Virtual Specialist Care During the COVID-19 Pandemic: Multimethod Patient Experience Study
Bibliographic record
Abstract
BACKGROUND: Transitioning nonemergency, ambulatory medical care to virtual visits in light of the COVID-19 global pandemic has been a massive shift in philosophy and practice that naturally came with a steep learning curve for patients, physicians, and clinic administrators. OBJECTIVE: We undertook a multimethod study to understand the key factors associated with successful and less successful experiences of virtual specialist care, particularly as they relate to the patient experience of care. METHODS: This study was designed as a multimethod patient experience study using survey methods, descriptive qualitative interview methodology, and administrative virtual care data collected by the hospital decision support team. Six specialty departments participated in the study (endoscopy, orthopedics, neurology, hematology, rheumatology, and gastroenterology). All patients who could speak and read English and attended a virtual specialist appointment in a participating clinic at St. Michael's Hospital (Toronto, Ontario, Canada) between October 1, 2020, and January 30, 2021, were eligible to participate. RESULTS: During the study period, 51,702 virtual specialist visits were conducted in the departments that participated in the study. Of those, 96% were conducted by telephone and 4% by video. In both the survey and interview data, there was an overall consensus that virtual care is a satisfying alternative to in-person care, with benefits such as reduced travel, cost, time, and SARS-CoV-2 exposure, and increased convenience. Our analysis further revealed that the specific reason for the visit and the nature and status of the medical condition are important considerations in terms of guidance on where virtual care is most effective. Technology issues were not reported as a major challenge in our data, given that the majority of "virtual" visits reported by our participants were conducted by telephone, which is an important distinction. Despite the positive value of virtual care discussed by the majority of interview participants, 50% of the survey respondents still indicated they would prefer to see their physician in person. CONCLUSIONS: Patient experience data collected in this study indicate a high level of satisfaction with virtual specialty care, but also signal that there are nuances to be considered to ensure it is an appropriate and sustainable part of the standard of care.
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.003 |
| 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".