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
VA HSR&D Center of Excellence
University of Michigan Departments of Health
Management & Policy, Family Medicine
Ann Arbor, MI
Ann Arbor, Michigan
Introduction
• Question: How to get clinicians to
change their practices and adopt
practice guidelines for treating
hypertension in patients with diabetes?
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
• 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
• 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
• 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
Objective
• To evaluate the construct validity and
reliability of the physician classification
instrument.
Primary Hypothesis
• Compliance with medication guidelines
for diabetes patients with hypertension
varies by physician category and
guideline implementation strategy.
Methods
• Cross-sectional, observational design.
• Primary and secondary data collection.
Analysis
• Logistic regression, clustering within
provider.
• 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.]
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.
Data Collection: Phase III
• Data sources: VA secondary data sets
with data on vitals, medications,
diagnoses.
• Time frame: October 1998 – March
2000.
• Patient data were matched to each
participating PCP.
Results: Phase I
• All sites used some type of educational
approach to implement the guidelines
(written, presentation, or conference).
• 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,
Results: Phase II
• Of 745 questionnaires distributed to primary
care physicians, 307 were returned
(response rate of 41.2%).
• Of 307 questionnaires returned, 16 had
missing data, leaving a total of 291 useable
surveys/PCPs.
• Factor analysis confirmed the 3-factor
psychometric scaling used previously (2
questions dropped).
Results: Phase III
• 174 pragmatists (59.8%)
• 80 receptives (27.5%).
• 36 seekers (12.4%).
• 1 traditionalist (0.3%).
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.
• Final dataset: 1174 diabetes patients had
BP data, had HTN, and could be matched to
163 of our participating PCPs.
Results: Phase III
• 1st method of measuring interaction between
intervention strategy and physician category
(concordance scores)  no association
with guideline adherence.
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 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: 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: 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.
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.
Discussion
• 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
• 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.
Discussion
• Focus of interventions should be at the
system or organizational level, rather than
the provider.
• Results consistent with other studies 
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.