Assessing accuracy of an electronic provincial medication repository
Bibliographic record
Abstract
BACKGROUND: Jurisdictional drug information systems are being implemented in many regions around the world. British Columbia, Canada has had a provincial medication dispensing record, PharmaNet, system since 1995. Little is known about how accurately PharmaNet reflects actual medication usage. METHODS: This prospective, multi-centre study compared pharmacist collected Best Possible Medication Histories (BPMH) to PharmaNet profiles to assess accuracy of the PharmaNet profiles for patients receiving a BPMH as part of clinical care. A review panel examined the anonymized BPMHs and discrepancies to estimate clinical significance of discrepancies. RESULTS: 16% of medication profiles were accurate, with 48% of the discrepant profiles considered potentially clinically significant by the clinical review panel. Cardiac medications tended to be more accurate (e.g. ramipril was accurate >90% of the time), while insulin, warfarin, salbutamol and pain relief medications were often inaccurate (80-85% of the time). 1215 sequential BPMHs were collected and reviewed for this study. CONCLUSIONS: The PharmaNet medication repository has a low accuracy and should be used in conjunction with other sources for medication histories for clinical or research purposes. This finding is consistent with other, smaller medication repository accuracy studies in other jurisdictions. Our study highlights specific medications that tend to be lower in accuracy.
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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 | 0.041 | 0.253 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".