AI in Healthcare
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.
Executive Impact
This research demonstrates that carefully designed AI support in ECG interpretation can significantly enhance diagnostic accuracy, reduce diagnosis time, and improve clinician psychological well-being. The study found that visual support and a two-stage presentation model are most effective.
Max Diagnostic Improvement Potential
Diagnosis Time Reduction
UX Benefit Factor
Clinician Preference for Support
Deep Analysis & Enterprise Applications
Select a topic to dive deeper, then explore the specific findings from the research, rebuilt as interactive, enterprise-focused modules.
Enterprise Process Flow
ECG only
→
ECG diagnosis (Immediate)
→
ECG diagnosis + marking (Immediate)
→
ECG diagnosis (Two-stage)
→
ECG diagnosis + marking (Two-stage)
| Support Type | Accuracy (%) |
|---|---|
| ECG only | 57 |
| ECG diagnosis (Immediate) | 75 |
| ECG diagnosis + marking (Immediate) | 76 |
| ECG diagnosis (Two-stage) | 70 |
| ECG diagnosis + marking (Two-stage) | 78 |
Highest Diagnostic Accuracy with Visual Markings & Two-Stage Support
Participants Preferred Two-Stage Visual Marking Support
Optimizing Clinician Autonomy and Competence
The study highlights a tension between preferring support and autonomy satisfaction. Autonomy satisfaction was highest in the no-support condition, but clinicians still preferred support. Two-stage support protocols (first review ECG independently, then receive AI support) increased competence and security satisfaction while minimizing the negative impact on autonomy, fostering better user experience and trust.
Reduced Diagnosis Time with Two-Stage Support
| Support Type | Avg. Time (s) |
|---|---|
| ECG only | 85 |
| ECG diagnosis (Immediate) | 86 |
| ECG diagnosis + marking (Immediate) | 101 |
| ECG diagnosis (Two-stage) | 57 |
| ECG diagnosis + marking (Two-stage) | 65 |
Research Design and Limitations
This preregistered experimental study employed a within-subjects design with 58 physicians. While ensuring control (e.g., randomization of ECGs and conditions) and blinding for scoring, the study acknowledged limitations such as always correct AI support (not reflective of real-world imperfect AI), the use of consumer-oriented need scales with some adaptations, and a sample primarily composed of early-career clinicians. These factors inform future research directions in explainable AI for healthcare.
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