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Using the AHRQ National Quality Measures Clearinghouse to Assess Diabetes Quality Measurement Brian Leas, MS, MA University of Pennsylvania Health System AHRQ Annual Conference September 19, 2011. Background. Research Objectives

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  1. Using the AHRQ National Quality Measures Clearinghouse to Assess Diabetes Quality MeasurementBrian Leas, MS, MAUniversity of Pennsylvania Health SystemAHRQ Annual ConferenceSeptember 19, 2011

  2. Background • Research Objectives • Evaluate the state of diabetes quality measurement, utilization & impact • Determine key strengths, weaknesses, gaps • Develop strategies to enhance value and impact of diabetes measures • Research Team • Thomas Jefferson University School of Population Health David Nash, Principal Investigator Kathryn Kash, Qualitative Interviewer Neil Goldfarb, Co-Investigator Rich Toner, Research Coordinator Al Crawford, Measurement Analyst Brian Leas, Project Manager Bettina Berman, Measurement Analyst • Timeline: Spring 2008 – Summer 2009 • Funding • National Changing Diabetes Program of Novo Nordisk

  3. Methodology • Environmental scan of diabetes quality measures • Measure developers: Accreditation agencies, payers, physician groups, QI organizations, state/local initiatives • All types of measures: Process, outcome, safety, etc. • Specifications: Definitions, inclusion/exclusion, time frame • Scope: Broadly representative of key sectors, rather than fully comprehensive • Key informant interviews • Measure developers, physicians, payers, employers, QI orgs • Representation from all relevant stakeholder groups • Interviewees expert in both technical development and practical utilization of quality measures • Semi-structured, recorded

  4. Search Strategy • Step 1: National Quality Measures Clearinghouse (NQMC) • Keywords: “diabetes” and “diabetes mellitus” • 98 measures identified • Step 2: Supplemental search • Roster compiled of key organizations & known measure sets • Reviewed websites, contacted organizations for information • 48 additional measures identified • Step 3: Data abstracted into Excel spreadsheet

  5. Measure Abstraction • Descriptive characteristics • Brief description • Clinical topic (e.g. HgbA1c mgmt, lipid profile, foot exam) • Developer • Release and revision dates • Broader measurement set • Website link • NQMC identification # • Specifications • Level of measurement (e.g. clinician, health plan) • Source of data (e.g. administrative, medical record, pharmacy, lab, registry) • Numerator / denominator • Inclusion / exclusion • Time frame

  6. Measure Abstraction • Validity • Supporting evidence • Extent of measure testing • Composition of development group • Endorsement by NQF and others • Policy-relevant domains* • AHRQ: Process, outcome, access, structure, use of services, patient experience, population health • IOM: Effectiveness, efficiency, equity, patient-centeredness, safety, timeliness *Dimensions as of 2008

  7. Key Informant Interviews Measure utilization and value • Blue Cross Blue Shield Association • Centers for Disease Control and Prevention • Institute for Healthcare Improvement • America’s Health Insurance Plans • American Academy of Family Physicians • Nat’l Business Coalition on Health • American Diabetes Association • American Association of Clinical Endocrinologists • American Association of Diabetes Educators Measure development, selection, endorsement • National Committee for Quality Assurance • National Quality Forum • American Medical Association • Centers for Medicare and Medicaid Services • Ambulatory Quality Alliance • Pharmacy Quality Alliance • American Board of Medical Specialties • American Board of Internal Medicine • American College of Physicians • Wisconsin Collaborative for Healthcare Quality

  8. Findings • Scope of Measurement • Nearly 150 measures in > 25 distinct clinical categories • Primarily process measures • Substantial redundancy • Widely varying specifications • Evidence base often weak or unclear • Measurement Gaps • Primary prevention • Outcomes of care • Patient perspectives • Coordination of care • Access to care

  9. Perspectives on NQMC • Highly useful for identifying relevant measures • Although 1/3 of measures in our study had not been included in NQMC, many were later submitted by their respective authors • Sufficiently specific and sensitive • Capability to stratify by numerous categories • Only known resource for identifying and comparing measure sets from different sources • Limitations • Time lag from development to submission to posting • Relies on self-reporting • Until recently, limited data available on evidence base, validity, conflicts of interest

  10. Questions? Brian Leas Research Analyst Center for Evidence-based Practice University of Pennsylvania Health System Brian.leas@uphs.upenn.edu

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