Family Physicians’ Feedback on the Feature Design of a Digital Health Platform to Streamline the Care of Older Adults
Bibliographic record
Abstract
Background/Objectives: Family physicians are essential to a well-functioning healthcare system; however, they face significant administrative and cognitive burdens that contribute to their burnout and reduce the quality of patient care they provide. Digital health tools offer potential solutions to these problems. This study examined the interface design and features of a digital health platform, Carmi, designed to mitigate administrative inefficiencies and cognitive overload by asynchronous patient data gathering and automated report generation. Methods: We conducted semi-structured interviews with nine family physicians practicing in Alberta, Canada, to gather their feedback on Carmi’s interface design and features. Participants were asked to view a 20 min virtual demonstration of Carmi and provide input on its interface, navigation, potential impact on their clinic workflow, and suggestions for additional features. Interviews were transcribed and thematically analyzed using NVivo. Results: Participants found Carmi’s interface user-friendly; most agreed that Carmi could reduce cognitive burden by automatically generating summary reports of assessments completed by patients and facilitating care coordination. Participants thought integration within existing electronic medical records was important, albeit Care of the Elderly physicians saw the value of Carmi as a standalone platform, noting that it can become a collaborative space where all healthcare providers can contribute to patient care. Conclusions: Carmi has the potential to improve primary care efficiency, especially for older adults with complex health needs. Work is underway at several pilot sites that have implemented Carmi so far to gather physicians, patients, and their caregivers’ feedback on its usability.
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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 | 0.012 | 0.044 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".