Unintended Medication Discrepancies at the Time of Hospital Admission
Bibliographic record
Abstract
BACKGROUND: Prior studies suggest that unintended medication discrepancies that represent errors are common at the time of hospital admission. These errors are particularly worthy of attention because they are not likely to be detected by computerized physician order entry systems. METHODS: We prospectively studied patients reporting the use of at least 4 regular prescription medications who were admitted to general internal medicine clinical teaching units. The primary outcome was unintended discrepancies (errors) between the physicians' admission medication orders and a comprehensive medication history obtained through interview. We also evaluated the potential seriousness of these discrepancies. All discrepancies were reviewed with the medical team to determine if they were intentional or unintentional. All unintended discrepancies were rated for their potential to cause patient harm. RESULTS: After screening 523 admissions, 151 patients were enrolled based on the inclusion criteria. Eighty-one patients (53.6%; 95% confidence interval, 45.7%-61.6%) had at least 1 unintended discrepancy. The most common error (46.4%) was omission of a regularly used medication. Most (61.4%) of the discrepancies were judged to have no potential to cause serious harm. However, 38.6% of the discrepancies had the potential to cause moderate to severe discomfort or clinical deterioration. CONCLUSIONS: Medication errors at the time of hospital admission are common, and some have the potential to cause harm. Better methods of ensuring an accurate medication history at the time of hospital admission are needed.
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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 | 0.031 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".