Can we alter physician behavior by educational methods? Lessons learned from studies of the management and follow-up of hypertension
Bibliographic record
Abstract
INTRODUCTION: As expectations for effective continuing medical education (CME) grow, so, too, does the need to identify relationships among educational methods, physician performance, and patient outcomes associated with specific disease entities. Thus, we set out to review the literature on the effectiveness of physician educational interventions in the management and follow-up of hypertension. METHOD: We searched PubMed and the Research and Development Resource Base in Continuing Medical Education for randomized controlled trials of physician educational interventions. We included only those studies that (a) used replicable educational interventions with > 50% physician involvement and that employed objective methods to measure physician behavior change or patient outcomes, (b) indicated a physician or patient dropout rate of < 30%, and (c) followed outcome measurement for > 30 days. Studies were designated "positive" if one or more of the primary outcome measures demonstrated a statistically significant change in physician performance or health care outcome. RESULTS: We found 12 studies in which 7 different physician educational interventions were employed, alone or in combination, including reminders (computer or chart), formal CME, computerized decision support systems/risk stratification, printed educational materials, academic detailing, continuous quality improvement projects, and disease management aids in patient charts. Of the 12, 7 were positive and 4 were negative. One had mixed results. DISCUSSION: Although physician educational interventions, especially reminders, improved the follow-up of hypertension, they were ineffective in changing blood pressure levels. However, they may have some utility in improving compliance with guideline recommendations.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.034 | 0.127 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.003 | 0.006 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".