AI-Supported Electrocardiogram Interpretation: The Effect of Support Presentation on Diagnostic Accuracy, Psychological Need Satisfaction, and Diagnosis Time

Interpreting electrocardiograms (ECGs) is an important but complex and error-prone task. While diagnostic support algorithms exist, how support is displayed and how clinicians interact with ECG diagnostic and clinical decision support systems in general remain underexplored. In this preregistered experiment, we studied how providing clinicians with different versions of diagnostic support affects ECG interpretation. All four support types improved diagnosis accuracy compared to a no-support control condition, but the most effective was support offering visual ECG trace markings. User experience, in the form of psychological need satisfaction of competence and security, was highest when clinicians first viewed the ECG independently and then received support in a second stage. The latter two-stage support also resulted in the shortest diagnosis times. We conclude with design and research implications for creating clinician-algorithmic support interactions that improved user experience, efficacy, and effectiveness in the present study, and may ultimately contribute to patient safety.