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

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

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

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.
LanguageEnglish
Title of host publicationUnknown Host Publication
Pages105-108
Number of pages4
Volume43
Publication statusE-pub ahead of print - 2 Mar 2017
EventComputing in Cardiology - Vancouver
Duration: 2 Mar 2017 → …

Conference

ConferenceComputing in Cardiology
Period2/03/17 → …

Fingerprint

Physiologic Monitoring
Nurses
Cardiovascular Nursing
United States National Aeronautics and Space Administration
Vital Signs
Workload
Heart Rate

Keywords

  • Eye tracking
  • clinical decision making
  • health informatics
  • medical informatics
  • simulation-based training

Cite this

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title = "An Eye-Tracking Assessment of Coronary Care Nurses during the Interpretation of Patient Monitoring Scenarios",
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.",
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Currie, J, Bond, R, McCullagh, PJ, Black, P, Finlay, D & Peace, A 2017, An Eye-Tracking Assessment of Coronary Care Nurses during the Interpretation of Patient Monitoring Scenarios. in Unknown Host Publication. vol. 43, pp. 105-108, Computing in Cardiology, 2/03/17.

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; Peace, Aaron.

Unknown Host Publication. Vol. 43 2017. p. 105-108.

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

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