Introduction Factors Affecting Compliance with Diabetes Hypertension Guidelines Julie C. Lowery, PhD, MHSA Sarah L. Krein, PhD, RN Lee A Green, MD, MPH Leon Wyszewianski, PhD Hyungjin Myra Kim, ScD Christine P. Kowalski, MPH • Question: How to get clinicians to change their practices and adopt practice guidelines for treating hypertension in patients with diabetes? VA HSR&D Center of Excellence University of Michigan Departments of Health Management & Policy, Family Medicine Ann Arbor, MI Ann Arbor, Michigan Introduction • Educational strategies are most common, but seldom result in lasting practice changes. • Other strategies work some of the time, but none works all of the time. Introduction • Clinicians are classified into four categories based on their usual responses to new research findings about the effectiveness of clinical practices: seeker, receptive, traditionalist, pragmatist. • Classification is based on scores from 3 subscales: evidence, practicality, conformity. Introduction • New perspective by Wyszewianski and Green provides a guide for selecting the most effective change strategies for a given group of physicians: Wyszewianski L, Green LA. Strategies for changing clinicians’ practice patterns: A new perspective. J Fam Pract 2000;49:461-4. Introduction • A valid physician classification instrument could be used by clinicians and managers, both within and outside VHA, to tailor the design of their research translation or guideline implementation efforts to the types of physicians in their organizations, thereby improving the effectiveness of their efforts. 1 Objective • To evaluate the construct validity and reliability of the physician classification instrument. Methods Primary Hypothesis • Compliance with medication guidelines for diabetes patients with hypertension varies by physician category and guideline implementation strategy. Analysis • Cross-sectional, observational design. • Logistic regression, clustering within provider. • Primary and secondary data collection. • DV: Adherence to medication guidelines (yes/no). • IVs: Site implementation strategies, physician category (defined two different ways). Data Collection: Phase I • IV: Site implementation strategies. Semi-structured telephone interviews were conducted with 2 clinical representatives at 43 participating VA medical centers to determine what strategies were implemented for meeting diabetes hypertension guidelines in the time period from 1999-2001. Data Collection: Phase II • IV: Physician categories. All primary care physicians (PCPs) in the participating VAMCs were sent a one-page questionnaire (the physician classification instrument) regarding their responses to research findings about the efficacy of specific clinical practices. [Instrument.] 2 Data Collection: Phase III • DV: Adherence to medication guidelines (yes/no). Defined as: % of each participating physician’s patients with diabetes and HTN who were on HTN meds at time of elevated BP reading, or who had ↑ in dosage or Δ in med class in 6 months following reading. Data Collection: Phase III • Diabetes = (1) had filled a prescription for diabetes medications or blood glucose monitoring supplies; or, (2) had 1 inpatient or 2 outpatient encounters with a diabetes related ICD-9 code (250.x, 357.2, 362.0-362.1, 366.41) in fiscal year (FY) 1999. • HTN = BP > 140/90 mmHg. • HTN meds: ace inhibitors, beta blockers, calcium channel blockers, alpha blockers, angiotensin II inhibitors, diuretics. • Data sources: VA secondary data sets with data on vitals, medications, diagnoses. • Time frame: October 1998 – March 2000. Data Collection: Phase III Results: Phase I • Data sources: VA secondary data sets with data on vitals, medications, diagnoses. • All sites used some type of educational approach to implement the guidelines (written, presentation, or conference). • Time frame: October 1998 – March 2000. • Patient data were matched to each participating PCP. • Over 90% of sites also provided group or individual feedback on physician performance on the guidelines, and over 75% implemented some type of reminder system. • Minority of sites used monetary incentives, penalties, or barrier reduction. Results: Phase II Results: Phase III • Of 745 questionnaires distributed to primary care physicians, 307 were returned (response rate of 41.2%). • 174 pragmatists (59.8%) • Of 307 questionnaires returned, 16 had missing data, leaving a total of 291 useable surveys/PCPs. • 1 traditionalist (0.3%). • 80 receptives (27.5%). • 36 seekers (12.4%). • Factor analysis confirmed the 3-factor psychometric scaling used previously (2 questions dropped). 3 Results: Phase III • The total number of diabetes patients in the 42 participating sites was 208,653 in 1999. • Patients in the diabetes cohort were assigned to participating PCPs if more than 50% of a patient’s outpatient medical clinic visits were to a participating PCP. Results: Phase III • 1st method of measuring interaction between intervention strategy and physician category (concordance scores) Æ no association with guideline adherence. • Final dataset: 1174 diabetes patients had BP data, had HTN, and could be matched to 163 of our participating PCPs. Results: Phase III • 2nd method of measuring interaction between intervention strategy and physician category: • Interventions were coded as the number of educational interventions, barrier removal interventions, and motivational interventions (3 variables). • Physician characteristics coded as scale scores (3 variables). Results: Phase III Testing full model with all 2-way interactions between interventions and physician scale scores: • The only interaction that approached significance was conformity with barrier removal (p = 0.07). Barrier reduction was associated with improved guideline concordance for the least conforming physicians, but not for the conforming physicians. [Figure] • Significant positive association of barrier removal with guideline concordance (p = 0.03). Results: Phase III Testing without interactions: • Only conformity scale was significantly associated with guideline concordant-care. Lower conformity was associated with better decisions. • No association between guideline interventions and guideline concordant care. Results: Reliability • One-year test-retest results. Of the 291 participating providers with useable surveys, 263 (90%) completed follow-up surveys one year later. The correlations for the three subscales were as follows: – Evidence: .75 – Practicality: .68 – Non-comformity: .75 • These findings suggest that physician scores on the three subscales remain relatively stable over time, indicating that the concept of physician response to new information is more of a trait than a state. 4 Discussion Discussion • Main conclusions: – Non-conformity is associated with better guideline adherence. – Barrier reduction is associated with better guideline adherence. – As conformity increases, the impact of barrier reduction decreases. • Guideline implementation strategies that were designed to reduce, or at least not increase, physician time demands and task complexity were the only ones that improved guideline adherence—particularly for physicians low on the conformity scale. In other words, the more physicians were willing to practice differently from the local norm, the more they took advantage of system changes to change their own practices. • Education may have been necessary, but it was clearly not sufficient; all sites included education in their mix of strategies, but those doing a great deal of it saw no more effect than those doing the minimum. • Incentives had no discernible effect. Discussion Discussion • Primary hypothesis was not valid—no association between physician type/intervention interaction (measured by concordance scores) and guideline adherence. • Possible explanation: time constraints of current delivery environment? • Is an instrument for measuring physician type useful? • Limitation: Small sample size. • Focus of interventions should be at the system or organizational level, rather than the provider. • Results consistent with other studies Æ quality improvement efforts should focus on addressing facility-level performance variations, because of the small amount of variation in performance found at the provider level. 5