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

Area of Science:

• Medical Education
• Cardiology
• Diagnostic Skills

Background:

• Electrocardiography (ECG) interpretation is crucial for physicians, yet trainees often exhibit poor diagnostic accuracy.
• Existing training methods may not be optimally effective for developing essential ECG interpretation skills.

Purpose of the Study:

• To evaluate the effectiveness of a self-generation (SG) diagnosis format versus a multiple-choice (MCQ) format for improving ECG interpretation learning.
• To assess the impact of SG training on immediate and delayed diagnostic performance and interpretation speed.

Main Methods:

• Medical students and residents were randomly assigned to either an MCQ or an SG ECG interpretation training format.
• Training involved 30 ECGs, followed by immediate and delayed post-tests (3-4 weeks later).
• The SG format involved free-text diagnosis entry with autocomplete assistance.

Main Results:

• Participants in the SG group showed significantly higher scores on the immediate post-test (78% vs. 57%) with a large effect size.
• The SG group diagnosed primary conditions faster (32 vs. 56 seconds) but identified fewer secondary diagnoses.
• A trend favored the SG group on the delayed post-test, though statistical significance was limited by lower completion rates.

Conclusions:

• Practicing ECG interpretation via self-generation of diagnoses enhances immediate learning outcomes and diagnostic fluency.
• The self-generation method shows promise for improving ECG interpretation skills among medical trainees.
• Further studies are needed to confirm findings and explore long-term knowledge retention.