Quality of care with telemedicine for medical assistance in dying eligibility assessments: a mixed-methods study
Bibliographic record
Abstract
BACKGROUND: To facilitate access to medical assistance in dying (MAiD) in British Columbia, telemedicine has been used for eligibility assessments. This research explored the impacts of using telemedicine on quality of care. METHODS: This mixed-methods study consisted of data from 3 BC health authorities and semistructured interviews with a patient, support persons, providers and administrators about the use of telemedicine for MAiD eligibility assessment. Interviews were conducted by telephone, video meeting or email between June and November 2018. We analyzed the quantitative data using descriptive statistics. We categorized the qualitative data using the 7 dimensions of the BC Health Quality Matrix and then analyzed them qualitatively with abductive coding. RESULTS: Twenty-one participants (8 MAiD assessors, 1 patient, 7 support persons of patients and 5 MAiD administrators) were interviewed. Telemedicine for MAiD eligibility assessments was highly acceptable to the support persons and patient and to most assessors and administrators. Assessors expressed challenges with empathy, eye contact, nonverbal communication and missing contextual factors. Participants described which patients were appropriate and which were not. Telemedicine improved access and equity for the patients who received this service. It was perceived as an effective and efficient way to perform eligibility assessments. Concerns were expressed by assessors and administrators, but not by the patient or support persons, about confidentiality. Opinions varied on the requirement for a regulated health care professional to be in physical attendance with the patient to act as a witness. INTERPRETATION: Quality of care can be achieved with telemedicine for MAiD eligibility assessments for specific situations and patients, and this modality has the potential to expand access to MAiD. Updated clinical and administrative policies are needed to address barriers to telemedicine access and to best support patients and assessors using this technology.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".