Evaluating the Experiences of New and Existing Teledermatology Patients During the COVID-19 Pandemic: Cross-sectional Survey Study
Bibliographic record
Abstract
Background As teledermatology has been widely adopted during the COVID-19 pandemic, it is essential to examine patients’ experiences and satisfaction with teledermatology. Objective We aimed to assess the teledermatology experiences of new and existing clinic patients in the context of the rapid shift toward teledermatology practices during the COVID-19 pandemic. Methods We conducted a cross-sectional study of 184 teledermatology patients who were assessed during the COVID-19 pandemic at a major southeastern medical center from May 13 to June 5, 2020. The primary outcome was patient satisfaction levels among new and existing patients. The secondary outcome was patients’ willingness to use teledermatology in the future. Results Of the 288 teledermatology patients who were assessed during the study period, 184 (63.9%) completed the survey. Patients reported high overall satisfaction with teledermatology, with 86.4% (159/184) of participants reporting positive overall satisfaction and experiences with teledermatology. New patients had significantly higher Likert scores for overall satisfaction with teledermatology than those of follow-up patients (new patients: mean 4.70; existing patients: mean 4.43; P=.03). Overall, patients’ satisfaction with teledermatology did not significantly differ based on age (P=.36), race and ethnicity (P=.46), education level (P=.11), residence (P=.74), or insurance status (P=.74). There were no significant differences in overall satisfaction between patients with and without prior telehealth experience (P=.53), between the video and telephone visit types (P=.17), and among platform types (P=.22). Prior telehealth experience was associated with higher odds of being willing to use telehealth in the future (odds ratio 2.39, 95% CI 1.31-4.35; P=.004). Conclusions This cross-sectional survey study demonstrates that during the rapid expansion of teledermatology, new clinic patients had significantly higher scores for overall satisfaction with their teledermatology experience compared to those of established clinic patients (P=.03). Prior telehealth experience was associated with higher odds of being willing to use teledermatology in the future. Overall, teledermatology expansion was met with high levels of patient satisfaction during the COVID-19 pandemic.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".