Improving ECG interpretation through self-generation of diagnoses during practice: a randomized study.

ABSTRACT While electrocardiogram (ECG) is a fundamental skill for most physicians, trainees have poor diagnostic performance when interpreting ECGs. In this study, we examine a strategy to improve learning ECG interpretation: self-generation of diagnoses during online practice. We randomly assigned medical students and residents to one of two ECG interpretation training formats: multiple-choice (MCQ) or self-generation format (SG) where participants free-text type their diagnosis aided by an auto-complete feature. The training phase consisted of 30 ECGs, after which, participants completed an immediate post-test and delayed post-test (3-4 weeks later). 48 participants completed the training module, 45 completed the immediate post-test and 27 completed the delayed post-test. Participants assigned to the SG format scored higher on the immediate post-test compared to those who practiced using the MCQ format with a large effect size (78 vs. 57%; d=0.94; p=0.02). There was a trend favouring SG on the delayed post-test with a moderate effect size (67 vs. 56%;d=0.65; p=0.09). However, only 60% of participants completed the delayed post-test, which hindered the detection of a statistically significant difference. The SG group made the correct primary diagnosis at a faster rate (32 vs. 56 seconds; p BRIEF SUMMARY: In an effort to improve the teaching of ECG interpretation, the standard practice of multiple-choice questions was compared with self-generation of diagnoses in a prospective randomized control trial. Training using self-generation of diagnoses resulted in superior post-test performance and fluency when compared to training with multiple choice questions.