Understanding Physicians’ Preferences for Telemedicine During the COVID-19 Pandemic: Cross-sectional Study
Bibliographic record
Abstract
BACKGROUND: In contrast to the current broad dissemination of telemedicine across medical specialties, previous research focused on the effectiveness of telemedicine in special populations and for behavioral health encounters, demonstrating that both physician and patient factors impact the efficacious use of telemedicine. OBJECTIVE: We aim to evaluate physician perceptions of the appropriateness of telemedicine for patients attending the primary care practices of a federally qualified health center in New York City. METHODS: We used an anonymous cross-sectional survey including closed- and open-ended questions. We used chi-square to test whether providers from certain specialties were more likely to state they would use telemedicine in the future. We used t tests to compare age between those who would versus would not use telemedicine. We then used logistic regression to test whether age and specialty were both correlated with the desire to use telemedicine in the future. We used thematic content analysis to describe the reasons providers felt they would not want to use telemedicine in the future and to describe the situations for which they felt telemedicine would be appropriate. RESULTS: Of 272 health care providers who were sent the electronic survey, 157 (58%) responded within the 2-week survey time frame. The mean age of providers was 45 (range 28-75) years. Overall, 80% (126/157) stated they would use telemedicine in the future. Compared to the family medicine, internal medicine, behavioral health, dental, and obstetrics and gynecology specialties, providers from pediatrics, med-peds, subspecialties, and surgery (protelemedicine specialties) were more likely to believe telemedicine would be useful post pandemic (61/67 [91%] vs 65/90 [72%]; P<.001). Providers who reported they would use telemedicine in the future were younger (mean age 44, range 42-46 years vs mean age 50, range 46-55 years; P=.048). In the regression analysis, both protelemedicine specialties and age were significantly associated with odds of reporting they would use telemedicine in the future (prospecialties: odds ratio 5.2, 95% CI 1.7-16.2; younger age: odds ratio 1.05, 95% CI 1.01-1.08). Providers who did not want to use telemedicine in the future cited concerns about inadequate patient care, lack of physical patient interaction, technology issues, and lack of necessity. Providers who felt telemedicine would be useful cited the following situations: follow-up visits, medication refills, urgent care, patient convenience, and specific conditions such as behavioral health, dermatology visits, and chronic care management. CONCLUSIONS: The majority of health providers in this resource-limited setting in a federally qualified health center believed that telemedicine would be useful for providing care after the pandemic is over.
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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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".