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Eye Tracking the Visual Attention of Nurses Interpreting Simulated Vital Signs Scenarios: Mining Metrics to Discriminate Between Performance Level

Currie, Jonathan, Bond, Raymond, McCullagh, P. J., Black, Pauline, Finlay, Dewar and Peace, Aaron (2018) Eye Tracking the Visual Attention of Nurses Interpreting Simulated Vital Signs Scenarios: Mining Metrics to Discriminate Between Performance Level. IEEE Transactions on Human-Machine Systems, 48 (2). pp. 113-124. [Journal article]

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DOI: 10.1109/THMS.2017.2754880

Abstract

Nurses welcome innovative training and assessment methods to effectively interpret physiological vital signs. The objective is to determine if eye-tracking technology can be used to develop biometrics for automatically predict the performance of nurses whilst they interact with computer-based simulations. 47 nurses were recruited, 36 nursing students (training group) and 11 coronary care nurses (qualified group). Each nurse interpreted five simulated vital signs scenarios whilst ‘thinking-aloud’. The participant’s visual attention (eye tracking metrics), verbalisation, heart rate, confidence level (1-10, 10=most confident) and cognitive load (NASA-TLX) were recorded during performance. Scenario performances were scored out of ten. Analysis was used to find patterns between the eye tracking metrics and performance score. Multiple linear regression was used to predict performance score using eye tracking metrics. The qualified group scored higher than the training group (6.851.5 vs. 4.591.61, p=<0.0001) and reported greater confidence (7.511.2 vs. 5.791.39, p=<0.0001). Regression using a selection of eye tracking metrics was shown to adequately predict score (adjusted R2=0.80, p=<0.0001). This shows that eye tracking alone could predict a nurse’s performance and can provide insight to the performance of a nurse when interpreting bedside monitors.

Item Type:Journal article
Keywords:Eye tracking, eye gaze analytics, simulation based training in healthcare, human computer interaction, HCI, health informatics, sensor data, regression, vital signs, monitoring, bedside, nursing, intensive care unit
Faculties and Schools:Faculty of Computing & Engineering
Faculty of Computing & Engineering > School of Computing and Mathematics
Faculty of Computing & Engineering > School of Engineering
Faculty of Life and Health Sciences > School of Nursing
Faculty of Life and Health Sciences
Research Institutes and Groups:Engineering Research Institute
Engineering Research Institute > Nanotechnology & Integrated BioEngineering Centre (NIBEC)
Computer Science Research Institute > Smart Environments
Computer Science Research Institute
ID Code:38626
Deposited By: Dr Raymond Bond
Deposited On:14 Sep 2017 13:42
Last Modified:16 Mar 2018 12:17

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