Development of a Web-Based Tool Built From Pharmacy Claims Data to Assess Adherence to Respiratory Medications in Primary Care
Bibliographic record
Abstract
BACKGROUND: Medication adherence in asthma and COPD is notoriously low. To intervene effectively, family physicians need to assess adherence accurately, which is a challenging endeavor. In collaboration family physicians and individuals with asthma or COPD, we aimed to explore the barriers and facilitators of assessing medication adherence in clinical practice (exploratory phase), and to develop a novel web-based tool (e-MEDRESP) that will allow physicians to monitor adherence using pharmacy claims data (development phase). METHODS: = 20), and 10 individual interviews were conducted with physicians. In the exploratory phase, data were analyzed using thematic networks. In the development phase, we identified components to be included in an e-MEDRESP prototype through an iterative approach. The web-based e-MEDRESP tool was constructed by applying algorithms to pharmacy claims data that reflected end-users' recommendations through an informatics approach designed for electronic medical records. RESULTS: The main barriers to assessing medication adherence included a lack of objective information regarding medication use and short duration of medical visits. Physicians emphasized that identifying patients at risk for nonadherence requires a team effort from pharmacists, respiratory therapists, and nurses. Subjects also agreed that the use of easily interpretable pharmacy claims data could be an important facilitator and contributed to the development of the e-MEDRESP prototype, which contains graphical representations of the adherence to respiratory controller medications and dispensing of rescue medications. CONCLUSIONS: The e-MEDRESP tool has the potential to allow physicians to measure adherence objectively and to facilitate patient-physician communication concerning medication use. Future studies are needed to evaluate the feasibility of implementing e-MEDRESP in clinical practice. It would be relevant to develop strategies that could facilitate the sharing of information presented in e-MEDRESP among primary care health professionals.
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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 | 0.010 | 0.038 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".