Using Implementation Science to Understand Teledermatology Implementation Early in the COVID-19 Pandemic: Cross-sectional Study
Bibliographic record
Abstract
Background: Implementation science has been recognized for its potential to improve the integration of evidence-based practices into routine dermatologic care. The COVID-19 pandemic has resulted in rapid teledermatology implementation worldwide. Although several studies have highlighted patient and care provider satisfaction with teledermatology during the COVID-19 pandemic, less is known about the implementation process. Objective: Our goal was to use validated tools from implementation science to develop a deeper understanding of the implementation of teledermatology during the COVID-19 pandemic. Our primary aims were to describe (1) the acceptability and feasibility of the implementation of teledermatology and (2) organizational readiness for the implementation of teledermatology during the COVID-19 pandemic. We also sought to offer an example of how implementation science can be used in dermatologic research. Methods: An anonymous, web-based survey was distributed to Association of Professors of Dermatology members. It focused on (1) the acceptability, feasibility, and appropriateness of teledermatology and (2) organizational readiness for implementing teledermatology. It incorporated subscales from the Organizational Readiness to Change Assessment-a validated measure of organizational characteristics that predict implementation success. Results: Of the 518 dermatologists emailed, 35 (7%) responded, and all implemented or scaled up teledermatology during the pandemic. Of the 11 care providers with the highest level of organizational readiness, 11 (100%) said that they plan to continue using teledermatology after the pandemic. Most respondents agreed or strongly agreed that they had sufficient training (24/35, 69%), financial resources (20/35, 57%), and facilities (20/35, 57%). However, of the 35 respondents, only 15 (43%) agreed or strongly agreed that they had adequate staffing support. Most respondents considered the most acceptable teledermatology modality to be synchronous audio and video visits with supplemental stored digital photos (23/35, 66%) and considered the least acceptable modality to be telephone visits without stored digital photos (6/35, 17%). Overall, most respondents thought that the implementation of synchronous audio and video with stored digital photos (31/35, 89%) and telephone visits with stored digital photos (31/35, 89%) were the most feasible. When asked about types of visits that were acceptable for synchronous video/audio visits (with stored digital photos), 18 of the 31 respondents (58%) said "new patients," 27 (87%) said "existing patients," 19 (61%) said "medication monitoring," 3 (10%) said "total body skin exams," and 22 (71%) said "lesions of concern." Conclusions: This study serves as an introduction to how implementation science research methods can be used to understand the implementation of novel technologies in dermatology. Our work builds upon prior studies by further characterizing the acceptability and feasibility of different teledermatology modalities. Our study may suggest initial insights on how dermatology practices and health care systems can support dermatologists in successfully incorporating teledermatology after the 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 | 0.056 | 0.099 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.006 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| 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".