Evaluation of a Novel Goals-of-Care Discussion Priming Tool (MyCare) in Inpatient General Internal Medicine Ward Settings: Feasibility, Acceptability, and Usability Study
Bibliographic record
Abstract
BACKGROUND: For patients who are seriously ill, conversations about prognosis, values, and goals can improve person-centered care; however, these conversations do not occur consistently. OBJECTIVE: We aimed to develop an online, interactive goals-of-care discussion priming tool called MyCare to facilitate conversations about patient goals among physicians, patients, and family members. Our objective was to determine the feasibility, acceptability, and usability of MyCare for patients who are seriously ill in hospitals. METHODS: We conducted a mixed methods study on internal medicine wards with patients who were seriously ill at 2 hospitals. Participants completed MyCare with a research assistant in their hospital room, and their responses were sent to their attending physician team. Patients, family members, and physicians participated in semistructured interviews to assess advance care planning engagement, usability, and acceptability. Patients also completed the System Usability Scale, the eHealth literacy scale, and survey questions about acceptability. RESULTS: Patients took a median time of 32 (IQR 21-43) minutes to complete MyCare, with a range of 12 to 101 minutes. The mean eHealth literacy score was 56 out of 100. In total, 19 (76%) out of 25 patients indicated that MyCare helped them think about and understand what was most important to them, and 16 (64%) stated that MyCare helped them think about the type of care they would want if their illness progressed. The mean advance care planning engagement scores on a 5-point scale before and after MyCare were 4.11 (SD 0.77) and 4.20 (SD 0.74), respectively (mean difference 0.09, 95% CI -0.08 to 0.34; P=.22). After completing MyCare, 16% (4/25) of the patients reported feeling more prepared to talk to their medical decision-maker about the type of care they would want if they were very sick or near the end of life, and none felt less prepared. The most useful elements for patients were (1) clarifying goals and wishes for care, (2) identifying people who supported them, and (3) having a resource to facilitate conversations with their families. The most useful elements for physicians were patients' prioritized values and goals. CONCLUSIONS: MyCare and similar tools will be more usable and acceptable if they are short and simple, focus on shared physician-patient priorities, and empower patients to advocate for their own goals.
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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 | 0.018 | 0.043 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| 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".