Early Implementation and Evaluation of a Teledermatology Virtual Clinic Within an Academic Medical Center
Bibliographic record
Abstract
Background Teledermatology can increase patient access; however, its optimal implementation remains unknown. Objective This study aimed to describe and evaluate the implementation of a pilot virtual clinic teledermatology service at Duke University. Methods Leaders at Duke Dermatology and Duke Primary Care identified a teledermatology virtual clinic to meet patients’ access needs. Implementation was planned over the exploration, preparation, implementation, and sustainment phases. We evaluated the implementation success of teledermatology using the Reach, Effectiveness, Adoption, Implementation, and Maintenance framework and prioritized outcome collection through a stakeholder survey. We used the electronic health record and patient surveys to capture implementation outcomes. Results Our process consisted of primary care providers (PCPs) who sent clinical and dermatoscopic images of patient lesions or rashes via e-communication to a teledermatology virtual clinic, with a subsequent virtual clinic scheduling of a video visit with the virtual clinic providers (residents or advanced practice providers, supervised by Duke Dermatology attending physicians) within 2-5 days. The teledermatology team reviews the patient images on the day of the video visit and gives their diagnosis and management plan with either no follow-up, teledermatology nurse follow-up, or in-person follow-up evaluation. Implementation at 4 pilot clinics, involving 19 referring PCPs and 5 attending dermatologists, began on September 9, 2021. As of October 31, 2021, a total of 68 e-communications were placed (50 lesions and 18 rashes) and 64 virtual clinic video visits were completed. There were 3 patient refusals and 1 conversion to a telephonic visit. Participating primary care clinics differed in the number of patients referred with completed visits (range 2-32) and the percentage of providers using e-communications (range 13%-53%). Patients were seen soon after e-communication placement; compared to in-person wait times of >3 months, the teledermatology virtual clinic video visits occurred on average 2.75 days after e-communication. In total, 20% of virtual clinic video visits were seen as in-person visit follow-up, which suggests that the majority of patients were deemed treatable at the virtual clinic. All patients who returned the patient survey (N=10, 100%) agreed that their clinical goals were met during the virtual clinic video visits. Conclusions Our virtual clinic model for teledermatology implementation resulted in timely access for patients, while minimizing loss to follow-up, and has promising patient satisfaction outcomes. However, participating primary care clinics differ in their volume of referrals to the virtual clinic. As the teledermatology virtual clinics scale to other clinic sites, a systematic assessment of barriers and facilitators to its implementation may explain these interclinic differences. Acknowledgments We are grateful to the Private Diagnostic Clinic and Duke Institute for Health Innovation for their support. Conflicts of Interest None declared.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".