Changing physician behavior: what works?
Bibliographic record
Abstract
OBJECTIVES: There are various interventions for guideline implementation in clinical practice, but the effects of these interventions are generally unclear. We conducted a systematic review to identify effective methods of implementing clinical research findings and clinical guidelines to change physician practice patterns, in surgical and general practice. STUDY DESIGN: Systematic review of reviews. METHODS: We searched electronic databases (MEDLINE, EMBASE, and PubMed) for systematic reviews published in English that evaluated the effectiveness of different implementation methods. Two reviewers independently assessed eligibility for inclusion and methodological quality, and extracted relevant data. RESULTS: Fourteen reviews covering a wide range of interventions were identified. The intervention methods used include: audit and feedback, computerized decision support systems, continuing medical education, financial incentives, local opinion leaders, marketing, passive dissemination of information, patient-mediated interventions, reminders, and multifaceted interventions. Active approaches, such as academic detailing, led to greater effects than traditional passive approaches. According to the findings of 3 reviews, 71% of studies included in these reviews showed positive change in physician behavior when exposed to active educational methods and multifaceted interventions. CONCLUSIONS: Active forms of continuing medical education and multifaceted interventions were found to be the most effective methods for implementing guidelines into general practice. Additionally, active approaches to changing physician performance were shown to improve practice to a greater extent than traditional passive methods. Further primary research is necessary to evaluate the effectiveness of these methods in a surgical setting.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.036 | 0.112 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.010 | 0.006 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".