Telemedicine interventions for improving antibiotic stewardship and prescribing: A systematic review
Bibliographic record
Abstract
The global antibiotic resistance crisis necessitates optimized stewardship programs, with telemedicine emerging as a promising delivery strategy. This systematic review evaluated the effectiveness of telemedicine interventions in improving antibiotic stewardship across clinical settings. Following Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, we systematically searched seven databases from January 2010 to July 2024. Two independent reviewers assessed studies using Risk of Bias in Non-randomized Studies (ROBINS-I) and Cochrane Risk of Bias 2.0 tools, with evidence certainty evaluated using Grading of Recommendations Assessment, Development, and Evaluation (GRADE). Twenty-one studies met inclusion criteria (10 observational, 8 quasi-experimental, 2 Randomized Controlled Trials [RCTs], 1 mixed-methods), predominantly from the United States (57.0%, n = 12). Among studies reporting antibiotic use outcomes (52.4%, n = 11), 90.9% demonstrated significant reductions ranging from 5.3% to 62.7%, with the highest-quality evidence showing a 28% reduction (95% Confidence Interval [CI]: 22-34%). Guideline adherence studies (57.1%, n = 12) showed acceptance rates of 67.7% to 98%, with comparable effectiveness between telemedicine and in-person consultation (79.1% vs 80.4%, p = 0.36). Prescribing rate outcomes (38.1%, n = 8) revealed setting-dependent variations: inpatient implementations demonstrated significant reductions (Relative Risk [RR] 0.68; 95% CI: 0.63-0.75), while outpatient findings showed mixed results. Quality assessment revealed predominantly low risk of bias [ROB] (66.7%, n = 14). These findings suggest that telemedicine effectively improves antibiotic stewardship compared to traditional care models, particularly in hospital settings, while outpatient applications demonstrated variable effectiveness. This review was registered with the International Prospective Register of Systematic Reviews (PROSPERO: CRD42023454663).
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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 | 0.011 | 0.041 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.011 |
| Bibliometrics | 0.008 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".