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Next Steps in Measuring Clinical Quality

Next Steps in Measuring Clinical Quality . Joe V. Selby, MD Division of Research Kaiser Permanente Northern California. Differences in Clinical Quality – Diabetes Care. * HEDIS Health Plan Summary Data. The Chasm in Clinical Quality Assessment. What We Know. What We Measure.

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Next Steps in Measuring Clinical Quality

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  1. Next Steps in Measuring Clinical Quality Joe V. Selby, MD Division of Research Kaiser Permanente Northern California

  2. Differences in Clinical Quality – Diabetes Care *HEDIS Health Plan Summary Data

  3. The Chasm in Clinical Quality Assessment What We Know WhatWe Measure Processes of care not known to be related to outcomes or effectiveness Semi-quantitative outcomes (Hb A1c >9.5%, LDL-C < 100) that hide more effectiveness differences than they reveal Quantitative effects of many process measures and of differences in outcomes on survival and on non-fatal complications in populations – from clinical trials

  4. Point #1 Population Rates for Simple Process Measures do NOT Consistently Reflect Clinical Benefit in those Populations

  5. Translating Research Into Action for Diabetes A multi-center cohort study of diabetes in managed care settings Pacific Health Research Institute UMDNJ U. Michigan Kaiser Permanente No. California Indiana U. UCLA CDC PacifiCare Texas Centers for Disease Control - Sponsor and Data Coordinating Center

  6. The TRIAD Sampling Scheme 10 health plans (n=500 to 2000 per plan) 67 physician groups with > 50 members in sampling frame (Sampling scheme: Aimed for equal numbers from each physician group within health plan, so from 50 - 1500 per physician group)

  7. TRIAD Data (2000-2001) • Patient Surveys (telephone or mailed) – 11,928 respondents • Chart Reviews – 8,757 patients • Medical Director Surveys – health plan and provider group directors

  8. Four Measures of Disease Management Intensity – from Health Plan and Provider Group Director Surveys • Use of diabetes registries • Use of clinician reminders • Performance feedback to physicians • Diabetes care management: • Guideline use • Patient reminders, • Patient education • Use of care/case managers

  9. Provider Group Performance Difference (%) (80th – 20th Percentile of Dis Mgmt Intensity) PROCESS MEASURES adjusted for patient age, sex, race, education/income, diabetes treatment and duration, comorbidities, SF-12 (PCS), health plan disease mgmt intensity

  10. Provider Group Performance Differences (80th – 20th Percentile of Dis Mgmt Intensity) INTERMEDIATE OUTCOMES adjusted for patient age, sex, race, education/income, diabetes treatment and duration, comorbidities, health plan disease mgmt intensity

  11. Moreover, • Provider Group intensity of disease management also unrelated to the appropriateness* of treatment for each condition • Provider Group Quality Scores based on process measures were unrelated to provider group levels of control of blood pressure, LDL-C or Hb A1c *Proportion in control or on appropriately aggressive pharmacotherapy

  12. Point #2 Even if we measure evidence-based processes or outcomes, the potpourri of indicators within and across diseases don’t readily yield a measure of overall clinical benefit

  13. Differences in Clinical Quality (hypothetical) based on evidence-based processes/ outcomes

  14. How Do We Quantify the Net Benefit? • Each of these differences represents a predictable change in expected survival and complications (i.e., each measures a clinical benefit ) • But practical questions remain: • Which is more important, the difference in Hb A1c levels or the difference in BP control? • Should plans, providers work to improve multiple measures modestly, or drive one indicator toward the optimal for all patients? • We need a composite, quantitative measure of net clinical benefit that can be compared across plans, provider groups, systems.

  15. EURO

  16. QALY Quality-adjusted life-year

  17. The QALY • A common metric for measuring clinical quality (both survival and quality of life) • Across interventions (using aspirin, BP lowering) • Across perspectives (patient, provider, purchaser) • Across diseases (diabetes, CHF, CAD, asthma) • Across activities (e.g., chronic disease care, prevention)

  18. Where Do QALY’s Come From?

  19. Creating a Quantitative Metric for Diabetes Systolic Blood Pressure Risk Adjusters Hemoglobin A1C Natural History Model Expected Survival & Complicatons LDL- Cholesterol Adjusted Life-expectancy Aspirin Use

  20. Potential Advantages of Model • Expresses quality in familiar metric – life expectancy • Requires clinical trial evidence clearly evidence-based • Allows exploration to explain differences, which emphasizes population importance of various indicators

  21. Potential Disadvantages/Questions • Will require extensive explanation and transparency of the model to gain acceptance • New evidence will have to be incorporated over time, potentially altering metrics across years • Because it takes a population or public health perspective, will not capture quality of care well for rare conditions (because prevalence too small) • Questions of whether and how to adjust for case-mix differences between population will have to be addressed

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