Efficacy of telemedicine-based antimicrobial stewardship program to combat antimicrobial resistance: A systematic review and meta-analysis
Bibliographic record
Abstract
IntroductionAntimicrobial resistance (AMR) is a major public health threat. Improving antimicrobial use is the main strategy against AMR, but it is challenging to implement especially in low-resource settings. Thus, this review aims to explore the efficacy of telehealth-based antimicrobial stewardship programs (ASP), which is more accessible.MethodsRegistered to PROSPERO and following PRISMA guidelines, literature search was performed in databases including PubMed, Scopus, Cochrane, Science Direct, EBSCOhost, EMBASE, and Google Scholar, searching for studies implementing telehealth ASP. Critical appraisal of studies was performed using Newcastle-Ottawa Scale for Cohort Studies (NOS), Cochrane Risk-of-Bias tool (RoB) 2.0, and Risk Of Bias In Non-randomised Studies-of Interventions (ROBINS-I). We utilized inverse variance, random effects model to obtain the pooled odds ratio (OR) and mean difference (MD) estimates, as well as sensitivity and subgroup analysis.Results and DiscussionThe search yielded 14 studies. Telehealth-based ASP was associated with better adherence to guidelines (pooled OR: 2.78 [95%CI:1.29-5.99], p = 0.009; I2 = 93%), within which streamlining yielded better odds (pooled OR: 30.54 [95%CI:10.42-89.52], p < 0.001) more than the compliance with policy subgroup (pooled OR: 1.60 [95%CI:1.02-2.51], p = 0.04). The odds of antimicrobial prescription rate reduced significantly (pooled OR: 0.60 [95%CI:0.42-0.85], p = 0.005; I2 = 94%), especially for the lower respiratory infection subgroup (pooled OR: 0.37 [95%CI:0.28-0.49], p < 0.001). Days of therapy decreased (pooled MD: -47.12 [95%CI: -85.78- -8.46], p = 0.02; I2 = 100%), with the greatest effect in acute care settings (pooled MD: -97.73 [95%CI:-147.48-47.97], p = 0.0001). Mortality did not change significantly (pooled OR: 1.20 [95%CI:0.69-2.10], p = 0.52; I2 = 63%).ConclusionTelehealth-based ASP was proven beneficial to increase adherence to guideline and reduce prescription rates, without significantly affecting patient clinical outcome. After further studies, we recommend more widespread use of telemedicine to combat AMR.
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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 | 0.014 | 0.030 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.020 | 0.041 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| 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".