Empowering Patients with Heart Failure Through Digital Health: The Role of Family Physicians
Bibliographic record
Abstract
Abstract Heart failure (HF) affects millions worldwide and is associated with high hospital readmission rates. Patients with HF often face challenges such as medication non-adherence, mild cognitive decline, limited healthcare access, and poor knowledge of their condition. Validated digital health tools (DHTs) can help manage HF and prevent symptom worsening. However, it is essential to consider the input of family physicians (FPs) in the development of user-centered DHTs. This study aimed to understand FPs’ attitudes and needs regarding the feasible and effective use of existing self-care DHTs to support older patients with HF. This qualitative study used a user-centered design framework. Semi-structured interviews with twelve FPs were conducted, using persona-case scenarios. The data were analyzed using NVivo 12 software and Braun and Clarke’s thematic analysis method. The following themes were revealed: availability of advice in challenging situations, patient clinical data, digital health tool competencies, patient factors, attitudes and preferences toward digital health technology, and the primary care climate . This study explores family physicians’ (FPs) needs and expectations in supporting seniors with heart failure (HF) through self-care digital health technologies (DHTs). The findings highlight key challenges FPs face in managing chronic diseases in older adults and emphasize critical considerations for designing HF digital health platforms. Specifically, the study underscores the importance of feasibility and effective integration into primary care, ensuring these platforms align with clinical workflows and enhance patient self-management. These insights can guide the development of user-centered DHTs that support both physicians and patients, ultimately improving HF care in primary care settings. Author Summary This study aimed to explore family physicians’ (FPs) attitudes and needs regarding the feasibility and effectiveness of supporting older patients with heart failure (HF) using a self-care digital health tool (DHT). The DHT used in this study is CorLibra, a medical device designed to promote HF self-management in the home setting. CorLibra helps patients monitor their weight daily and adjust their diuretic medications accordingly. The tool was specifically developed to address the unique needs of older adults based on extensive qualitative research. Our findings provide valuable insights into how FPs can effectively support patients using such tools to manage chronic conditions like HF.
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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 | 0.005 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".