Impact of an Intrainstitutional Teledermatology Service: Mixed-Methods Case Study
Bibliographic record
Abstract
Background: Teledermatology provides timely access to consultative dermatology services while reducing the need for travel among patients in rural and underserviced areas. However, knowledge about the potential benefits of such a service in urban areas is limited. Objective: This study aimed to determine the impact of a geographically unrestricted, intrainstitutional, secure, email teledermatology service for dermatology. Methods: We employed a mixed-methods approach using chart review, surveys, and semistructured interviews from the Canada Health Infoway Benefits Evaluation Framework. Patient charts were reviewed for demographics, clinical characteristics, and outcomes. Electronic and paper surveys were sent to patients and providers to quantify aspects of the service, such as satisfaction and usability, on a Likert scale. Semistructured interviews of referring providers and a convenience sample of academic consultant dermatologists who were considering teledermatology for their practice were conducted. Interviews were transcribed and analyzed using manual coding and thematic analysis by both the primary author and a second independent reviewer. All results were concurrently triangulated in an overarching analysis. Results: A total of 76 consultations were reviewed over a period of 18 months, of which 84% were completely managed without an in-person visit. Only 6% of rashes required a subsequent in-person visit to a dermatologist for management, compared to 41% of lesions. In addition, 28% (21/76) of patients responded to the survey. Patients “strongly agreed” to use the service again, were satisfied with the management of their skin issue, and thought the service saved them time. In general, providers who answered the electronic survey “strongly agreed” that the service demonstrated quality, timeliness, and an educational benefit, but increased their administrative time. A total of 9 interviews of 5 referring providers and 4 dermatologists were completed. Triangulation of all study components supported the hypothesis that teledermatology benefits providers, patients, and the health care system. Conclusions: Intrainstitutional teledermatology has high satisfaction among patients and providers and saves patients time, even when there are no geographic or systemic barriers to access. This service may be most effective when targeted at rashes rather than lesions. Additional research on the cost-effectiveness and educational benefits of this service is warranted.
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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 | 0.000 |
| 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".