Understanding Cardiology Practitioners' Interpretations of Electrocardiograms: An Eye-Tracking Study

Mohammed Tahri Sqalli; Dena Al-Thani; Mohamed B Elshazly; Mohammed Al-Hijji; Alaa Alahmadi; Yahya Sqalli Houssaini

doi:10.2196/34058

Understanding Cardiology Practitioners' Interpretations of Electrocardiograms: An Eye-Tracking Study

JMIR Hum Factors. 2022 Feb 9;9(1):e34058. doi: 10.2196/34058.

Authors

Mohammed Tahri Sqalli^#¹, Dena Al-Thani¹, Mohamed B Elshazly^{2

3}, Mohammed Al-Hijji^{4

5}, Alaa Alahmadi⁶, Yahya Sqalli Houssaini⁷

Affiliations

¹ Information and Computing Technology Division, College of Science and Engineering, Hamad Bin Khalifa University, Doha, Qatar.
² Department of Cardiac Electrophysiology, Cleveland Clinic, Cleveland, OH, United States.
³ Johns Hopkins Ciccarone Center, Heart and Vascular Institute, Johns Hopkins Medicine, Baltimore, MD, United States.
⁴ Interventional & Structural Cardiology Division, Heart Hospital, Hamad Medical Corporation, Doha, Qatar.
⁵ Weill Cornell Medical College, Cornell University, Doha, Qatar.
⁶ College of Computer Science and Engineering, Taibah University, Yanbu, Saudi Arabia.
⁷ Department of Medicine, Faculty of Medecine and Pharmacy, Mohammed V University, Rabat, Morocco.

^# Contributed equally.

PMID: 35138258
PMCID: PMC8867292
DOI: 10.2196/34058

Abstract

Background: Visual expertise refers to advanced visual skills demonstrated when performing domain-specific visual tasks. Prior research has emphasized the fact that medical experts rely on such perceptual pattern-recognition skills when interpreting medical images, particularly in the field of electrocardiogram (ECG) interpretation. Analyzing and modeling cardiology practitioners' visual behavior across different levels of expertise in the health care sector is crucial. Namely, understanding such acquirable visual skills may help train less experienced clinicians to interpret ECGs accurately.

Objective: This study aims to quantify and analyze through the use of eye-tracking technology differences in the visual behavior and methodological practices for different expertise levels of cardiology practitioners such as medical students, cardiology nurses, technicians, fellows, and consultants when interpreting several types of ECGs.

Methods: A total of 63 participants with different levels of clinical expertise took part in an eye-tracking study that consisted of interpreting 10 ECGs with different cardiac abnormalities. A counterbalanced within-subjects design was used with one independent variable consisting of the expertise level of the cardiology practitioners and two dependent variables of eye-tracking metrics (fixations count and fixation revisitations). The eye movements data revealed by specific visual behaviors were analyzed according to the accuracy of interpretation and the frequency with which interpreters visited different parts/leads on a standard 12-lead ECG. In addition, the median and SD in the IQR for the fixations count and the mean and SD for the ECG lead revisitations were calculated.

Results: Accuracy of interpretation ranged between 98% among consultants, 87% among fellows, 70% among technicians, 63% among nurses, and finally 52% among medical students. The results of the eye fixations count, and eye fixation revisitations indicate that the less experienced cardiology practitioners need to interpret several ECG leads more carefully before making any decision. However, more experienced cardiology practitioners rely on their skills to recognize the visual signal patterns of different cardiac abnormalities, providing an accurate ECG interpretation.

Conclusions: The results show that visual expertise for ECG interpretation is linked to the practitioner's role within the health care system and the number of years of practical experience interpreting ECGs. Cardiology practitioners focus on different ECG leads and different waveform abnormalities according to their role in the health care sector and their expertise levels.

Keywords: ECG; ECG interpretation; cardiology; cardiology practitioners; electrocardiogram; eye tracking; human-computer interaction.

©Mohammed Tahri Sqalli, Dena Al-Thani, Mohamed B Elshazly, Mohammed Al-Hijji, Alaa Alahmadi, Yahya Sqalli Houssaini. Originally published in JMIR Human Factors (https://humanfactors.jmir.org), 09.02.2022.