Interpretation of a 12-Lead Electrocardiogram by Medical Students: Quantitative Eye-Tracking Approach
Background
Accurate interpretation of a 12-lead electrocardiogram (ECG) demands high levels of skill and expertise. Early training in medical school plays an important role in building the ECG interpretation skill. Thus, understanding how medical students perform the task of interpretation is important for improving this skill.
Objective
We aimed to use eye tracking as a tool to research how eye fixation can be used to gain a deeper understanding of how medical students interpret ECGs.
Methods
In total, 16 medical students were recruited to interpret 10 different ECGs each. Their eye movements were recorded using an eye tracker. Fixation heatmaps of where the students looked were generated from the collected data set. Statistical analysis was conducted on the fixation count and duration using the Mann-Whitney U test and the Kruskal-Wallis test.
Results
The average percentage of correct interpretations was 55.63%, with an SD of 4.63%. After analyzing the average fixation duration, we found that medical students study the three lower leads (rhythm strips) the most using a top-down approach: lead II (mean=2727 ms, SD=456), followed by leads V1 (mean=1476 ms, SD=320) and V5 (mean=1301 ms, SD=236). We also found that medical students develop a personal system of interpretation that adapts to the nature and complexity of the diagnosis. In addition, we found that medical students consider some leads as their guiding point toward finding a hint leading to the correct interpretation.
Conclusions
The use of eye tracking successfully provides a quantitative explanation of how medical students learn to interpret a 12-lead ECG.
Other Information
Published in: JMIR Medical Education
License: https://creativecommons.org/licenses/by/4.0/
See article on publisher's website: https://dx.doi.org/10.2196/26675
History
Language
- English
Publisher
JMIR PublicationsPublication Year
- 2021
License statement
This Item is licensed under the Creative Commons Attribution 4.0 International LicenseInstitution affiliated with
- Hamad Bin Khalifa University
- College of Science and Engineering - HBKU
- Weill Cornell Medicine - Qatar
- Hamad Medical Corporation