Sustained impact of a computer-assisted antimicrobial stewardship intervention on antimicrobial use and length of stay
Bibliographic record
Abstract
Objectives: : Prospective audit and feedback interventions are the core components of an antimicrobial stewardship programme. Herein, we describe the sustained impact of an antimicrobial stewardship programme, based on a novel clinical decision-support system (Antimicrobial Prescription Surveillance System; APSS), on antimicrobial use and costs, hospital length of stay (LOS) in days and the proportion of inappropriate antimicrobial prescriptions. Methods: A quasi-experimental, retrospective study was conducted using interrupted time series between 2008 and 2013. Data on all hospitalized adults receiving antimicrobials were extracted from the data warehouse of a 677 bed academic centre. The intervention started in August 2010. Prospective audit and feedback interventions, led by a pharmacist, were triggered by APSS based on deviations from published and local guidelines. Changes in outcomes before and after the intervention were compared using segmented regression analysis. Results: APSS reviewed 40 605 hospitalizations for 35 778 patients who received antimicrobials. The intervention was associated with a decrease in the average LOS (level change -0.92, P < 0.01; trend -0.08, P < 0.01; intercept 11.4 days), antimicrobial consumption in DDDs/1000 inpatient days (level change -32.4, P < 0.01; trend -1.12, P < 0.02; intercept 243 DDDs per 1000 days of hospitalization), antimicrobial spending in Canadian dollars (level change -19 649, P = 0.01; trend -1881, P < 0.01; intercept $74 683) and proportion of non-concordance with local guidelines for prescribing antimicrobials (level change -2.3, P = 0.04; intercept 41%). Conclusions: The implementation of the APSS-initiated strategy was associated with a positive impact on antimicrobial use and spending, LOS and inappropriate prescriptions. The high rate of accepted interventions may have contributed to these results.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".