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An Eye-Tracking Assessment of Coronary Care Nurses during the Interpretation of Patient Monitoring Scenarios

Currie, Jonathan, Bond, Raymond, McCullagh, P. J., Black, Pauline, Finlay, Dewar and Peace, Aaron (2017) An Eye-Tracking Assessment of Coronary Care Nurses during the Interpretation of Patient Monitoring Scenarios. In: Computing in Cardiology, Vancouver. IEEE. Vol 43 4 pp. [Conference contribution]

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URL: http://ieeexplore.ieee.org/document/7868690/

DOI: 10.22489/CinC.2016.105-108

Abstract

Introduction: It has yet to be determined whether visual attention, measured via eye tracking metrics (ETMs) can be indicative of performance level in coronary care nursing when interpreting patient vitals. Methods: This study captures the visual attention of nurses when interpreting five scenarios using simulated text and vital signs. Baseline performance was marked using detailed criteria and scored 0-10. Self-rated confidence from 1-10 was also collected for each scenario. Cognitive workload was assessed by measuring a participant’s heart rate and post-performance NASA-TLX responses. Eleven coronary care nurses were recruited providing 55 interpretations/observations in total. 45 of which, post data quality filtering, were used to analyse ETMs. Results: Mean performance score = 6.86±1.50 and mean confidence rating = 7.51±1.2. A subset of ETMs significantly correlate with performance across all scenarios. Individual scenarios also provide significant correlations. Three of six regression models were statistically significant with R2 ≥ 0.5. Conclusion: Correlations between specific ETMs and performance have been found across all scenarios and for individual scenarios. Further work is needed to confirm the benefit of ETM in assessing simulation-based training performance.

Item Type:Conference contribution (Paper)
Keywords:Eye tracking, clinical decision making, health informatics, medical informatics, simulation-based training
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:37175
Deposited By: Dr Raymond Bond
Deposited On:16 Mar 2017 14:26
Last Modified:17 Oct 2017 16:28

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