Development and Usability of an Advance Care Planning Website (My Voice) to Empower Patients With Heart Failure and Their Caregivers: Mixed Methods Study
Bibliographic record
Abstract
Background: Web-based advance care planning (ACP) interventions offer a promising solution to improve ACP engagement, but none are specifically designed to meet the needs of patients with heart failure and their caregivers. Objective: We aimed to develop and assess the usability and acceptability of a web-based ACP decision aid called "My Voice," which is tailored for patients with heart failure and their caregivers. Methods: This study's team and advisory board codeveloped the content for both patient and caregiver modules in "My Voice." Using a mixed methods approach, we iteratively tested usability and acceptability, incorporating feedback from patients, caregivers, and health care professionals (HCPs). Results: We interviewed 30 participants (11 patients, 9 caregivers, and 10 HCPs). Participants found the website easy to navigate, with simple and clear content facilitating communication of patients' values and goals. They also appreciated that it allowed them to revisit their care goals periodically. The average System Usability Scale score was 74 (SD 14.8; range: 42.5-95), indicating good usability. Over 80% (8/11) of patients and 87% (7/8) of caregivers rated the website's acceptability as good or excellent. Additionally, 70% (7/10) of HCPs strongly agreed or agreed with 11 of the 15 items testing the website's acceptability. Conclusions: "My Voice" shows promise as a tool for patients with heart failure to initiate and revisit ACP conversations with HCPs and caregivers. We will evaluate its efficacy in improving patient and caregiver outcomes in a randomized controlled trial.
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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 | 0.061 | 0.048 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".