Development and evaluation of a patient-centric approach for accurate medication capture
Bibliographic record
Abstract
Objective: To develop and evaluate a patient-centric medication module within a personal health record (PHR) app for capturing medication use, focusing on accuracy, usability, and concordance. Materials and Methods: The medication module offered 4 entry methods: picklist, National Drug Code (NDC), free-text, and portal import, with the first 2 leveraging RxNorm and openFDA APIs. Patients from an integrated delivery network (IDN) created medication lists and recorded daily use in the app's diary. Pharmacists evaluated medication accuracy by reviewing patient-uploaded medication images. Usability was measured using the System Usability Scale (SUS). Concordance was assessed by comparing Electronic Health Records (EHR) with diary entries. Results: Over a 14-day period, 85 patients entered 617 medications, with 533 logged in the diary representing current use. Picklist was the most used entry method. Overall medication entry accuracy was 92% (picklist 97%; NDC 87%; free-text 84%; and portal import 100%). The mean system usability score was 56.5 for the study app (patients) and 80.8 for the medication module (pharmacists). EHR concordance with diary entries was low (25% using the 14-day window; 53% using a 1-year window); most unmatched entries were over-the-counter (OTC) medications. Discussion: Accurate and complete medication records are essential for the safe and effective use of medications. This patient-centric medication module supported accurate capture of prescription and OTC medications. Gaps in EHR data highlight the need to improve medication record accuracy and reconciliation. Conclusion: Patient-generated health data can have a central role in creating the "Best Possible Medication History" envisioned by the World Health Organization.
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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 | 0.024 | 0.036 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".