Investigating Learning Effects Through the Implementation of Teledermatology Consultations Among General Practitioners in Germany: Mixed Methods Process Evaluation
Bibliographic record
Abstract
Background: The increasing prevalence of dermatological diseases will pose a growing challenge to the health care system and, in particular, to general practitioners (GPs) as the first point of contact for these patients. In many countries, primary care physicians are supported by teledermatology services. Objective: The aim of this study was to detect learning effects and gains among GPs through teledermatology consultations (TCs) in daily practice. Methods: As part of a mixed methods study embedded in a cluster-randomized controlled trial (TeleDerm), a full survey and semiguided face-to-face interviews were conducted among GPs of participating intervention practices using the telemedicine approach. A TC assessment tool (TC-AT) was developed to evaluate the quality of clinical data and images of TCs conducted during the run-in and intervention phases, with a score ranging from 0 (lowest quality) to 10 (highest quality). Mixed methods analysis triangulated qualitative content analysis, survey data with a growth curve model calculated from TC-AT data, comparing subjective experiences of GPs with objective process data. Results: A total of 487 TCs of 33 practices were analyzed. Questionnaires from n=46 GPs (practice-level response rate: 69.9%) were included in the quantitative analysis. Two-thirds of the GPs (n=31; 67.4%) in the written survey rated the TCs as helpful for differential diagnosis and treatment management. Improved self-reported confidence in diagnosing skin diseases due to the timely clinical feedback from dermatologists was reported by more than half of the responding GPs (n=25; 54.3%). In the interviews (n=13), teleconsultations were mainly seen as a learning opportunity by the GPs. Regarding the quality of TCs, a mean TC-AT score of 7.4 (SD 1.7, range 0-10) was observed. In the growth curve model, a simple linear time trend provided the best fit to the TC-AT score trajectory across the observed study period. A significant time * TC-AT start score interaction was found (F452=30.66, P<.001). While regardless of the initial TC-AT score, repeated TCs lead to process quality improvements over time, post hoc probing of the TC-AT start score as a moderator of the learning effect over time revealed the highest improvements among GP practices with a lower initial TC-AT score (-1 SD: standardized slope=0.59, P<.001; mean: standardized slope=0.38, P<.001; +1 SD: standardized slope=0.18, P<.001). Conclusions: TCs have been shown to be an effective method of education for GPs in terms of "learning on the job" in daily practice. The telemedicine approach seems to be an easily implementable and effective tool to support continuing medical education in the field of dermatology. Strategies could be developed to train GPs and medical students in the use of TC to adequately prepare them for the increasing technological demands of their future profession in primary care.
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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 | 0.100 | 0.096 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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 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".