Concordance between discharge prescriptions and insurance claims in post‐myocardial infarction patients
Bibliographic record
Abstract
PURPOSE: To assess the degree of concordance between the information (drug quantity, days' supply, and daily dose) recorded on hospital discharge prescriptions and what appears in a public drug insurance electronic claims database. METHODS: A retrospective chart audit of hospital discharge prescriptions with linkage to a prescription claims database was conducted. Three hundred and forty-five post-myocardial infarction patients discharged from an Ontario university-affiliated teaching hospital were included. The percentage of linkable records with perfect agreement between the written prescription and the insurance claim was our measure of concordance. RESULTS: Seventy-seven per cent and 82% of discharge prescriptions were filled within 7 days, and 120 days post-discharge, respectively. Of those dispensed and that contained adequate information, concordance was perfect for days' supply, quantity, and daily dose for 70.7% (95%CI 67.9-73.4%), 65.9% (95%CI 63.2-68.7%), and 75.9% (95%CI 73.2-78.6%) of prescriptions, respectively. For cardiac drugs, which comprised the majority of filled prescriptions, concordance was greater for daily dose and days' supply than for quantity (75.7% [95%CI 72.7-78.6%] and 75.5% [95%CI 72.6-78.4%] vs. 65.3% [95%CI 62.3-68.4%]). Concordance varied by medication type. CONCLUSION: Most hospital discharge prescriptions were filled within 1 week. Among the data elements studied, concordance between written prescriptions and insurance claims was greatest for daily dose. Concordance was greater for scheduled cardiac medications than for other medications.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".