Telemedicine Use during the COVID-19 Pandemic: Results of an International Survey
Bibliographic record
Abstract
Abstract Objective The aim of the study is to survey hand surgeons' perspectives on telemedicine during the coronavirus disease 2019 (COVID-19) pandemic and intended applications after the pandemic. Methods Online surveys were sent to 285 Canadian and American surgeons in late April and early May 2020. Results Response rate was 63% (180)—84% (152) American and 16% (28) Canadian. Forty-three percent (76) of respondents were in private practice, 36% (64) academics, 13% (24) privademics, and 6% (12) hospital employed. The most common telemedicine platform was Zoom. During the pandemic, 42% of patient visits were conducted via telemedicine; however, 37% required a subsequent in-person office visit. The most common complaint by surgeons was the inability to provide routine in-office procedures. The most beneficial feature was ease of use, and the most frustrating feature was connectivity difficulty. Time spent was similar to in-person visits, and surgeons were likely to recommend their platforms. Surgeons were neutral about using telehealth in the future and were most likely to use it for follow-up visits. New patient visits for traumatic injuries or fractures were of limited value. Canadians used telemedicine for a greater proportion than Americans (50 vs. 40%, p <0.05) and spent more time than in-person visits (7/10 vs. 5/10, p <0.05). Americans were more likely to use telemedicine for postoperative follow-up visits (6/10 vs. 4/10, p <0.05) and in mornings before clinic opens (4/10 vs. 2/10, p <0.05). Private practices were more likely to use telemedicine for future allied health provider visits than all other practice types (p <0.05). Conclusion Telemedicine comprised nearly half of patient encounters during the COVID-19 pandemic, but limitations remain.
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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 | 0.005 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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".