Reducing co-administration of proton pump inhibitors and antibiotics using a computerized order entry alert and prospective audit and feedback
Bibliographic record
Abstract
BACKGROUND: Antibiotics and proton pump inhibitors (PPIs) are associated with Clostridium difficile infection (CDI). Both a computer order entry alert to highlight this association as well as antimicrobial stewardship directed prospective audit and feedback represent novel interventions to reduce the co-administration of antibiotics and PPIs among hospitalized patients. METHODS: Consecutive patients admitted to two General Internal Medicine wards from October 1, 2010 until March 31, 2013 at a teaching hospital in Toronto, Ontario, Canada were evaluated. The baseline observation period was followed by the first phase, which involved the creation of a computerized order entry alert that was triggered when either a PPI or an antibiotic was ordered in the presence of the other. The second phase consisted of the introduction of an antibiotic stewardship-initiated prospective audit and feedback strategy. The primary outcome was the co-administration of antibiotics and PPIs during each phase. RESULTS: This alert led to a significant reduction in the co-administration of antibiotics and PPIs adjusted for month and secular trends, expressed as days of therapy per 100 patient days (4.99 vs. 3.14, p < 0.001) The subsequent introduction of the antibiotic stewardship program further reduced the co-administration (3.14 vs. 1.80, p <0.001). No change was observed in adjusted monthly CDI rates per 100 patient care days between the baseline and alert cohorts (0.12 vs. 0.12, p = 0.99) or the baseline and antibiotic stewardship phases (0.12 vs. 0.13, p = 0.97). CONCLUSIONS: Decreasing the co-administration of PPIs and antibiotics can be achieved using a simple automatic alert followed by prospective audit and feedback.
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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 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".