112 Dual use of prescription insurance: drivers and challenges
Bibliographic record
Abstract
Introduction Nearly 30% of Veterans with diabetes fill their medications through both VA and Medicare prescription drug insurance and have complicated choices about where to fill medications.1 The study objective was to understand drivers and challenges of filling through both systems and identify informational needs. Methods We conducted semi-structured phone interviews with Veterans (n=24) and care partners (n=12), (e.g., family member, caregiver, or another involved in decisions about filling the Veteran’s prescriptions). The interview guide was developed based on the Ottawa Decision Support Framework.2 Interview transcripts were generated for thematic analysis. Results Veteran interviewees were older than care partner interviewees (respective mean ages 77 and 69). Most Veterans were male (88%) and most care partners were female (92%). The main drivers for filling diabetes medications through two systems were cost and formulary availability; additional factors included distance and trust in providers and healthcare systems. Participants reported challenges such as high and variable copays, inability to switch all medications from one system to the other, and inconsistent care coordination between VA and Medicare providers. Participants shared a preference to receive medications through one system and to be able to directly access information on expected medication costs and formulary availability through VA versus Medicare, as well as discuss this information with clinicians. Discussion Despite a preference to use a single system to fill all medications, cost and formulary availability were the primary reasons for choosing to fill through both systems. Logistics, communication challenges, and information gaps impact dual use; Veterans and care partners may benefit from targeted information that allows them to make choices aligned with their values. Conclusion Veterans and care partners have information gaps that lead to values-incongruent decisions on where Veterans fill their medications. Our next steps include creating a decision aid to help address this problem. References Taber DJ, Ward R, Axon RN, Walker RJ, Egede LE, Gebregziabher M. Ann Pharmacother. 2019;53(7):675–682. Hoefel L, O’Connor A, Lewis KB, Boland L, Sikora L, Hu J, Stacey D. Medical Decision Making. 2020;40(5):555–81.
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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 | 0.004 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".