Challenges and opportunities for increasing patient involvement in heart failure self-care programs and self-care in the post–hospital discharge period
Bibliographic record
Abstract
BACKGROUND: People living with heart failure (HF) are particularly vulnerable after hospital discharge. An alliance between patient authors, clinicians, industry, and co-developers of HF programs can represent an effective way to address the unique concerns and obstacles people living with HF face during this period. The aim of this narrative review article is to discuss challenges and opportunities of this approach, with the goal of improving participation and clinical outcomes of people living with HF. METHODS: This article was co-authored by people living with HF, heart transplant recipients, patient advocacy representatives, cardiologists with expertise in HF care, and industry representatives specializing in patient engagement and cardiovascular medicine, and reviews opportunities and challenges for people living with HF in the post-hospital discharge period to be more integrally involved in their care. A literature search was conducted, and the authors collaborated through two virtual roundtables and via email to develop the content for this review article. RESULTS: Numerous transitional-care programs exist to ease the transition from the hospital to the home and to provide needed education and support for people living with HF, to avoid rehospitalizations and other adverse outcomes. However, many programs have limitations and do not integrally involve patients in the design and co-development of the intervention. There are thus opportunities for improvement. This can enable patients to better care for themselves with less of the worry and fear that typically accompany the transition from the hospital. We discuss the importance of including people living with HF in the development of such programs and offer suggestions for strategies that can help achieve these goals. An underlying theme of the literature reviewed is that education and engagement of people living with HF after hospitalization are critical. However, while clinical trial evidence on existing approaches to transitions in HF care indicates numerous benefits, such approaches also have limitations. CONCLUSION: Numerous challenges continue to affect people living with HF in the post-hospital discharge period. Strategies that involve patients are needed, and should be encouraged, to optimally address these challenges.
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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 | 0.014 | 0.034 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".