Effect of Computerized Provider Order Entry with Clinical Decision Support on Adverse Drug Events in the Long‐Term Care Setting
Why this work is in the frame
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Bibliographic record
Abstract
OBJECTIVES: To evaluate the efficacy of computerized provider order entry with clinical decision support for preventing adverse drug events in long-term care. DESIGN: Cluster-randomized controlled trial. SETTING: Two large long-term care facilities. PATIENTS: One thousand one hundred eighteen long-term care residents of 29 resident care units. INTERVENTION: The 29 resident care units, each with computerized provider order entry, were randomized to having a clinical decision support system (intervention units) or not (control units). MEASUREMENTS: The number of adverse drug events, severity of events, and whether the events were preventable. RESULTS: Within intervention units, 411 adverse drug events occurred over 3,803 resident-months of observation time; 152 (37.0%) were deemed preventable. Within control units, there were 340 adverse drug events over 3,257 resident-months of observation time; 126 (37.1%) were characterized as preventable. There were 10.8 adverse drug events per 100 resident-months and 4.0 preventable events per 100 resident-months on intervention units. There were 10.4 adverse drug events per 100 resident-months and 3.9 preventable events per 100 resident-months on control units. Comparing intervention and control units, the adjusted rate ratios were 1.06 (95% confidence interval (CI)=0.92-1.23) for all adverse drug events and 1.02 (95% CI=0.81-1.30) for preventable adverse drug events. CONCLUSION: Computerized provider order entry with decision support did not reduce the adverse drug event rate or preventable adverse drug event rate in the long-term care setting. Alert burden, limited scope of the alerts, and a need to more fully integrate clinical and laboratory information may have affected efficacy.
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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 | 0.008 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| 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 it