Feasibility of Implementing a Web-based Tool Built from Pharmacy Claims Data (e-MEDRESP) to Monitor Adherence to Respiratory Medications in Primary Care
Bibliographic record
Abstract
Objectives: e-MEDRESP is a novel web-based tool that provides easily interpretable information on patient adherence to asthma/chronic obstructive pulmonary disease medications, using pharmacy claims data. This study investigated the feasibility of implementing e-MEDRESP in primary care. Materials and Methods: In this 16-month prospective cohort study, e-MEDRESP was integrated into electronic medical records. Nineteen family physicians and 346 of their patients were enrolled. Counters embedded in the tool tracked physician use during the follow-up. Patient/physician satisfaction with e-MEDRESP was evaluated though telephone interviews and online questionnaires. The capacity of e-MEDRESP to improve adherence was explored using a pre–post analysis. Results: Overall, 252 patients had at least one medical visit during follow-up. e-MEDRESP was consulted by 15 (79%) physicians for 85 (34%) patients during clinic visits. Seventy-three patients participated in telephone interviews; 84% reported discussing their medication use with their physician; 33% viewed their e-MEDRESP report and indicated that it was easy to interpret. The physicians reported that the tool facilitated their evaluation of their patients’ medication adherence (mean ± standard deviation rating: 4.8 ± 0.7, on a 5-point Likert scale). Although the pre–post analysis did not reveal improved adherence in the overall cohort, adherence improved significantly in patients whose adherence level was <80% and patients prescribed inhaled corticosteroids (26.9% [95% CI 14.3%–39.3%]) or long-acting muscarinic agents (26.4% [95% CI 12.4%–40.2%]). Conclusions: e-MEDRESP was successfully integrated in clinical practice. It could serve as a powerful tool to help physicians monitor their patients’ medication adherence.
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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 | 0.016 | 0.039 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| 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".