Coronavirus Disease 2019 Pandemic as Catalyst for Telemedicine Adoption: A Single-Center Experience
Bibliographic record
Abstract
Background: Telemedicine use has increased due to stay-at-home orders during the novel coronavirus 2019 pandemic. We explored ambulatory provider's perception on telemedicine. Methods: An anonymized survey was e-mailed to physicians and midlevel providers of our university hospital ambulatory clinics to assess current use, preferences for future use, and satisfaction with televisits. Results: Of all providers, 249 responded (response rate 24%, 121 [48.6%] men, 177 [71.1%] attending physicians, 43 [17.2%] trainees, and 29 [11.6%] midlevel providers). Most respondents (120, 48.2%) belonged to subspecialties in medicine. At the time of the survey, 168 (67.5%) were using telemedicine for less than half of all visits and had video capabilities, of whom 224 (90%) considered it to be effective for return visits and 37 (15%) perceived it to be effective for new patients, 217 (87.1%) wanted to continue with telemedicine practice, and 113 (45.4%) preferred to use telemedicine for more than a quarter of their future patients even after the pandemic. Most (194 [77.9%]) were satisfied with telemedicine and we found no differences among specialties. Those with audio-only visits reported least effectiveness for new patient evaluation (p < 0.001) and overall provider satisfaction (p = 0.02) when compared with others. Those who saw more than a quarter of their patients through televisits desired to increase their future televisits to >50% including new patients (p < 0.001). Conclusions: There is widespread interest in telemedicine in all specialties. Acceptance is high for return visits, but low for new patient visits. Improvement in technology to have both audio and video capability consistently may foster further interest toward increasing telemedicine in the future.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".