Using Eye-Tracking Technology to Capture the Visual Attention of Nurses During Interpretation of Patient Monitoring Scenarios from a Computer Simulated Bedside Monitor

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

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Introduction:This study analysed the utility of eye tracking technology for gaining insight into the decision making processes of nurses during their interpretation of patient scenarios and vital signs.Methods:Five patient monitoring scenarios (vignette, vital signs [ECG, BP etc.] and scoring criteria) were designed and validated by critical care experts. Participants were asked to interpret these scenarios whilst ‘thinking aloud’. Visual attention was measured using infrared light- based eye-tracking technology. Each interpretation was scored out of 10. Subjects comprised of students (n=36) and qualified nurses (n=11). Scores and self-rated confidence (where 1=low, 10=high) are presented using mean±SD. Significance testing was performed using a t-test and ANOVA where appropriate (α = 0.05). Multivariate regression was performed to determine if a machine could use eye gaze features to accurately predict competency (dependent variable=score). Independent eye gaze only variables were used in the regression models if they statistically significantly (p
LanguageEnglish
Title of host publicationUnknown Host Publication
Number of pages1
Publication statusPublished - 13 Apr 2016
EventInternational Society for Computerized Electrocardiology - Arizona
Duration: 13 Apr 2016 → …

Conference

ConferenceInternational Society for Computerized Electrocardiology
Period13/04/16 → …

Fingerprint

Physiologic Monitoring
Nurses
Technology
Vital Signs
Critical Care
Decision Making
Analysis of Variance
Electrocardiography
Students
Light

Keywords

  • Eye tracking
  • healthcare training
  • simulation based training
  • competency
  • patient safety
  • clinical decision making
  • bedside monitoring
  • vital signs

Cite this

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title = "Using Eye-Tracking Technology to Capture the Visual Attention of Nurses During Interpretation of Patient Monitoring Scenarios from a Computer Simulated Bedside Monitor",
abstract = "Introduction:This study analysed the utility of eye tracking technology for gaining insight into the decision making processes of nurses during their interpretation of patient scenarios and vital signs.Methods:Five patient monitoring scenarios (vignette, vital signs [ECG, BP etc.] and scoring criteria) were designed and validated by critical care experts. Participants were asked to interpret these scenarios whilst ‘thinking aloud’. Visual attention was measured using infrared light- based eye-tracking technology. Each interpretation was scored out of 10. Subjects comprised of students (n=36) and qualified nurses (n=11). Scores and self-rated confidence (where 1=low, 10=high) are presented using mean±SD. Significance testing was performed using a t-test and ANOVA where appropriate (α = 0.05). Multivariate regression was performed to determine if a machine could use eye gaze features to accurately predict competency (dependent variable=score). Independent eye gaze only variables were used in the regression models if they statistically significantly (p",
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AU - Currie, Jonathan

AU - Bond, Raymond R

AU - McCullagh, P. J.

AU - Black, Pauline

AU - Finlay, Dewar

AU - Peace, Aaron

PY - 2016/4/13

Y1 - 2016/4/13

N2 - Introduction:This study analysed the utility of eye tracking technology for gaining insight into the decision making processes of nurses during their interpretation of patient scenarios and vital signs.Methods:Five patient monitoring scenarios (vignette, vital signs [ECG, BP etc.] and scoring criteria) were designed and validated by critical care experts. Participants were asked to interpret these scenarios whilst ‘thinking aloud’. Visual attention was measured using infrared light- based eye-tracking technology. Each interpretation was scored out of 10. Subjects comprised of students (n=36) and qualified nurses (n=11). Scores and self-rated confidence (where 1=low, 10=high) are presented using mean±SD. Significance testing was performed using a t-test and ANOVA where appropriate (α = 0.05). Multivariate regression was performed to determine if a machine could use eye gaze features to accurately predict competency (dependent variable=score). Independent eye gaze only variables were used in the regression models if they statistically significantly (p

AB - Introduction:This study analysed the utility of eye tracking technology for gaining insight into the decision making processes of nurses during their interpretation of patient scenarios and vital signs.Methods:Five patient monitoring scenarios (vignette, vital signs [ECG, BP etc.] and scoring criteria) were designed and validated by critical care experts. Participants were asked to interpret these scenarios whilst ‘thinking aloud’. Visual attention was measured using infrared light- based eye-tracking technology. Each interpretation was scored out of 10. Subjects comprised of students (n=36) and qualified nurses (n=11). Scores and self-rated confidence (where 1=low, 10=high) are presented using mean±SD. Significance testing was performed using a t-test and ANOVA where appropriate (α = 0.05). Multivariate regression was performed to determine if a machine could use eye gaze features to accurately predict competency (dependent variable=score). Independent eye gaze only variables were used in the regression models if they statistically significantly (p

KW - Eye tracking

KW - healthcare training

KW - simulation based training

KW - competency

KW - patient safety

KW - clinical decision making

KW - bedside monitoring

KW - vital signs

M3 - Conference contribution

BT - Unknown Host Publication

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