Rapid Expansion of a Teledermatology Web Application for Digital Dermatology Assessment Necessitated by the COVID-19 Pandemic: Retrospective Evaluation
Bibliographic record
Abstract
BACKGROUND: The COVID-19 pandemic necessitated a change in the provision of outpatient care in dermatology. OBJECTIVE: A novel, asynchronous, digital consultation platform was codeveloped with 2 National Health Service dermatology teams to improve access and enhance choice in outpatient care. METHODS: The rollout of the platform was accelerated during the initial COVID-19 lockdown, and its wider use across 2 Scottish health boards was retrospectively evaluated. Integrated with the hospital booking system and electronic patient record, the platform provides an alternative to face-to-face consultations, using information and images submitted by the patients. RESULTS: In total, 297 new patient consultations and 108 return patient consultations were assessed, and 80% (324/405) of the images submitted were of satisfactory quality. The consultations were, on average, 3 minutes shorter than equivalent face-to-face interactions, and a total of 5758 km of patient travel was avoided. Outcomes included web-based reviews (66/405, 16.3%), face-to-face reviews (190/405, 46.9%), biopsies (46/405, 11.4%), discharge (89/405, 22%), and other treatments or investigations (14/405, 3.5%). High levels of patient satisfaction (92/112, 82.1%) were reported. CONCLUSIONS: Digital dermatology assessments are now included in the choices for consultation types that are available to patients, helping to augment service capacity during pandemic recovery.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.000 | 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".