Nurse Prompting for Prescriber-Led Review of Antimicrobial Use in the Critical Care Unit
Bibliographic record
Abstract
BACKGROUND: Developing a sustainable strategy for prescriber-led review of antimicrobial use in a critical care unit may improve antimicrobial use without the need for additional resources. METHODS: Using a quality improvement framework, the researchers created a prompt for prescriber-led review of antimicrobial use. The outcome measure was antimicrobial use (days of therapy per 1000 patient days). The process measure was the proportion of relevant cases for which an antimicrobial prompt was provided. Balancing measures included mortality rate, length of stay, 48-hour readmission rates, and multiple organ dysfunction score. Interrupted time series with segmented regression analysis was used for the outcome measure. RESULTS: Process analysis identified critical care unit nurses for antimicrobial use prompting. A standard script was developed to incorporate a days of therapy prompt into nurse rounds, with primed prescriber responses. Before the intervention, monthly antimicrobial use was 804 days of therapy per 1000 patient days, with a positive trend (7.3 days of therapy per 1000 patient days, P < .05). After the intervention, there was an immediate reduction of 217 days of therapy per 1000 patient days (P < .05), with a nonsignificant negative trend, representing a 20% (95% CI, -15% to -25%) reduction. No significant change was noted in use of the control class of medications. The proportion of relevant cases for which an antimicrobial prompt was provided increased from 21% to 48% during the intervention period. Balancing measures were comparable before and after the intervention. CONCLUSIONS: Nurse prompting can lead to significant reductions in antimicrobial use, providing a sustainable mechanism for independent antimicrobial reassessment.
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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 | 0.023 | 0.070 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".