Abstract

IntroductionUnobtrusive metrics that can auto-assess performance during clinical procedures are of value. Three approaches to deriving wearable technology-based metrics are explored: (1) eye tracking, (2) psychophysiological measurements [e.g. electrodermal activity (EDA)] and (3) arm and hand movement via accelerometry. We also measure attentional capacity by tasking the operator with an additional task to track an unrelated object during the procedure.MethodsTwo aspects of performance are measured: (1) using eye gaze and psychophysiology metrics and (2) measuring attentional capacity via an additional unrelated task (to monitor a visual stimulus/playing cards). The aim was to identify metrics that can be used to automatically discriminate between levels of performance or at least between novices and experts. The study was conducted using two groups: (1) novice operators and (2) expert operators. Both groups made two attempts at a coronary angiography procedure using a full-physics virtual reality simulator. Participants wore eye tracking glasses and an E4 wearable wristband. Areas of interest were defined to track visual attention on display screens, including: (1) X-ray, (2) vital signs, (3) instruments and (4) the stimulus screen (for measuring attentional capacity).ResultsExperts provided greater dwell time (63% vs 42%, p = 0.03) and fixations (50% vs 34%, p = 0.04) on display screens. They also provided greater dwell time (11% vs 5%, p = 0.006) and fixations (9% vs 4%, p = 0.007) when selecting instruments. The experts’ performance for tracking the unrelated object during the visual stimulus task negatively correlated with total errors (r = − 0.95, p = 0.0009). Experts also had a higher standard deviation of EDA (2.52 µS vs 0.89 µS, p = 0.04).ConclusionsEye tracking metrics may help discriminate between a novice and expert operator, by showing that experts maintain greater visual attention on the display screens. In addition, the visual stimulus study shows that an unrelated task can measure attentional capacity.Trial registration This work is registered through clinicaltrials.gov, a service of the U.S. National Health Institute, and is identified by the trial reference: NCT02928796.

Highlights

  • Unobtrusive metrics that can auto-assess performance during clinical procedures are of value

  • This study aims to (1) use wearable technology to determine metrics that could be used to auto-assess operator and procedural performance and (2) to determine whether a visual stimulus task can be used to measure attentional capacity and whether performance of this task is associative to operator errors

  • The study captured a unique dataset with psychophysiological metrics along with a novel measurement of attentional capacity recorded during an important highly skilled clinical procedure

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Summary

Introduction

Unobtrusive metrics that can auto-assess performance during clinical procedures are of value. We measure attentional capacity by tasking the operator with an additional task to track an unrelated object during the procedure. Methods Two aspects of performance are measured: (1) using eye gaze and psychophysiology metrics and (2) measuring attentional capacity via an additional unrelated task (to monitor a visual stimulus/playing cards). Conclusions Eye tracking metrics may help discriminate between a novice and expert operator, by showing that experts maintain greater visual attention on the display screens. The visual stimulus study shows that an unrelated task can measure attentional capacity. Many healthcare tasks and procedures can be simulated using computer technology for training purposes and provide novices with a way to improve or maintain their skills [6,7,8]. A high cognitive load may affect the non-technical leadership skills of the operator in the clinical environment

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