Reconcilable differences: correcting medication errors at hospital admission and discharge
Bibliographic record
Abstract
BACKGROUND: Medication errors at the time of hospital admission and discharge are common and can lead to preventable adverse drug events. The objective of this study was to describe the potential impact of a medication reconciliation process to identify and rectify medication errors at the time of hospital admission and discharge. METHODS: Sixty randomly selected patients were prospectively enrolled at the time of admission to a Canadian community hospital. At admission, patients' medication orders were compared with pre-admission medication use based on medication vials and interviews with patients, caregivers, and/or outpatient healthcare providers. At discharge, pre-admission and in-patient medications were compared with discharge orders and written instructions. All variances were discussed with the prescribing physician and classified as intended or unintended; unintended variances were considered to be medication errors. An internist classified the clinical importance of each unintended variance. RESULTS: Overall, 60% (95% CI 48 to 72) of patients had at least one unintended variance and 18% (95% CI 9 to 28) had at least one clinically important unintended variance. None of the variances had been detected by usual clinical practice before reconciliation was conducted. Of the 20 clinically important variances, 75% (95% CI 56 to 94) were intercepted by medication reconciliation before patients were harmed. DISCUSSION: Unintended medication variances at the time of hospital admission and discharge are common and clinically important. The medication reconciliation process identified and addressed most of these unintended variances before harm occurred. In this small study, medication reconciliation was a useful method for identifying and rectifying medication errors at times of transition. Reconciliation warrants broader evaluation.
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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 | 0.015 | 0.094 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".