Determinants of the Intention to Use Teledermatology: Evidence From Dermatologists and Primary Care Physicians
Bibliographic record
Abstract
Background: Teledermatology (TD) is one of the applications of electronic health and telemedicine that involves the use of information and communication technologies (ICTs) for the care of skin diseases. Previous studies on TD indicate that it seems to be effective in diagnosing early malignant pathologies, such as melanoma, and in reducing waiting lists by prioritizing urgent cases of pathology. Despite these advantages, the implementation of TD is still low in many areas. Objective: Most previous studies on TD have focused on analyzing the results of TD use. However, to completely understand TD, it is necessary to consider the determinants of its use. This study analyzes the factors that motivate medical professionals to use TD in their clinical practice. Methods: A survey that targeted a total population of 743 medical professionals from health care institutions in Andalusia (Spain) was used. The study sample comprised 223 doctors (87 dermatologists and 136 primary care physicians). Results: Using an extended Technology Acceptance Model and microdata for the 223 physicians, a cluster analysis (of the user’s ICT profile) and binary logistic regression analysis were conducted. This analysis demonstrated the presence of 3 clusters in the sample with respect to the use of technology (cluster 1: advanced use of ICTs; cluster 2: moderate use of ICTs; and cluster 3: scarce use of ICTs). The analysis performed confirmed the model’s goodness of fit, which allowed 69% of the variable’s variance to be explained. The outcomes revealed that the factors that were most important when implementing a TD system were the user’s ICT profile (P=.048), system efficiency (P<.001), and preference of the subjects involved (P=.008; P<.005). The quality of the assistance, the difficulties arising from the use of technology (information security and confidentiality), or interests of the administration were not decisive factors for the implementation of TD. Subsequently, we performed a logistic regression analysis, separating primary care doctors from dermatologists. For the former, the determining factors were the ICT profile and the efficiency of the system, whereas, among dermatologists, only the preference of each individual was considered to be a determining factor. Conclusions: The use of TD should be accompanied by a comprehensive program of validation and evaluation. These results show that determinants of TD implementation differ depending on the subjects involved. Therefore, it is essential to perform studies before the implementation of a TD system to identify and influence the aforementioned predictive factors.
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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) | 0.000 | 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) | 0.000 | 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".