review." Palliat Med 28(10): 1167-1196. Guadagnolo, B. A., K. P. Liao, et al. (2015). "Variation in Intensity and Costs of Care by Payer and Race for Patients Dying of Cancer in Texas: An Analysis of Registry-linked Medicaid, Medicare, and Dually Eligible Claims Data." Med Care 53(7): 591-598.

Summary of NQF Endorsement Review




Proportion of patients who died from cancer receiving chemotherapy in the last 14 days of life (Program: Prospective Payment System-Exempt Cancer Hospital Quality Reporting Program; MUC ID: MUC16-271)

Measure Specifications

Preliminary Analysis of Measure

Rationale for measure provided by HHS
El-Jawahri, A. R., G. A. Abel, et al. (2015). "Health care utilization and end-of-life care for older patients with acute myeloid leukemia." Cancer 121(16): 2840-2848. Mack, J. W., A. Walling, et al. (2015). "Patient beliefs that chemotherapy may be curative and care received at the end of life among patients with metastatic lung and colorectal cancer." Cancer 121(11): 1891-1897.

Summary of NQF Endorsement Review





Appendix B: Program Summaries

The material in this appendix was drawn from the CMS Program Specific Measure Priorities and Needs document, which was released in April 2016.

Program Index


Full Program Summaries

Ambulatory Surgical Center Quality Reporting Program 
The material in this appendix was drawn from the CMS Program Specific Measure Priorities and Needs document, which was released in April 2016.

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

High Priority Domains for Future Measure Consideration:

High Priority Domains for Future Measure Consideration:

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

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

Measure Requirements:

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

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

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

End-Stage Renal Disease Quality Incentive Program 
The material in this appendix was drawn from the CMS Program Specific Measure Priorities and Needs document, which was released in April 2016.

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

High Priority Domains for Future Measure Consideration:

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

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

Measure Requirements:

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

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

Hospital Acquired Condition Reduction Program 
The material in this appendix was drawn from the CMS Program Specific Measure Priorities and Needs document, which was released in April 2016.

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

High Priority Domains for Future Measure Consideration:

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

Making Care Safer:

  1. Adverse Drug Events
  2. Ventilator Associated Events
  3. Additional Surgical Site Infection Locations
  4. Outcome Risk-Adjusted Measures
  5. Diagnostic Errors
  6. All-Cause Harm

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:

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

Hospital Inpatient Quality Reporting and EHR Incentive Program 
The material in this appendix was drawn from the CMS Program Specific Measure Priorities and Needs document, which was released in April 2016.

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

High Priority Domains for Future Measure Consideration:

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

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

Measure Requirements:

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

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

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

Hospital Outpatient Quality Reporting Program 
The material in this appendix was drawn from the CMS Program Specific Measure Priorities and Needs document, which was released in April 2016.

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

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

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

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

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

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

Hospital Readmissions Reduction Program 
The material in this appendix was drawn from the CMS Program Specific Measure Priorities and Needs document, which was released in April 2016.

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

High Priority Domains for Future Measure Consideration:

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

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:

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

Hospital Value-Based Purchasing Program 
The material in this appendix was drawn from the CMS Program Specific Measure Priorities and Needs document, which was released in April 2016.

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

High Priority Domains for Future Measure Consideration:

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

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

Measure Requirements:

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

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

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

Inpatient Psychiatric Facility Quality Reporting Program 
The material in this appendix was drawn from the CMS Program Specific Measure Priorities and Needs document, which was released in April 2016.

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

High Priority Domains for Future Measure Consideration:

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

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

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

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

Prospective Payment System-Exempt Cancer Hospital Quality Reporting Program 
The material in this appendix was drawn from the CMS Program Specific Measure Priorities and Needs document, which was released in April 2016.

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

High Priority Domains for Future Measure Consideration:

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

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

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

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


Appendix C: Public Comments

Index of Measures (by Program)

All measures are included in the index, even if there were not any public comments about that measure for that program.

General Comments

Ambulatory Surgical Center Quality Reporting Program

Hospital Inpatient Quality Reporting and EHR Incentive Program

Hospital Outpatient Quality Reporting Program

Hospital Value-Based Purchasing Program

Inpatient Psychiatric Facility Quality Reporting Program

Prospective Payment System-Exempt Cancer Hospital Quality Reporting Program


Full Comments (Listed by Measure)

