Clinicians’ Perspectives on the Telehealth Serious Illness Care Program for Older Adults With Myeloid Malignancies: Single-Arm Pilot Study
Bibliographic record
Abstract
BACKGROUND: Serious illness conversations may help patients avoid unwanted treatments. We previously piloted the telehealth Serious Illness Care Program (SICP) for older adults with acute myeloid leukemia and myelodysplastic syndrome. OBJECTIVE: In this study, we aimed to understand the experience of the telehealth SICP from the clinician's perspective. METHODS: We studied 10 clinicians who delivered the telehealth SICP to 20 older adults with acute myeloid leukemia or myelodysplastic syndrome. Quantitative outcomes included confidence and acceptability. Confidence was measured using a 22-item survey (range 1-7; a higher score is better). Acceptability was measured using an 11-item survey (5-point Likert scale). Hypothesis testing was performed at α=.10 (2-tailed) due to the pilot nature and small sample size. Clinicians participated in audio-recorded qualitative interviews at the end of the study to discuss their experience. RESULTS: A total of 8 clinicians completed the confidence measure and 7 clinicians completed the acceptability measure. We found a statistically significant increase in overall confidence (mean increase of 0.5, SD 0.6; P=.03). The largest increase in confidence was in helping families with reconciliation and goodbye (mean 1.4, SD 1.5; P=.04). The majority of clinicians agreed that the format was simple (6/7, 86%) and easy to use (6/7, 86%). Clinicians felt that the telehealth SICP was effective in understanding their patients' values about end-of-life care (7/7, 100%). A total of three qualitative themes emerged: (1) the telehealth SICP deepened relationships and renewed trust; (2) each telehealth SICP visit felt unique and personal in a positive way; and (3) uninterrupted, unrushed time optimized the visit experience. CONCLUSIONS: The telehealth SICP increased confidence in having serious illness conversations while deepening patient-clinician relationships. TRIAL REGISTRATION: ClinicalTrials.gov NCT04745676; https://www.clinicaltrials.gov/study/NCT04745676.
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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 | 0.017 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".