Audit and feedback: effects on professional practice and health care outcomes
Bibliographic record
Abstract
BACKGROUND: Audit and feedback has been identified as having the potential to change the practice of health care professionals. OBJECTIVES: To assess the effects of audit and feedback on the practice of health professionals and patient outcomes. SEARCH STRATEGY: We searched MEDLINE up to June 1997, the Research and Development Resource Base in Continuing Medical Education, and reference lists of related systematic reviews and articles. SELECTION CRITERIA: Randomised trials of audit and feedback (defined as any summary of clinical performance of health care over a specified period of time). The participants were health care professionals responsible for patient care. DATA COLLECTION AND ANALYSIS: Two reviewers independently extracted data and assessed study quality. MAIN RESULTS: Thirty-seven studies were included, involving more than 4977 physicians. The reporting of study methods was inadequate for almost all studies. In 31 out of 37 studies the randomisation process could not be determined. Information regarding data analysis was also lacking. For example, power calculations were not mentioned in 27 out of 37 studies. A variety of behaviours were targeted including the reduction of diagnostic test ordering, prescribing practices, preventive care, and the general management of a problem, for example hypertension. Twenty-eight studies measured physician performance, one study targeted patient outcomes in diabetes and the remaining eight studies measured both physician performance and patient outcomes. The relative percentage differences ranged from -16% to 152%. The clinical importance of the changes was not always clear. REVIEWER'S CONCLUSIONS: Audit and feedback can sometimes be effective in improving the practice of health care professionals, in particular prescribing and diagnostic test ordering. When it is effective, the effects appear to be small to moderate but potentially worthwhile. Those attempting to enhance professional behaviour should not rely solely on this approach.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Review About the Canadian research system: no · About a Canadian topic: no | Systematic review | low |
| gpt | no category Domain: not available · Genre: Review About the Canadian research system: no · About a Canadian topic: no | Systematic review | high |
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 | 0.128 | 0.501 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.009 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.005 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
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, unvalidatedLabeled directly by 2 models reading the full record.
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".