Clinic Characteristics that May Impact Diabetes Care Outcomes and Costs: Conceptual Approach

1. Clinic Characteristics that May Impact Diabetes Care Outcomes and Costs: Conceptual Approach Patrick J. O’Connor MD MPH Robin R. Whitebird Ph.D. Jon C. Christianson Ph.D.1 Paul E. Johnson Ph.D.1 Stephen E. Asche MA Leif I. Solberg MD A. Lauren Crain Ph.D. William A. Rush Ph.D. Gail Amundson MD Andy Van de Ven Ph.D. HealthPartners Research Foundation & The University of Minnesota 1University of Minnesota

2. PURPOSE We conducted an observational study to identify patient, physician, & primary care clinic characteristics that predict glycated hemoglobin (A1c) levels and costs of care for diabetes patients over a 3-year period (AHRQ RO1 HS11919)

3. METHODS The study included 2,117 adults with an established diagnosis of diabetes, 349 primary care physicians who provided care to these patients, 84 clinics, and 19 Medical Groups. Data were collected for a 36-month period that ended in 2002; there were 5,199 patient-years of data available for this analysis. Hierarchical analysis was used to accommodate the nested study design and data.

4. Significant variation in costs and quality of care would be located at the patient, physician, clinic, and medical group levels. After control for patient and physician characteristics, specific clinic and/or medical group characteristics significantly related to quality and costs. Hypotheses

5. We conceptualized four principal domains of clinic characteristics: Work Environment Clinic Systems to Improve Care Quality Improvement Strategies Financial Stability and Systems Conceptual Approach

6. Affiliation & Participation Recognition & Compensation Control over Work Processes Satisfaction with Leadership Satisfaction with Medical Group & Clinic Does Quality Need Improvement? Is Improvement a Priority Here? Work Environment

7. Expanded Roles for Nurses/Teams Registries Electronic Medical Records Monitoring of Clinical Status Prioritization based on Risk, RTC Active Interventions: Visit Planning Active Outreach Patient Activation Clinic Systems to Improve Care

8. Leadership Stability Improvement Priorities Financial Incentives for Quality Resources Allocated to Improve Quality External Accountability for Improvement Investments in Information Infrastructure Feedback to Physicians (Quality, Costs) Quality Improvement Strategies

9. Ownership of Medical Group Longevity of Medical Group Recent Expansion or Contraction Perceived Financial Pressures Profitable in Last 3 Years? Stability of Leadership and Physicians Capitated or FFS? Insurance Mix, Uninsured Physician Compensation Arrangements Financial Stability & Systems

10. A1c levels improved overall across these medical groups and clinics: 1999 mean A1c = 7.52 2000 mean A1c = 7.32 2001 mean A1c = 7.26 Results: A1c Values

11. 80+% of variance at patient level 5-10% at clinic level 5-7% at medical group level Intercept-Only MLwiN Model

12. Many Patient Factors Were Associated with Better A1c: • Older Age (p < 0.001) • Higher Educational Level (p=0.008) • Duration of Diabetes < 10 years (p < 0.001) • Greater Comorbidity (p < 0.001) • Readiness to Change (interaction effect)* • Drug Intensification (p < 0.01)* • Patient gender (p=0.19), physician gender (p=0.16), primary care physician specialty (p=0.18), and years of practice experience (p=0.21) were not associated with A1c level.

13. After adjustment for patient and physician characteristics, clinic factors associated (p<0.05) with best A1c improvement: • Stable Medical Leadership • Use of a Diabetes Patient Registry • Diabetes Educator On-site at Clinic • Financial Incentives to Improve Care • Financial Pressures on Clinic

14. Clinic factors associated with future costs after adjustment for patient and physician factors: • Use of a Diabetes Patient Registry • Physician Team Meetings to Discuss Care • Feedback to Docs on Costs of Care • Monitoring Physician Drug Use Patterns

15. Large number of clinics, physicians, and patients with diabetes. Use of hierarchical analytic models to accommodate nested data. Uniform data collection procedures and standards at all clinics. Single data source for all cost data. Strengths of Study

16. Observational study Insufficient variation in measures at the level of clinics or medical groups Low power at the level of the medical groups (N=19) Something in the Minnesota air would wash out the impact of things we wanted to know about (unmeasured confounder) Potential Limitations

17. Most variation in quality of care was at the patient level, with smaller but significant variation at the physician level Little significant variation was noted at the clinic level (N=84) or at the medical group level (N=19) Conclusions 1

18. After control for patient and provider characteristics, we identified specific characteristics of clinics that predict better-than-average glycemic control improvement over a 3-year period, and several factors related to increased or decreased costs. Conclusions 2

19. Translate clinical measures into QALYs and/or a composite risk index Conduct analysis to assess factors associated with efficiency at the level of the patient, physician, clinic, and medical group Focus More Attention on the Physician-Patient Dyad to Improve Care Future Directions