The effect of digital antimicrobial stewardship programmes on antimicrobial usage, length of stay, mortality and cost
Bibliographic record
Abstract
Antimicrobial stewardship aims to slow the emergence of antimicrobial resistance. We conducted a systematic review on the use of digital antimicrobial stewardship programmes (ASP) on antimicrobial usage, cost, length of stay (LoS) and mortality. We identified, quality appraised using the Newcastle-Ottawa scale, and descriptively and narratively synthesised data on primary research articles that implemented an ASP(s) for adult inpatients for at least six months, and reported antimicrobial usage as defined daily dose (DDD) per 1000 patient days and at least one of: LoS, mortality or cost. Our review was registered with PROSPERO: CRD42020154124 and adhered to the PRISMA guideline. Our searches retrieved 3,997 titles, from which 13 studies were included. The risk of bias assessment resulted in 10 studies receiving a rating of 7 stars or over. A range of ASPs were implemented using computerised decision support (CDS) systems, including those that combined audit and feedback, guidelines and approval, computerised approval processing, computerised recommendations and surveillance. All studies found a decrease in antimicrobial usage (DDD range, −8.42% to −61.29%). All six studies that considered costs also showed a decrease (range, −8.12% to −69.19%). Six studies reported a decrease in mortality and one showed no change. The digital ASP programmes investigated appeared to have a positive impact on antimicrobial usage and clinical outcomes. Our review found that ASPs that utilised an audit and feedback approach showed a promising and consistent reduction in DDD.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.019 | 0.098 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.008 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".