Assessing Pediatric Life Support Skills Using Augmented Reality Medical Simulation With Eye Tracking: A Pilot Study.

J Educ Perioper Med

The following authors are at Stanford School of Medicine, Stanford, CA: is a Medical Student; is a medical student. The following authors are in the Department of Anesthesiology, Perioperative and Pain Medicine, Division of Pediatric Anesthesiology, at Stanford University School of Medicine, Stanford, CA: is a Clinical Assistant Professor; is a Clinical Associate Professor; is a Clinical Associate Professor; is a Statistician; is a Clinical Professor. is a Research Assistant in the Stanford Chariot Program at Stanford School of Medicine, Stanford, CA and at Lucile Packard Children's Hospital Stanford, Palo Alto, CA.

Published: July 2022

Background: Augmented reality (AR) and eye tracking are promising adjuncts for medical simulation, but they have remained distinct tools. The recently developed Chariot Augmented Reality Medical (CHARM) Simulator combines AR medical simulation with eye tracking. We present a novel approach to applying eye tracking within an AR simulation to assess anesthesiologists during an AR pediatric life support simulation. The primary aim was to explore clinician performance in the simulation. Secondary outcomes explored eye tracking as a measure of shockable rhythm recognition and participant satisfaction.

Methods: Anesthesiology residents, pediatric anesthesiology fellows, and attending pediatric anesthesiologists were recruited. Using CHARM, they participated in a pediatric crisis simulation. Performance was scored using the Anesthesia-centric Pediatric Advanced Life Support (A-PALS) scoring instrument, and eye tracking data were analyzed. The Simulation Design Scale measured participant satisfaction.

Results: Nine each of residents, fellows, and attendings participated for a total of 27. We were able to successfully progress participants through the AR simulation as demonstrated by typical A-PALS performance scores. We observed no differences in performance across training levels. Eye tracking data successfully allowed comparisons of time to rhythm recognition across training levels, revealing no differences. Finally, simulation satisfaction was high across all participants.

Conclusions: While the agreement between A-PALS score and gaze patterns is promising, further research is needed to fully demonstrate the use of AR eye tracking for medical training and assessment. Physicians of multiple training levels were satisfied with the technology.

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Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC9583759PMC
http://dx.doi.org/10.46374/volxxiv_issue3_qianDOI Listing

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