Effect of Computerized Provider Order Entry with Clinical Decision Support on Adverse Drug Events in the Long‐Term Care Setting
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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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 | 0.006 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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) | 0.004 | 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".