Reduction of inappropriate exit prescriptions for proton pump inhibitors: A before‐after study using education paired with a web‐based quality‐improvement tool
Bibliographic record
Abstract
BACKGROUND: Proton pump inhibitors (PPIs) are overprescribed despite concerns regarding associated adverse drug events. OBJECTIVE: To reduce inappropriate PPI prescriptions using hospitalization as the point of contact to effect meaningful change. DESIGN: Before-after study design. SETTING: Forty-six-bed medical clinical teaching unit in a 417-bed university teaching hospital in Montreal, Canada. PATIENTS: Four hundred sixty-four consecutively admitted patients in the preintervention control group, and 640 consecutively admitted patients in the intervention group. INTERVENTION: A monthly educational intervention paired with a Web-based quality improvement tool. MEASUREMENTS: We determined the proportion of patients admitted on PPIs, their indications, and appropriateness of use. We then compared the proportion of patients whose PPIs were discontinued at discharge before and after our intervention. RESULTS: Forty-four percent of patients were already using a PPI prior to their hospitalization. In evaluated patients, only 54% of these patients had an evidence-based indication for ongoing use. The proportion of PPIs discontinued at hospital discharge increased from 7.7% per month in the 6 months prior to intervention, to 18.5% per month postintervention (P = 0.03). CONCLUSIONS: Strategies to combat PPI overuse are needed to improve the overall quality of patient care. We significantly reduced discharge prescriptions for PPIs through the implementation of an educational initiative paired with a Web-based quality improvement tool. An active interventional strategy is likely required considering the increasingly recognized and preventable adverse events associated with PPI misuse.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".