Assessing Patient Satisfaction with Live-Interactive Teledermatology Visits During the COVID-19 Pandemic: A Survey Study
Bibliographic record
Abstract
Introduction: Coronavirus disease 2019 (COVID-19) has brought teledermatology to the forefront. Understanding patients' experiences will clarify its benefits and limitations. Materials and Methods: Patients evaluated through live-interactive teledermatology at New York University Langone Health March–June 2020 were surveyed. Patient demographics, satisfaction with, and preferences between teledermatology and in-person visits across four domains (visit preparation, provider communication, physical examination, and treatment plan/follow-up) were collected. Results/Discussion: Of 602 respondents, >70% indicated at least equal satisfaction compared with in-person visits across all domains. More than a quarter of patients were dissatisfied with the virtual examination and more than half preferred in-person examinations. Male gender was associated with treatment plan/follow-up satisfaction ( p = 0.03). Patients ≥66 years preferred in-person visit preparation, communication, and treatment plan/follow-up (all p < 0.01). New patients were less satisfied with teledermatology communication ( p = 0.02) and treatment plan/follow-up ( p < 0.01) but preferred teledermatology visit preparation ( p = 0.01). Conclusions: Patients were satisfied with live-interactive teledermatology during the COVID-19 pandemic, although preferred in-person physical examinations. Satisfaction and preferences varied between patient populations.
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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 | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".