“Nothing is going to replace an in-person visit”: Canadian long-term care providers’ and recipients’ perspectives on when telehealth for physician visits is not appropriate
Bibliographic record
Abstract
Background: Within long-term care (LTC) homes, telehealth use has been found to reduce unnecessary emergency department transfers, support the care needs of rural and underserved communities, and supplement in-person physician care. Despite these benefits, it is not well understood when telehealth is not an appropriate medium for providing physician care to residents with complex health needs. This knowledge gap must be addressed given the recent rise in telehealth use in LTC homes in many health systems following the COVID-19 pandemic, when virtual care use increased in many health care sectors to limit travel and in-person exposure risks, that is expected to be maintained going forward. Methods: This analysis contributes to a broader evaluative study investigating care provider and care recipient experiences and preferences for physician telehealth in LTC homes within the Fraser Health region in British Columbia, Canada. For data collection, semi-structured interviews and focus groups were undertaken with seventy care providers (staff, physicians) and recipients (residents, family caregivers). Using a thematic approach, transcripts were analyzed to find common instances when using telehealth for physician care was seen as not appropriate across participant groups. Results: Three types of patient care activities were identified as not appropriate to be conducted via physician visits using telehealth. First, new patient visits were thought to benefit from an interpersonal and conversational familiarity that could not be supported by telehealth. Second, difficult in-depth conversations that required conversational nuance (e.g., eye contact, supportive body language), such as palliative care planning, were thought to be inappropriate for telehealth appointments. Finally, instances where LTC staff would need to perform hands-on clinical assessments on behalf of physicians who were attending virtually via telehealth were not seen as desirable. Conclusions: This analysis highlights perspectives surrounding when telehealth is not appropriate for providing physician services for residents in LTC based on the preferences and experiences shared by both care recipients and care providers. The findings present an opportunity to develop and implement guidelines on appropriate use of telehealth in LTC to support best care practices.
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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 | 0.010 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.018 | 0.006 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.006 |
| 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".