Personalized Care of Patients with Heart Failure: Are we Ready for a REWOLUTION? Insights from two International Surveys on Healthcare Professionals' Needs and Patients' Perceptions
Bibliographic record
Abstract
AIMS: Guidelines for the management of heart failure (HF) are evolving, and increasing emphasis is placed on patient-centred care. As part of the REWOLUTION HF (REal WOrLd EdUcaTION in HF) programme, we conducted two international surveys aimed at assessing healthcare professionals' (HCPs) educational needs and patients' perspectives on the care of HF. METHODS AND RESULTS: Anonymous online questionnaires co-developed by HF experts and patients assessed HCPs' educational needs (520 respondents, mostly cardiologists, in 67 countries) and patients' perceptions on HF impact and management (98 respondents in 18 countries). Among HCPs, 62.7% prioritized rapid initiation of all guideline-mandated medications over up-titration of some medications, and 87.7% always or frequently discussed treatment goals with patients. There was good agreement between HCPs and patients on key treatment goals, except for a greater emphasis on reducing hospitalizations among HCPs. The most frequently cited barriers to the provision of guideline-recommended pharmacological therapy were treatment side effects/intolerance, complex treatment regimens, low blood pressure, cost/reimbursement issues, and low estimated glomerular filtration rate. Most patients (81.6%) reported no difficulties taking medications as prescribed, although 21.4% felt they were taking too many pills. Patients wanted more information about HF and its consequences, prognosis, and treatments (70.4%, 74.5% and 76.6%, respectively). Cardiologists were the preferred source of information about HF, followed by general practitioners and HF nurses. CONCLUSIONS: These surveys provide valuable insights into HCPs' needs about personalized care for patients with HF, as well as patients' perceptions, expectations and preferences. These findings will be helpful to develop patient-centred, needs-driven quality improvement programmes.
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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 | 0.008 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".