Gamification of Electronic Learning in Radiology Education to Improve Diagnostic Confidence and Reduce Error Rates.

The purpose of this study is to validate an electronic learning, or e-learning, concept featuring gamification elements, rapid case reading, and instant feedback. An e-learning concept was devised that offered game levels for the purpose of providing training in the detection of pneumothorax in 195 cases, with questions read in rapid succession and instant feedback provided for each case. The user's task was to locate the pneumothorax on chest radiographs and indicate its presence by clicking a mouse. The game level design included an entry test consisting of 15 cases, training levels with increasing difficulty that involved 150 cases, and a final test that including 30 cases (the 15 cases from the entry test plus 15 new cases). A total of 126 candidates were invited via e-mail to participate and were asked to complete a survey before and after playing the game, which is known as RapRad. The level of diagnostic confidence and the error rate before and after playing the game were compared using a Wilcoxon signed rank test. Fifty-nine of 126 participants (47%) responded to the first survey and finished the game. Of these 59 participants, 29 (49%) responded to the second survey after completing the game. Diagnostic confidence in pneumothorax detection improved significantly, from a mean (± SD) score of 4.3 ± 2.1 on the entry test to a final score of 7.3 ± 2.1 ( < 0.01) after playing RapRad, with the score measured on a 10-point scale, with 10 denoting the highest possible score. Of the participants, 93% indicated that they would use the game for learning purposes again, and 87% indicated that they had fun using RapRad (7% had a neutral response and 6% had a negative response). The error rate (i.e., the number of failed attempts to answer a question correctly) significantly decreased from 39% for the entry test to 22% for the final test ( < 0.01). Our e-learning concept is capable of improving diagnostic confidence, reducing error rates in training pneumothorax detection, and offering fun in interaction with the platform.