Effectiveness of Audit and Feedback and Academic Detailing Interventions to Support Safer Opioid Prescribing in Primary Care
Bibliographic record
Abstract
BACKGROUND: Opioids, prescribed to manage pain, are associated with safety risks. Quality improvement strategies such as audit and feedback and academic detailing may improve prescribing in primary care. METHODS: We used a matched-cohort design with claims databases. Participants were family physicians practicing in Ontario, Canada. The interventions were a voluntary audit and feedback report with or without academic detailing sessions. Physicians in the control group received neither intervention. The primary outcome was mean rate of high-risk opioid prescriptions per 100 patients per month. Data were analyzed comparing monthly percentage change in slope over 12 months before and 18 months after the intervention. Additional analyses considered only the subgroup of higher-prescribing physicians. RESULTS: There were 1469 (25%) physicians in the audit and feedback group, 245 (4%) in the audit and feedback + academic detailing group, and 4211 (71%) matched controls. All groups showed a significant preintervention decline in opioid prescribing. There were no significant between-group differences in opioid prescribing postintervention. Among high-prescribing physicians, there was a significant reduction in the audit and feedback group (% change in slope = -0.37, 95% CI = -0.65 to -0.09, P < .01), but not in the academic detailing group (% change in slope = 0.19, 95% CI = -0.52 to 0.91, P = .59). CONCLUSIONS: This study demonstrated declining secular trends in prescribing and suggests that two large-scale initiatives had limited additional benefits. We found some additional reductions after audit and feedback among the highest-volume opioid prescribers. Future interventions should focus on these physicians for the greatest benefit.
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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 | 0.009 | 0.065 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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, 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".