Assessing the impact of discontinuation of formulary prior authorization on antibiotic prescribing
Bibliographic record
Abstract
Objective: To compare prescribing patterns of restricted antimicrobials before and after the removal of prior authorization and to develop a prospective audit and feedback program to mitigate the potential inappropriate prescribing of these antimicrobials. Methods: An interrupted time-series analysis assessing the trends in antibiotic use was conducted between May 2020 and February 2023 in large urban hospitals, where all ASP activities were discontinued in May 2022 and a pilot prospective audit and feedback (PAF) program was initiated in January 2023. Results: The collective change in restricted antibiotic utilization after the removal of prior authorization was trending towards increased utilization but was not statistically significant. With the PAF program, 9.8% of patients were identified by the antimicrobial stewardship pharmacists as requiring intervention. Within these patients, 19 different recommendations were made, with the most common being to narrow the therapeutic spectrum (47.4%). Stewardship interventions suggestions were accepted (full and partial) 69.2% of the time. Conclusions: Although there were some small statistically significant changes detected for a few antibiotics, there were no situations where those changes remained significant after appropriate controls were added to the analyses. As such, the intervention may not have had any statistically significant impact on DDDs of the studied antibiotics.
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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 | 0.012 | 0.051 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".