Design of a Dyadic Digital Health Module for Chronic Disease Shared Care: Development Study
Bibliographic record
Abstract
BACKGROUND: The COVID-19 pandemic forced the spread of digital health tools to address limited clinical resources for chronic health management. It also illuminated a population of older patients requiring an informal caregiver (IC) to access this care due to accessibility, technological literacy, or English proficiency concerns. For patients with heart failure (HF), this rapid transition exacerbated the demand on ICs and pushed Canadians toward a dyadic care model where patients and ICs comanage care. Our previous work identified an opportunity to improve this dyadic HF experience through a shared model of dyadic digital health. We call this alternative model of care "Caretown for Medly," which empowers ICs to concurrently expand patients' self-care abilities while acknowledging ICs' eagerness to provide greater support. OBJECTIVE: We present the systematic design and development of the Caretown for Medly dyadic management module. While HF is the outlined use case, we outline our design methodology and report on 6 core disease-invariant features applied to dyadic shared care for HF management. This work lays the foundation for future usability assessments of Caretown for Medly. METHODS: We conducted a qualitative, human-centered design study based on 25 semistructured interviews with self-identified ICs of loved ones living with HF. Interviews underwent thematic content analysis by 2 coders independently for themes derived deductively (eg, based on the interview guide) and inductively refined. To build the Caretown for Medly model, we (1) leveraged the Knowledge to Action (KTA) framework to translate knowledge into action and (2) borrowed Google Sprint's ability to quickly "solve big problems and test new ideas," which has been effective in the medical and digital health spaces. Specifically, we blended these 2 concepts into a new framework called the "KTA Sprint." RESULTS: We identified 6 core disease-invariant features to support ICs in care dyads to provide more effective care while capitalizing on dyadic care's synergistic benefits. Features were designed for customizability to suit the patient's condition, informed by stakeholder analysis, corroborated with literature, and vetted through user needs assessments. These features include (1) live reports to enhance data sharing and facilitate appropriate IC support, (2) care cards to enhance guidance on the caregiving role, (3) direct messaging to dissolve the disconnect across the circle of care, (4) medication wallet to improve guidance on managing complex medication regimens, (5) medical events timeline to improve and consolidate management and organization, and (6) caregiver resources to provide disease-specific education and support their self-care. CONCLUSIONS: These disease-invariant features were designed to address ICs' needs in supporting their care partner. We anticipate that the implementation of these features will empower a shared model of care for chronic disease management through digital health and will improve outcomes for care dyads.
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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 | 0.026 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".