The value of technology to support caregiving for individuals living with heart failure (Preprint)
Bibliographic record
Abstract
BACKGROUND The demand for health services to meet the chronic health needs of our aging population is significant and remains unmet due to a limited supply of clinical resources. Specifically in managing heart failure (HF), virtual care sought to address this gap during COVID-19, but highlighted an access issue for those who could not use technology-mediated healthcare services without the support of their informal caregivers (ICs). Because of the complexity of managing HF symptoms and recurrent exacerbations, many patients co-manage their illness with their ICs in a care dyad, working together to optimize the patient’s outcomes and health-related quality of life. However, most HF programs have missed the opportunity to consider the dyadic perspective despite dyadic behaviour and well-being interdependencies. OBJECTIVE This research sought to characterize the value of technology in supporting caregiving for individuals living with heart failure. METHODS Motivated by an observed unique pattern of engagement in patients enrolled in our Medly HF management program at the Peter Munk Cardiac Centre in Toronto, Canada, we conducted 20 semi-structured interviews with a diverse convenience sample of informal caregivers. All interviews were analyzed using the iterative refinement of a co-developed codebook.. The team kept reflexivity journals to reflect the impact of their positionality on their coding. Themes were first derived deductively using HF typologies (patient-oriented dyads, caregiver-oriented dyads, and collaboratively-oriented dyads), and then inductively refined and re-categorized based on concepts from the van Houtven et al. framework. RESULTS We believe there is a need to formally and intentionally expand HF technologies to be inclusive of dyadic needs and goals. We suggest defining three opportunities for where value can be added during technology design. First, identify how technology may be leveraged to increase psychological bandwidth, curb uncertainty, and provide peace of mind. We found actionable feedback to be highly desired by both partners. Second, develop technology that can serve as a member of the dyad’s support system. In our experience, automated prompts to patients for taking measurements can mimic the support typically provided by ICs and ease their load. Third, consider how technology can mitigate the dyad’s clinical knowledge requirements and learning curve. Our approach included real-time actionable feedback paired with a human-in-the-loop, nurse-led model of care. CONCLUSIONS Our findings identified a need to focus on improving the dyadic experience as a whole by building IC functionality into digital health self-management interventions. Through a shared model of care that supports the role of the patient in their own HF management, includes ICs to expand and enhance the patient’s capacity to care, and acknowledges the needs of ICs to care for themselves, we anticipate improved outcomes for both partners.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.000 | 0.003 |
| 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".