General
Tobacco Use Screening (TOB-1) (Program: ; MUC ID: MUC16-050)
Tobacco Use Treatment Provided or Offered (TOB-2)/Tobacco Use Treatment (TOB-2a) (Program: ; MUC ID: MUC16-051)
Tobacco Use Treatment Provided or Offered at Discharge (TOB-3)/Tobacco Use Treatment at Discharge (TOB-3a) (Program: ; MUC ID: MUC16-052)
Influenza Immunization (IMM-2) (Program: ; MUC ID: MUC16-053)
Median Time from ED Arrival to ED Departure for Discharged ED Patients (Program: Hospital Outpatient Quality Reporting Program; MUC ID: MUC16-055)
Median Time to Pain Management for Long Bone Fracture (Program: Hospital Outpatient Quality Reporting Program; MUC ID: MUC16-056)
Patient Panel Smoking Prevalence IQR (Program: Hospital Inpatient Quality Reporting and EHR Incentive Program; MUC ID: MUC16-068)
Hospital Visits following Orthopedic Ambulatory Surgical Center Procedures (Program: Ambulatory Surgical Center Quality Reporting Program; MUC ID: MUC16-152)
Hospital Visits following Urology Ambulatory Surgical Center Procedures (Program: Ambulatory Surgical Center Quality Reporting Program; MUC ID: MUC16-153)
Ambulatory Breast Procedure Surgical Site Infection (SSI) Outcome Measure (Program: Ambulatory Surgical Center Quality Reporting Program; MUC ID: MUC16-155)
Safe Use of Opioids – Concurrent Prescribing (Program: ; MUC ID: MUC16-167)
Safe Use of Opioids – Concurrent Prescribing (Program: Hospital Outpatient Quality Reporting Program; MUC ID: MUC16-167)
Alcohol Use Brief Intervention Provided or Offered and Alcohol Use Brief Intervention (Program: Hospital Inpatient Quality Reporting and EHR Incentive Program; MUC ID: MUC16-178)
Alcohol Use Screening (Program: Hospital Inpatient Quality Reporting and EHR Incentive Program; MUC ID: MUC16-179)
Alcohol & Other Drug Use Disorder Treatment Provided or Offered at Discharge and Alcohol & Other Drug Use Disorder Treatment at Discharge (Program: Hospital Inpatient Quality Reporting and EHR Incentive Program; MUC ID: MUC16-180)
Hospital-Wide Risk Standardized Mortality Measure (Program: Hospital Inpatient Quality Reporting and EHR Incentive Program; MUC ID: MUC16-260)
Measure of Quality of Informed Consent Documents for Hospital-Performed, Elective Procedures (Program: Hospital Inpatient Quality Reporting and EHR Incentive Program; MUC ID: MUC16-262)
Communication about Pain During the Hospital Stay (Program: Hospital Inpatient Quality Reporting and EHR Incentive Program; MUC ID: MUC16-263)
Communication about Pain During the Hospital Stay (Program: Hospital Value-Based Purchasing Program; MUC ID: MUC16-263)
Communication about Treating Pain Post-Discharge (Program: Hospital Inpatient Quality Reporting and EHR Incentive Program; MUC ID: MUC16-264)
Communication about Treating Pain Post-Discharge (Program: Hospital Value-Based Purchasing Program; MUC ID: MUC16-264)
Proportion of patients who died from cancer receiving chemotherapy in the last 14 days of life (Program: Prospective Payment System-Exempt Cancer Hospital Quality Reporting Program; MUC ID: MUC16-271)
Proportion of patients who died from cancer admitted to the ICU in the last 30 days of life (Program: Prospective Payment System-Exempt Cancer Hospital Quality Reporting Program; MUC ID: MUC16-273)
Proportion of patients who died from cancer admitted to hospice for less than 3 days (Program: Prospective Payment System-Exempt Cancer Hospital Quality Reporting Program; MUC ID: MUC16-274)
Proportion of patients who died from cancer not admitted to hospice (Program: Prospective Payment System-Exempt Cancer Hospital Quality Reporting Program; MUC ID: MUC16-275)
Completion of a Malnutrition Screening within 24 Hours of Admission (Program: Hospital Inpatient Quality Reporting and EHR Incentive Program; MUC ID: MUC16-294)
Completion of a Nutrition Assessment for Patients Identified as At-Risk for Malnutrition within 24 Hours of a Malnutrition Screening (Program: Hospital Inpatient Quality Reporting and EHR Incentive Program; MUC ID: MUC16-296)
Standardized Transfusion Ratio for Dialysis Facilities (Program: ; MUC ID: MUC16-305)
Hemodialysis Vascular Access: Standardized Fistula Rate (Program: ; MUC ID: MUC16-308)
Hemodialysis Vascular Access: Long-term Catheter Rate (Program: ; MUC ID: MUC16-309)
Appropriate Documentation of a Malnutrition Diagnosis (Program: Hospital Inpatient Quality Reporting and EHR Incentive Program; MUC ID: MUC16-344)
Nutrition Care Plan for Patients Identified as Malnourished after a Completed Nutrition Assessment (Program: Hospital Inpatient Quality Reporting and EHR Incentive Program; MUC ID: MUC16-372)
PRO utilization in in non-metastatic prostate cancer patients (Program: Prospective Payment System-Exempt Cancer Hospital Quality Reporting Program; MUC ID: MUC16-393)
Identification of Opioid Use Disorder among Patients Admitted to Inpatient Psychiatric Facilities (Program: Inpatient Psychiatric Facility Quality Reporting Program; MUC ID: MUC16-428)
Identification of Opioid Use Disorder among Patients Admitted to Inpatient Psychiatric Facilities (Program: Inpatient Psychiatric Facility Quality Reporting Program; MUC ID: MUC16-428)

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