1. Using EHRs to Understand and Reduce Racial and Ethnic Disparities in Diabetes Care: the Translating Research into Action for Diabetes (TRIAD) Study Arleen F. Brown, MD, PhD UCLA General Internal Medicine & Health Services Research

2. Diabetes Disparities Type 2 diabetes disproportionately affects socially disadvantaged groups Some racial/ethnic minority patients and those of low SES: –High diabetes prevalence –Higher rates of complications Evidence-based therapies underused Racial/ethnic minorities clustered in health plans that have fewer resources and receive lower quality ratings (and are less likely to have EHRs) Interventions to reduce racial, ethnic and socioeconomic disparities may have a profound impact on the morbidity and mortality associated with diabetes

3. Factors that Contribute to Diabetes Disparities Financial barriers: insurance, income Personal barriers: language, literacy Health Care System Factors –Structure / Organization –Quality of Care Community-level barriers / resources –Clinics, pharmacies –Safe places to exercise –Food resource environment –Social supports

4. Translating Research Into Action for Diabetes TRIAD Multicenter study of diabetes care in managed care Objective: To determine the system-level disease management strategies and patient factors that influence the processes and outcomes of diabetes care, with special attention to vulnerable populations.

5. TRIAD Six Translational Research Centers with 10 managed care health plans and 68 provider groups that serve approximately 180,000 persons with diabetes Stratified random sample of adults with diabetes –18 years or older –Community dwelling –English or Spanish speaking –At least one claim for health service use in the previous 18 months Diabetes Care 2002;25:386-9

6. Kaiser Permanente Northern California Pacific Health Research Institute U Michigan Indiana U UCLA UMDNJ CDC TRIAD Sites and Sponsoring Agencies NIDDK National Institute of Digestive and Diabetes and Kidney Disorders - Sponsor Translational Research Centers Centers for Disease Control - Sponsor

7. 10 health plans (n = 500 to 2000 per plan) 68 physician groups with > 50 members in sampling frame TRIAD Nested Sampling Scheme Sampling scheme: Aimed for equal numbers from each physician group within health plan, so from 50 - 1500 per physician group

8. TRIAD Data Sources Patient Surveys (Waves 1, 2, 3) Medical Record Review (2 sites with EHRs had to go low tech) Administrative Data Provider Group Surveys Health Plan Surveys Geocoded data / U.S. Census data “Data Harmonization” (>6 years, mainly due to admin data) –Differences between administrative data  Not everyone had data on race/ethnicity –Human subjects –Legal issues - health plan data “proprietary”

9. System Factors Processes of Care (Quality of Care) Health Outcomes Health System structure Disease Mgmt. strategies Performance feedback Physician reminders Patient reminders Guideline use Formal Case Mgmt. Patient educ. resources Mgmt of referral care Clinician payment and incentives Cost-containment strategies Hb A1c test frequency Blood pressure assessment Lipid testing frequency Retinal examinations Microalbuminuria testing Foot examinations Smoking cessation counseling Aspirin prescription Health status Quality of life Glycemic control CVD risk factor control Foot problems Retinopathy Nephropathy Cardiovascular disease Mortality Utilization and costs TRIAD Conceptual Model for System Factors 1. Health plan surveys 2. Provider group surveys 3. Patient surveys 1. Medical record reviews 2. Patient surveys 3. Administrative surveys 1. Medical record reviews 2. Patient surveys 3. Administrative data.

10. TRIAD I Sample by Demographics

11. Hispanic/Latino 15% Asian & Pacific Islander 15% African American (Non-Hispanic) 15% Other 8% White (Non-Hispanic) 39% TRIAD I Sample by Ethnicity (N= 11,927) Unknown or Missing 8%

12. Quality Indicators Process of Care Dilated eye exam 1,2 Foot exam 1,2 Flu shot 1,2 A1c measured 1 LDL-C measured 1 Nephropathy assessment 1 Aspirin advice or use 1,2 Intermediate Outcomes Systolic blood pressure 1 Diastolic blood pressure 1 A1c level 1 LDL-C level 1 1 Medical record only 2 Medical record and survey

13. Quality of Care : LDL Measurement Poorest Performing Groups (20th percentile) Registry Feedback Care Mgmt AA disparity in provider groups with the least intensive management strategies *P<.05 in comparison to whites; Adjusted for demographic and clinical characteristics Duru et al., Medical Care, 2006

14. Quality of Care : LDL Measurement Best and Poorest Performing Groups (80 th and 20th percentile) Registry Feedback Care Mgmt AA disparity attenuated in the provider groups with the most intensive management strategies *P<.05 in comparison to whites; Adjusted for demographic and clinical characteristics Duru et al., Medical Care, 2006

15. Quality of Care: Influenza Vaccination Poorest Performing Groups (20th percentile) Registry Feedback Care Mgmt *P<.05 in comparison to whites *P<.05 in comparison to whites; Adjusted for demographic and clinical characteristics Duru et al., Medical Care, 2006 AA disparity in provider groups with the least intensive management strategies

16. Quality of Care Influenza Vaccination Best and Poorest Performing Groups (20th & 80th percentile) Registry Feedback Care Mgmt *P<.05 in comparison to whites *P<.05 in comparison to whites; Adjusted for demographic and clinical characteristics Duru et al., Medical Care, 2006 AA disparity NOT attenuated in the provider groups with the most intensive management strategies Suggests a need for culturally- tailored interventions

17. Understanding and Reducing Diabetes Disparities Can EHRs Address Limitations/Unanswered Questions Limitation / QuestionEHR Solution? Little/no data on race/ethnicity or SES If standardized data collection on race/ethnicity Important outcomes not routinely collected/documented (BP, height, depression, pain) If standardized data collection for these outcomes Relatively short follow-up with traditional observational, prospective research Potential for long term, prospective data Better data on complications Little data on groups other than Whites, Blacks, Latinos Monitor changes in quality and outcomes for other racial/ethnic subgroups in different centers

18. Understanding and Reducing Diabetes Disparities Can EMRs Address Limitations/Unanswered Questions Limitation / QuestionEHRs Solution? Benchmark against other regions or clinical settings Standardized data collection across different sites The interplay between health care system-level characteristics and health disparities Can the EMR incorporate features health plans / provider groups; characteristics of providers? Which health system interventions reduce disparities? …which exacerbate disparities? Impact of diabetes quality improvement in general vs. targeted, culturally tailored diabetes interventions Which health systems should we target to have the largest impact on disparities within a system? … at a population level? Racial/ethnic minority patients receive care from a small number of providers and/or resource-poor settings

19. Recommendations Recognize disparities as an important quality problem Collect via EHR relevant and reliable patient data on race/ethnicity, socioeconomic position, and key health status measures and health outcomes Collect via EHR relevant and reliable provider and health care system data Stratify performance measures by race/ethnicity and socioeconomic position to track changes in disparities over time for specific conditions Modified from Fiscella, et al. JAMA, 2000