Non-conscious detection of ST-segment elevation during physician ECG interpretation
Gregory J DiGirolamo1, Nikolaos Kakouros2, Federico Sorcini3
1Department of Psychology, College of the Holy Cross, Worcester, MA, United States of America; Department of Radiology, UMass Chan Medical School, Worcester, MA, United States of America.
Physicians missed 18% of ST-elevation myocardial infarctions (STEMIs) on ECGs. Eye-tracking revealed non-conscious detection of STEMIs, suggesting a sophisticated diagnostic mechanism beyond conscious recognition.
Area of Science:
- Cardiology
- Medical Imaging
- Cognitive Neuroscience
Background:
- Electrocardiograms (ECGs) are crucial for diagnosing ST-elevation myocardial infarctions (STEMIs).
- Diagnostic errors in STEMI interpretation can lead to delayed treatment and adverse patient outcomes.
- Understanding the cognitive processes underlying ECG interpretation is essential for improving accuracy.
Purpose of the Study:
- To investigate the role of non-conscious cognitive processes in the detection of STEMIs on ECGs.
- To determine if physicians' eye movements reveal subconscious detection of missed STEMIs.
- To explore the potential of eye-tracking technology in enhancing ECG interpretation.
Main Methods:
- Eight experienced physicians interpreted 90 ECGs (45 STEMI, 45 Normal) without clinical context.
- Eye movements were recorded during ECG interpretation using eye-tracking technology.
- Physicians consciously marked recognized or considered STEMIs.
Main Results:
- Physicians missed 18% of STEMIs.
- Eye-tracking data showed significantly longer viewing durations (P=0.02) and increased transitions (P=0.02) to ECG leads with missed STEMIs compared to normal leads.
- More frequent fixations (P=0.02) were observed on leads with missed STEMIs.
Conclusions:
- Non-conscious cognitive processes contribute to STEMI detection, even when not consciously recognized.
- The findings suggest a holistic, non-conscious mechanism for identifying culprit lesion sites on ECGs.
- Eye-tracking technology holds promise for improving ECG interpretation accuracy and reducing diagnostic errors in clinical practice.
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