Exploring the Value of Real-Time Medication Adherence Monitoring: A Qualitative Study
Bibliographic record
Abstract
Smart adherence products enable the monitoring of medication intake in real-time. However, the value of real-time medication intake monitoring to different stakeholders such as patients, their caregivers, clinicians, and insurers is not elucidated. The aim of this study was to explore the value different stakeholders place on the availability of smart adherence products and access to real-time medication intake data. A qualitative study design using semi-structured one-on-one virtual interviews was utilized. Schwartz's theory of values provided the foundation for the interview questions, data were analyzed using Braun and Clark's thematic analysis framework, and findings were mapped back to the constructs of Schwartz's theory of values. A total of 31 interviews with patients, caregivers, healthcare providers, and representatives of private or public insurance providers were conducted. Three themes and ten subthemes were identified. Themes included perceptions of integrating smart medication adherence technologies and real-time monitoring, technology adoption factors and data management. Stakeholders place different values based on the motivators and goals that can drive product use for daily medication management. Stakeholders valued the availability of real-time medication taking data that allow clinicians to make timely data-driven recommendations to their patients that may improve medication management for patients and reduce the caregiver burden.
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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 | 0.020 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.008 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".