1. Are efforts to improve technical quality of care competing with improving clinical outcomes?: The case of the elevated A1c Parchman ML, Pugh JA, Romero RL University of Texas Health Science Center-San Antonio VERDICT Health Services Research Program, South Texas Veterans Health Care System

2. Background: Quality of Healthcare Health Plan & Providers Performance  Audit & feedback  QI Improvement programs Quality Indicators focus on delivery or receipt of a service, e.g.  Immunization given  Screening test performed  Foot exam done Feasibility/Data Availability: ease of administrative data access

3. Understanding Quality of Care High level administrative data  Health Plan Performance  Clinic-level Performance  Provider-level Performance The “black box” of the clinical encounter

4. Clinic-Level Performance: Process Measures Done, CVD Risks Good: A1c<7; BP<130/80; LDL-C< 100

5. Objective: Inside the “Black Box” To examine the relationship between performance of technical/process measures of quality of care inside the primary care encounter and medication intensification for poorly controlled diabetes Is the likelihood that a change in medication occurred among patients with an elevated A1c associated with process measure performance?

6. Methods: Sample 20 diverse primary care clinics across South Texas  12 unaffiliated with 1-2 physicians  2 single specialty groups with 3 or more physicians  2 VA clinics  1 Federal Qualified Community Health Center  3 city/county clinics ~10 consecutive patients with T2DM in each clinic

7. Methods: Data Collection Trained Observer: Direct observation of encounter Patient Exit Survey Chart review  Most recent A1c value  Number of medications  Indicated diabetes service done in past year

8. Direct Observation of Encounter Length of visit (minutes) Change in a hypoglycemic medication (yes/no) Number of topics and issues raised by the patient or the physician Number of indicated diabetes services done

9. Indicated Diabetes Services If not done in past year by chart review  Foot exam  Referral for eye exam  A1c ordered  Lipid panel ordered  Urine micro-albumin ordered  BP measured For analysis converted to “all indicated done: yes/no”

10. Analysis Dependent: change in medication Independent: all indicated services done (yes/no) Co-variates  Length of visit  Number of topics/issues raised by patient or physician,  Most recent A1c level  Number of chronic medications.

11. Results 195 patients encounters observed  98 (55.4%) had an A1c above 7.0 and the analysis was limited to this group 35(35.6%) had a change in medication. All indicated diabetes services done in 36 (37%) of encounters Visit Length, minutes (mean 16.8, SD 7.8)  Less than 10: 22.3%  10 to 19: 40.4%  20 or more: 37.2%

12. Number Indicated and % Done

13. All Indicated Services Done?

14. Final Model (n=98) OR (95% CI) All Done0.24 (0.08, 0.78) Length of visit1.11 (1.01, 1.21) # patient questions0.57 (0.38, 0.84) MD topics1.03 (.91, 1.16) Recent A1c level1.20 (.89, 1.62) # Medications1.26 (1.05, 1.51)

15. Discussion In visits where all DM indicated services were performed, patients were less likely to have a change in hypoglycemic medication if A1c > 7% High levels of competing demands:  16.7 minute visits  17.7 issues/topics/questions by pt and MD Unintended consequences?

16. Performance Measurement Performance measures for quality of care often driven by available data Settle for measures that are simple and easy to gauge? Is the “good” the enemy of the “best.” Assumption: if we measure it, outcomes will improve  Measurement necessary but not sufficient

17. Conclusions “HEDIS-style performance measures…represent inefficient and sometimes counter-productive standards for improving clinical outcomes.”  Hayward RA, NEJM 2007

18. Discussion Studies of strategies to “un-burden” the demands on primary care clinicians during patient encounters are needed  Panel Size?  Pharmacists led-clinics  Group clinics  Caution on adding additional demands on patients for additional trips and visits and co-pays Patient-Centered Goal: prevent diabetes complications by controlling A1c, BP, lipids

19. Acknowledgements The research reported here was supported by:  Agency for Healthcare Research and Quality (K08 HS013008-02), Kay Anderson, Project Officer  Department of Veterans Affairs, Veterans Health Administration, Health Services Research and Development Service. The views expressed in this article are those of the authors and do not necessarily represent the views of the Department of Veterans Affairs. Members, South Texas Ambulatory Research Network (STARNet) Contact: parchman@uthscsa.edu