Incidence of clinically relevant medication errors in the era of electronically prepopulated medication reconciliation forms: a retrospective chart review
Bibliographic record
Abstract
BACKGROUND: To reduce medication discrepancies (unintended differences between a patient's outpatient and inpatient medication regimens), Canadian institutions have implemented medication reconciliation forms that are prepopulated with outpatient medication dispensing data. These may prompt prescribers to reorder discontinued medications or continue newly contraindicated medications. Our objective was to evaluate the incidence of medication discrepancies and errors of commission after the implementation of such forms. METHODS: This retrospective chart review included patients previously enrolled in an observational study in which a research pharmacist prospectively collected best-possible medication histories in the emergency department. Research assistants uninvolved with the parent study compared medication orders written in the first 48 hours after admission with the research pharmacist's best-possible medication history to identify medication discrepancies and errors of commission, defined as inappropriate medication continuations and reordering of previously stopped medications. An independent panel adjudicated the clinical significance of the errors. RESULTS: Of 151 patients, 71 (47.0% [95% confidence interval (CI) 39.2-54.9]) were exposed to 112 medication errors on admission. Of the 112 errors, 24 (21.4% [95% CI 14.9-29.9]) were clinically significant. Errors of commission accounted for 24.1% (27/112 [95% CI 17.3-32.8]) of all errors; 10 (37.0% [95% CI 18.8-55.2]) of the errors of commission were clinically significant. INTERPRETATION: Medication errors were common after the implementation of electronically prepopulated medication reconciliation forms. Prospective research is required to examine the impact of prepopulated medication reconciliation forms and ensure they do not facilitate errors of commission.
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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 | 0.004 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| 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".