Estimating the information gap between emergency department records of community medication compared to on-line access to the community-based pharmacy records
Bibliographic record
Abstract
OBJECTIVE: Errors in community medication histories increase the risk of adverse events. The objectives of this study were to estimate the extent to which access to community-based pharmacy records provided more information about prescription drug use than conventional medication histories. MATERIALS AND METHODS: A prospective cohort of patients with public drug insurance who visited the emergency departments (ED) in two teaching hospitals in Montreal, Quebec was recruited. Drug lists recorded in the patients' ED charts were compared with pharmacy records of dispensed medications retrieved from the public drug insurer. Patient and drug-related predictors of discrepancies were estimated using general estimating equation multivariate logistic regression. RESULTS: 613 patients participated in the study (mean age 63.1 years, 59.2% women). Pharmacy records identified 41.5% more prescribed medications than were noted in the ED chart. Concordance was highest for anticoagulants, cardiovascular drugs and diuretics. Omissions in the ED chart were more common for drugs that may be taken episodically. Patients with more than 12 medications (OR 2.92, 95% CI 1.71 to 4.97) and more than one pharmacy (OR 3.85, 95% CI 1.80 to 6.59) were more likely to have omissions in the ED chart. DISCUSSION: The development of health information exchanges could improve the efficiency and accuracy of information about community medication histories if they enable automated access to dispensed medication records from community pharmacies, particularly for the most vulnerable populations with multiple morbidities. CONCLUSIONS: Pharmacy records identified a substantial number of medications that were not in the ED chart. There is potential for greater safety and efficiency with automated access to pharmacy records.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".