Updated validation of AHRQ Prevention Quality Indicators in the USA

Transcription

1 Updated validation of AHRQ Prevention Quality Indicators in the USA Patrick S. Romano, MD MPH UC Davis Center for Healthcare Policy and Research Organization for Economic Cooperation and Development October 22, 2009

2 PQIs: Potentially Avoidable Hospitalizations Admissions for diagnoses that may have been prevented or ameliorated with access to high-quality outpatient care Two independently developed measure sets described in the 1990s literature John Billings Joel Weissman Strong independent negative correlations between self-rated access and avoidable hospitalization Correlations between avoidable hospitalization and: household income at zip code level (neg) uninsured or Medicaid enrolled (pos) maternal education (neg) Primary care physician to population ratio (neg) Weaker associations for Medicare populations

3 Current uses of the PQIs in the USA: Four examples National Healthcare Quality Report, Commonwealth Fund, California: Public health agencies tracking and comparing health system performance across counties, states Wisconsin Medicaid program: Comparing performance of managed care plans for low-income persons Dallas-Fort Worth Hospital Council: Exploring how community health affects hospitals General Motors: Estimating potential return on investment with improving primary care access in communities with GM employees.

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6 Measures recommended for state Medicaid programs by the Foundation for Accountability Ambulatory care sensitive conditions ( potentially avoidable hospitalizations ) Angina Adult asthma/pediatric asthma Chronic obstructive pulmonary disease Congestive heart failure Diabetes (short-term and long-term complications, uncontrolled) Lower extremity amputation with diabetes Hypertension Other potentially avoidable conditions Perforated appendix Low birth weight

7 Percent Percent Percent Percent Evaluating Medicaid managed care programs in Wisconsin % ICare Enrollees with CHF Hospitalized for CHF % ICare Enrollees with Asthma Hospitalized for Asthma: 1998 & % ICare Enrollees with COPD Hospitalized for COPD % ICare Enrollees with Diabetes Hospitalized for Diabetes: 1998 &

8 Reducing ED visits for PQIs in Medicaid managed care Comparison of total E.R. visits and ambulatory care sensitive condition (ACSC) E.R. visits for I-Care and matched FFS recipients, % 90% 80% 70% 60% 50% 40% 30% 20% 10% 0% I-Care (n=1722) Comparable FFS (n=1722) No ER visit ER, not for ACSC ER for ACSC

9 Community Health Assessments using PQIs to understand role of hospitals

10 Risk Adjusted Rates per 100,000 Population 1 to to to to > DI Hospitals AHRQ Prevention Quality Indicators Congestive Heart Failure Admission Rate Named counties without shading have a Risk Adjusted rate of zero. Congestive Heart Failure Admission Rate Congestive heart failure (CHF) can be controlled in an outpatient setting for the most part; however, the disease is a chronic progressive disorder for which some hospitalizations are appropriate. Texas Hospital Inpatient Discharge Public Use Date File, FY2002. Texas Health Care Information Council, Austin, Texas. December, (PQI 08) Rates per 100,000 Cases County Numerator (Outcome) Denominator (Population) Observed Risk Adjusted Confidence Interval (95%) Stat. Sig. State of Texas 66,822 15,882, BOSQUE 21 13, ( 0.0, 0.0 ) CAMP 80 8, ( 664.7, ) - COLLIN , ( 391.6, ) + COMANCHE ,233 1, ( 766.0, ) - COOKE 60 28, ( 98.5, ) + DALLAS 6,412 1,631, ( 516.7, ) - DELTA 32 4, ( 384.0, ) o DENTON , ( 435.1, ) + EASTLAND 26 14, ( 0.0, 0.0 ) ELLIS , ( 471.0, ) o ERATH 97 24, ( 300.8, ) + FANNIN , ( 513.7, ) - FRANKLIN 48 7, ( 346.4, ) o GRAYSON , ( 560.3, ) - HAMILTON 19 6, ( 0.0, 61.9 ) + HENDERSON , ( 500.2, ) o HILL , ( 669.0, ) - HOOD , ( 269.9, ) + HOPKINS 37 24, ( 47.7, ) + HUNT , ( 567.4, ) - JACK 12 6, ( 49.1, ) + JOHNSON , ( 614.7, ) - KAUFMAN , ( 681.4, ) - LAMAR , ( 539.2, ) - MONTAGUE 21 14, ( 0.0, 0.0 ) MORRIS 64 10, ( 368.2, ) o NAVARRO , ( 515.2, ) - PALO PINTO 22 20, ( 0.0, 10.3 ) + PARKER , ( 317.9, ) + RAINS 38 8, ( 262.9, ) o ROCKWALL , ( 332.4, ) + SOMERVELL 9 5, ( 39.8, ) + STEPHENS 6 7, ( 0.0, 0.0 ) TARRANT 3,688 1,118, ( 443.6, ) + TITUS 40 19, ( 136.5, ) + VAN ZANDT , ( 391.0, ) o WISE 58 39, ( 164.2, ) + WOOD , ( 513.0, ) - YOUNG 15 13, ( 0.0, 0.0 ) + = County s RA rate significantly lower than State RA rate - = County s RA rate significantly higher o = No statistical difference 2004, Dallas-Fort Worth Hospital Council - Data Initiative For Hospital Internal Use Only April, 2005

11 General Motors: Impact of PQI admissions on employer-financed health care QI Name Potential cost savings if number of admissions were reduced by specified percentage County name (all counties in MI listed), average cost of admission for QI specified, total number of cases, and total cost

12 General Motors mapped PQI data Name of Indicator and Data Year in Map Title Data quintiles. Green is the lowest 20% or the lowest rates. Red is the highest 20% or the highest rates. Symbol indicating number of GM covered beneficiaries, number below is average in the group. Counties with high indicator rates and higher number of beneficiaries

13 Using GM-mapped data Integrate action plans with other Community Initiatives projects Pay for Performance for providers in specific counties to reduce admission rates Coordination with other Community Stakeholders to achieve desired improvement Funding to implement projects at a community level Focus on indicators with highest potential cost savings

14 Area Potential uses of PQIs QI Comparative Reporting Payor X X Provider X X X X Pay for Performance Current application Extended applications 1 We initially assessed the internal quality improvement application for large provider groups. Following our initial rating period, panelists expressed interest in applying select indicators to the long term care setting and these applications were added to our panel questionnaire.

