Medication Reconciliation at Hospital Discharge: Evaluating Discrepancies
Bibliographic record
Abstract
BACKGROUND: Hospital discharge is an interface of care when patients are at a high risk of medication discrepancies as they transition from hospital to home. These discrepancies are important, as they may contribute to drug-related problems, medication errors, and adverse drug events. OBJECTIVE: To identify, characterize, and assess the clinical impact of unintentional medication discrepancies at hospital discharge. METHODS: All consecutive general internal medicine patients admitted for at least 72 hours to a tertiary care teaching hospital were prospectively assessed. Patients were excluded if they were discharged with verbal prescriptions; died during hospitalization; or transferred from or to a nursing home, another institution, or another unit within the same hospital. The primary endpoint was to determine the number of patients with at least one unintended medication discrepancy on hospital discharge. Medication discrepancies were assessed through comparison of a best possible medication discharge list with the actual discharge prescriptions. Secondary objectives were to characterize and assess the potential clinical impact of the unintentional discrepancies. RESULTS: From March 14, 2006, to June 2, 2006, 430 patients were screened for eligibility; 150 patients were included in the study. Overall, 106 (70.7%) patients had at least one actual or potential unintentional discrepancy. Sixty-two patients (41.3%) had at least one actual unintentional medication discrepancy at hospital discharge and 83 patients (55.3%) had at least one potential unintentional discrepancy. The most common unintentional discrepancies were an incomplete prescription requiring clarification, which could result in a patient delay in obtaining medications (49.5%), and the omission of medications (22.9%). Of the 105 unintentional discrepancies, 31(29.5%) had the potential to cause possible or probable patient discomfort and/or clinical deterioration. CONCLUSIONS: Medication discrepancies occur commonly on hospital discharge. Understanding the type and frequency of discrepancies can help clinicians better understand ways to prevent them. Structured medication reconciliation may help to prevent discharge medication discrepancies.
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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 | 0.020 | 0.088 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 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".