Interventions to Reduce Unnecessary Antibiotic Prescribing
Bibliographic record
Abstract
BACKGROUND: Overuse of antibiotics in ambulatory care persists despite many efforts to address this problem. We performed a systematic review and quantitative analysis to assess the effectiveness of quality improvement (QI) strategies to reduce antibiotic prescribing for acute outpatient illnesses for which antibiotics are often inappropriately prescribed. RESEARCH DESIGN AND METHODS: We searched the Cochrane Collaboration's Effective Practice and Organisation of Care database, supplemented by MEDLINE and manual review of article bibliographies. We included randomized trials, controlled before-after studies, and interrupted time series. Two independent reviewers abstracted all data, and disagreements were resolved by consensus and discussion with a third reviewer. The primary outcome was the absolute reduction in the proportion of patients receiving antibiotics. RESULTS: Forty-three studies reporting 55 separate trials met inclusion criteria. Most studies (N = 38) addressed prescribing for acute respiratory infections (ARIs). Among the 30 trials eligible for quantitative analysis, the median reduction in the proportion of subjects receiving antibiotics was 9.7% [interquartile range (IQR), 6.6-13.7%] over 6 months median follow-up. No single QI strategy or combination of strategies was clearly superior. However, active clinician education strategies trended toward greater effectiveness than passive strategies (P = 0.096). Compared with studies targeting specific conditions or patient populations, broad-based interventions extrapolated to larger community-level impacts on total antibiotic use, with savings of 17-117 prescriptions per 1000 person-years. Study methodologic quality was fair. CONCLUSIONS: QI efforts are effective at reducing antibiotic use in ambulatory settings, although much room for improvement remains. Strategies using active clinician education and targeting management of all ARIs (rather than single conditions in single age groups) may yield larger reductions in community-level antibiotic use.
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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 | 0.015 | 0.093 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.005 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 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, 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".