Evaluation of an automated feedback intervention to improve antibiotic prescribing among primary care physicians (OPEN Stewardship): a multinational controlled interrupted time-series study
Bibliographic record
Abstract
Tools to advance antimicrobial stewardship in the primary health care setting, where most antimicrobials are prescribed, are urgently needed. The aim of this study was to evaluate OPEN Stewarship (Online Platform for Expanding aNtibiotic Stewardship), an automated feedback intervention, among a cohort of primary care physicians. We performed a controlled, interrupted time-series study of 32 intervention and 725 control participants, consisting of primary care physicians from Ontario, Canada and Southern Israel, from October 2020 to December 2021. Intervention participants received three personalized feedback reports targeting several aspects of antibiotic prescribing. Study outcomes (overall prescribing rate, prescribing rate for viral respiratory conditions, prescribing rate for acute sinusitis, and mean duration of therapy) were evaluated using multilevel regression models. We observed a decrease in the mean duration of antibiotic therapy (IRR = 0.94; 95% CI: 0.90, 0.99) in intervention participants during the intervention period. We did not observe a significant decline in overall antibiotic prescribing (OR = 1.01; 95% CI: 0.94, 1.07), prescribing for viral respiratory conditions (OR = 0.87; 95% CI: 0.73, 1.03), or prescribing for acute sinusitis (OR = 0.85; 95% CI: 0.67, 1.07). In this antimicrobial stewardship intervention among primary care physicians, we observed shorter durations of therapy per antibiotic prescription during the intervention period. The COVID-19 pandemic may have hampered recruitment; a dramatic reduction in antibiotic prescribing rates in the months before our intervention may have made physicians less amenable to further reductions in prescribing, limiting the generalizability of the estimates obtained.IMPORTANCEAntibiotic overprescribing contributes to antibiotic resistance, a major threat to our ability to treat infections. We developed the OPEN Stewardship (Online Platform for Expanding aNtibiotic Stewardship) platform to provide automated feedback on antibiotic prescribing in primary care, where most antibiotics for human use are prescribed but where the resources to improve antibiotic prescribing are limited. We evaluated the platform among a cohort of primary care physicians from Ontario, Canada and Southern Israel from October 2020 to December 2021. The results showed that physicians who received personalized feedback reports prescribed shorter courses of antibiotics compared to controls, although they did not write fewer antibiotic prescriptions. While the COVID-19 pandemic presented logistical and analytical challenges, our study suggests that our intervention meaningfully improved an important aspect of antibiotic prescribing. The OPEN Stewardship platform stands as an automated, scalable intervention for improving antibiotic prescribing in primary care, where needs are diverse and technical capacity is limited.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".