%0 Journal Article
%@ 2562-0959
%I JMIR Publications
%V 6
%N 
%P e48357
%T Improving Skin Cancer Diagnostics Through a Mobile App With a Large Interactive Image Repository: Randomized Controlled Trial
%A Nervil,Gustav Gede
%A Ternov,Niels Kvorning
%A Vestergaard,Tine
%A Sølvsten,Henrik
%A Chakera,Annette Hougaard
%A Tolsgaard,Martin Grønnebæk
%A Hölmich,Lisbet Rosenkrantz
%+ Department of Plastic Surgery, Herlev-Gentofte Hospital, Borgmester Ib Juuls vej 5, Opgang 3, 4. etage, E7, Herlev, 2730, Denmark, 45 38689467, gustavnervil@gmail.com
%K dermoscopy
%K nevi
%K skin neoplasms
%K benign skin tumors
%K melanoma
%K skin cancer
%K medical education
%K eLearning
%K digital learning
%K diagnostic test
%K mHealth
%K mobile app
%K recognition training
%K skin lesions
%D 2023
%7 9.8.2023
%9 Original Paper
%J JMIR Dermatol
%G English
%X Background: Skin cancer diagnostics is challenging, and mastery requires extended periods of dedicated practice. Objective: The aim of the study was to determine if self-paced pattern recognition training in skin cancer diagnostics with clinical and dermoscopic images of skin lesions using a large-scale interactive image repository (LIIR) with patient cases improves primary care physicians’ (PCPs’) diagnostic skills and confidence. Methods: A total of 115 PCPs were randomized (allocation ratio 3:1) to receive or not receive self-paced pattern recognition training in skin cancer diagnostics using an LIIR with patient cases through a quiz-based smartphone app during an 8-day period. The participants’ ability to diagnose skin cancer was evaluated using a 12-item multiple-choice questionnaire prior to and 8 days after the educational intervention period. Their thoughts on the use of dermoscopy were assessed using a study-specific questionnaire. A learning curve was calculated through the analysis of data from the mobile app. Results: On average, participants in the intervention group spent 2 hours 26 minutes quizzing digital patient cases and 41 minutes reading the educational material. They had an average preintervention multiple choice questionnaire score of 52.0% of correct answers, which increased to 66.4% on the postintervention test; a statistically significant improvement of 14.3 percentage points (P<.001; 95% CI 9.8-18.9) with intention-to-treat analysis. Analysis of participants who received the intervention as per protocol (500 patient cases in 8 days) showed an average increase of 16.7 percentage points (P<.001; 95% CI 11.3-22.0) from 53.9% to 70.5%. Their overall ability to correctly recognize malignant lesions in the LIIR patient cases improved over the intervention period by 6.6 percentage points from 67.1% (95% CI 65.2-69.3) to 73.7% (95% CI 72.5-75.0) and their ability to set the correct diagnosis improved by 10.5 percentage points from 42.5% (95% CI 40.2%-44.8%) to 53.0% (95% CI 51.3-54.9). The diagnostic confidence of participants in the intervention group increased on a scale from 1 to 4 by 32.9% from 1.6 to 2.1 (P<.001). Participants in the control group did not increase their postintervention score or their diagnostic confidence during the same period. Conclusions: Self-paced pattern recognition training in skin cancer diagnostics through the use of a digital LIIR with patient cases delivered by a quiz-based mobile app improves the diagnostic accuracy of PCPs. Trial Registration: ClinicalTrials.gov NCT05661370; https://classic.clinicaltrials.gov/ct2/show/NCT05661370 
%M 37624707
%R 10.2196/48357
%U https://derma.jmir.org/2023/1/e48357
%U https://doi.org/10.2196/48357
%U http://www.ncbi.nlm.nih.gov/pubmed/37624707