A Longitudinal Four-year Pre-Post Intervention Study Evaluating the Use of an Antimicrobial Stewardship Application
Bibliographic record
Abstract
Clinical decision support via the electronic health record (EHR) is a vital component of many antimicrobial stewardship initiatives. However, many developed and developing countries lack the necessary infrastructure to perform such tasks. We hypothesized that an antimicrobial stewardship application (app) targeted towards empiric antimicrobial therapy, along with local clinical guidelines/pathways would decrease utilization of our two most commonly over-used antibiotics, piperacillin-tazobactam (PTZ) and vancomycin (VAN). A four-year pre-post study was performed following the implementation of our antimicrobial stewardship web-based app. The app was introduced to those on medical services/wards and not to users on surgical services/wards. Comparative utilization of PTZ and VAN were subsequently measured among medical and surgical wards in the form of days of therapy (DOT) per 1,000 patient-days. Following the intervention period, annual VAN utilization among medical wards decreased from 171 DOT per 1,000 patient-days to 139 DOT per 1,000 patient-days, while increasing from 106 DOT per 1,000 patient-days to 126 DOT per 1,000 patient-days among surgical wards (Figure 1). Similarly, PTZ utilization decreased from 141 DOT per 1,000 patient-days to 80 DOT per 1,000 patient-days among medical wards, while increasing from 128 DOT per 1,000 patient-days to 132 DOT per 1,000 patient days among surgical wards (Figure 2). App analytics demonstrated an average user time of 2.68 minutes per session with the respiratory and urinary tract sections being the most commonly visited. In settings where EHR clinical decision support is not available, an antimicrobial stewardship app targeting empiric therapy can be used successfully to decrease utilization of inappropriate antibiotics. Future work will look at incorporating more comprehensive guidelines to help with clinical decision-making. Vancomycin utilization (days of therapy per 1,000 patient days). Piperacillin–tazobactam utilization (days of therapy per 1,000 patient days). All authors: No reported disclosures.
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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 | 0.005 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".