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 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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".