15 Revalidation methods Clinical Panel review using new hybrid Delphi/Nominal Group technique Two groups: Core and Specialist Core assesses all; Specialist only those applicable to their specialty Three indicator groups: Acute, Chronic, Diabetes Two panels: Delphi Nominal Group

16 Delphi vs. Nominal Delphi group Advantages: Larger, hence better reliability, more points of view, less chance for one panelist to pull the group Disadvantage: Less communication and cross-pollination across panelists, less ability to discuss and refine details of indicators/evaluation Nominal group Advantages: Can discuss details, facilitate sharing of ideas Disadvantages: Limited in size and therefore in representation, one strong panelist can flavor group and therefore poorer reliability

17 Composition of panels Characteristic Delphi Group (n = 42) Gender Male Female Urban/Rural 1 Urban Suburban Rural Multiple/All areas served Academic Affiliation 1 Academic practice Non-academic practice Any academic affiliation Underserved population in practice Funding 1 Public Private and/or Non-profit Multiple sources Nominal Group (n = 23)

18 Specialties represented Specialty Delphi Panel Nominal Panel Internal Medicine 5 3 Family Medicine 4 1 Geriatric Medicine 2 2 Public Health Physician 4 0 Emergency Medicine 3 2 General Nurse Practitioner 2 1 Endocrinology 4 2 Vascular Surgery 2 1 Diabetes Outpatient 1 1 Management Nephrology 0 1 Cardiology 4 3 Pulmonology 3 2 Asthma Specialist 1 0 Pulmonary Rehabilitation 1 0 Infectious Disease 2 2 General Surgery 3 2 Urology 1 0

19 Panel Process: Exchange of Information Delphi Dephi rating Results: initial rating Delphi panel re-rates Delphi comments Nominal comment 1 st round results to panelists prior to call Diabetes call Acute call Call summaries to panels Final ratings Nominal Nominal rating Results: Initial rating Chronic call Nominal panel re-rates

20 Quality Improvement Applications Indicator Provider COPD and Asthma (40 yrs +) + Asthma ( < 39 yrs) Hypertension + Angina CHF Perforated Appendix + Diabetes Short Term Complications Diabetes Long-Term Complications Lower Extremity Amputation Bacterial Pneumonia UTI Dehydration Major Concern Regarding Use, Some Concern General Support Full Support + Either Delphi or Nominal Panel reported higher level of support for measure than shown

21 Comparative Reporting Applications Indicator Area Provider COPD + Asthma ( < 39 yrs) + + Hypertension + Angina CHF + Perforated Appendix + + Diabetes Short Term + Diabetes Long-Term + LE Amputation Bacterial Pneumonia UTI Dehydration Major Concern Regarding Use, Some Concern General Support Full,Support + Either Delphi or Nominal Panel reported higher level of support for measure than shown

22 Potential interventions to reduce hospitalizations Acute Chronic Area Access to primary care/urgent care Payor Coverage of medications Coverage of auxiliary health services (e.g. at home nursing) Access to primary care/urgent care Provider Quality nursing triage Patient education Accurate/rapid diagnosis and treatment Appointment availability Outpatient treatment of complications Access to care Lifestyle modifications Coverage of medications Coverage of comprehensive care programs Coverage of auxiliary health services (e.g. at home nursing) Disease management programs Lifestyle modification incentives Education, disease management Lifestyle medication interventions Comprehensive care programs, care coordination, auxiliary health services

23 Potential improvements Defining the numerator One admission per patient per year? Using related principal diagnoses with target secondary diagnoses (i.e., pneumonia with COPD) Consider excluding first hospitalization before chronic condition diagnosed Defining the denominator Identifying patients with chronic diseases (mulitple diagnoses from outpatient claims, population survey rates, pharmaceutical data Requiring minimum tenure with payor or provider or minimum duration of residence in an area

24 Risk adjustment Demographics Age and gender highly rated as important Race/ethnicity depending on indicator (i.e., diabetes) Disease severity Historical vs. current data Comorbidity Lifestyle associated risk and compliance Smoking, obesity Pharmacy records Socioeconomic status May mask true disparities in access to care Panel felt benefits of inclusion outweighed problems

25 Policy implications Ensuring true quality improvement Shifting risk (adverse selection) Manipulating coding Cost/burden of data collection to support better risk adjustment Different perspectives of different stakeholders Does avoiding hospitalization really reflect the best quality and value?

26 Acknowledgments Project team: Sheryl Davies, MA (Stanford) Kathryn McDonald, MM (Stanford) Eric Schmidt, BA (Stanford) Ellen Schultz, MS (Stanford) Olga Saynina, MS (Stanford) Jeffrey Geppert JD (Battelle) Patrick Romano, MS, MD (UC Davis) This project was funded by a contract from the Agency for Healthcare Research and Quality (# )