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Utilizing Eye Tracking to Assess Medical Student Non-Technical Performance During Scenario-Based Simulation: Results of a Pilot Study

Overview
Publisher Springer
Specialty Medical Education
Date 2024 Feb 28
PMID 38414559
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Abstract

Background: Non-technical skills (NTS) are essential for safe surgical patient management. However, assessing NTS involves observer-based ratings, which can introduce bias. Eye tracking (ET) has been proposed as an effective method to capture NTS. The purpose of the current study was to determine if ET metrics are associated with NTS performance.

Methods: Participants wore a mobile ET system and participated in two patient care simulations, where they managed a deteriorating patient. The scenarios featured several challenges to leadership, which were evaluated using a 4-point Likert scale. NTS were evaluated by trained raters using the Non-Technical Skills for Surgeons (NOTSS) scale. ET metrics included percentage of fixations and visits on areas of interest.

Results: Ten medical students participated. Average visit duration on the patient was negatively correlated with participants' communication and leadership. Average visit duration on the patient's intravenous access was negatively correlated with participants' decision making and situation awareness.

Conclusions: Our preliminary data suggests that visual attention on the patient was negatively associated with NTS and may indicate poor comprehension of the patient's status due to heightened cognitive load. In future work, researchers and educators should consider using ET to objectively evaluate and provide feedback on their NTS.

Citing Articles

Nontechnical Skills Assessment in Acute Care Trauma Simulations: A Mixed Methods Approach Using Eye Tracking and Behavioral Marker Systems.

Anton N, Obuseh M, Lim C, Chen H, Yang J, Stefanidis D Mil Med. 2024; 189(Suppl 3):719-727.

PMID: 39160814 PMC: 11368209. DOI: 10.1093/milmed/usae240.


Eye tracking and audio sensors to evaluate surgeon's non-technical skills: An empirical study.

Narasimha S, Obuseh M, Anton N, Chen H, Chakrabarty R, Stefanidis D Appl Ergon. 2024; 119:104320.

PMID: 38797012 PMC: 11863474. DOI: 10.1016/j.apergo.2024.104320.

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