Implementing an audit and feedback cycle to improve adherence to the Choosing Wisely Canada recommendations: clustered randomized trail
Bibliographic record
Abstract
Abstract Background Audit and Feedback (A&F), a strategy aimed at promoting modified practice through performance feedback, is a method to change provider behaviour and reduce unnecessary medical services. This study aims to assess the use of A&F to reduce antibiotic prescribing for viral infections and antipsychotic prescribing to patients with dementia. Methods Clustered randomized trial of 239 primary care providers in Manitoba, Canada, participating in the Manitoba Primary Care Research Network. Forty-six practices were randomly assigned to one of three groups: control group, intervention 1 (recommendations summary), intervention 2 (recommendations summary and personalized feedback). We assessed prescribing rates prior to the intervention (2014/15), during and immediately after the intervention (2016/17) and following the intervention (2018/19). Physician characteristics were assessed. Results Between 2014/15–2016/17, 91.6% of providers in intervention group 1 and 95.9% of providers in intervention group 2 reduced their antibiotic and antipsychotic prescribing rate by ≥ 1 compared to the control group (77.6%) (p-value 0.0073). This reduction was maintained into 2018/19 at 91.4%. On multivariate regression alternatively funded providers had 2.4 × higher odds of reducing their antibiotic prescribing rate compared to fee-for-service providers. In quantile regression of providers with a reduction in antibiotic prescribing, alternatively funded (e.g. salaried or locum) providers compared to fee-for-service providers were significant at the 80th quantile. Conclusions Both A&F and recommendation summaries sent to providers by a trusted source reduced unnecessary prescriptions. Our findings support further scale up of efforts to engage with primary care practices to improve care with A&F. Trial registration ClinicalTrials.gov NCT05385445, retrospectively registered, 23/05/2022.
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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 | 0.021 | 0.035 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".