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

Jonathan Currie; Raymond Bond; P. J. McCullagh; Pauline Black; Dewar Finlay; Aaron Peace

doi:10.1109/THMS.2017.2754880

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

Jonathan Currie, Raymond Bond, P. J. McCullagh, Pauline Black, Dewar Finlay, Aaron Peace

Research output: Contribution to journal › Article › peer-review

24 Citations (Scopus)

581 Downloads (Pure)

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=

Original language	English
Pages (from-to)	113-124
Journal	IEEE Transactions on Human-Machine Systems
Volume	48
Issue number	2
Early online date	4 Oct 2017
DOIs	https://doi.org/10.1109/THMS.2017.2754880
Publication status	Published (in print/issue) - 30 Apr 2018

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

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

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10.1109/THMS.2017.2754880

Accepted author manuscriptAuthor Accepted Manuscript, 1.02 MB

https://pure.ulster.ac.uk/en/publications/eye-tracking-the-visual-attention-of-nurses-interpreting-simulate-2

Cite this

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

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 {\textquoteleft}thinking-aloud{\textquoteright}. The participant{\textquoteright}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=",

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",

author = "Jonathan Currie and Raymond Bond and McCullagh, {P. J.} and Pauline Black and Dewar Finlay and Aaron Peace",

year = "2018",

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AU - Currie, Jonathan

AU - Bond, Raymond

AU - McCullagh, P. J.

AU - Black, Pauline

AU - Finlay, Dewar

AU - Peace, Aaron

PY - 2018/4/30

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N2 - 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=

AB - 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=

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KW - eye gaze analytics

KW - simulation based training in healthcare

KW - human computer interaction

KW - HCI

KW - health informatics

KW - sensor data

KW - regression

KW - vital signs

KW - monitoring

KW - bedside

KW - nursing

KW - intensive care unit

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JF - IEEE Transactions on Human-Machine Systems

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ER -

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

Abstract

Keywords

UN SDGs

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Pauline Black

Raymond Bond

Dewar Finlay

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

Abstract

Keywords

UN SDGs

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Other files and links

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Profiles

Pauline Black

Raymond Bond

Dewar Finlay

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