Improving Online ECG Interpretation Through Self-Generation of Diagnoses During Practice: A Randomized Study

Although electrocardiography (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 2 ECG interpretation training formats: multiple-choice (MCQ) or self-generation (SG) format, where participants free-text type their diagnosis aided by an autocomplete feature. The training phase consisted of 30 ECGs, after which participants completed an immediate post test and delayed post test (3-4 weeks later). Forty-eight 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 with those who practiced with 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 < 0.001) but had a lower detection of secondary diagnoses (22 vs 42%; P = 0.007). Practicing ECG interpretation using self-generation of diagnoses improved immediate post test performance and fluency. Replication in other contexts and with other populations is required to confirm our findings and to further study retention.