Attentional Capacity and Clinical Performance: Eye Tracking Cardiologists Performing Simulated Coronary Angiography

Jonathan Currie, Raymond R Bond, P. J. McCullagh, Pauline Black, Dewar Finlay, Stephen Gallagher, Anthony Gallagher, Peter Kearney

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

Abstract

Simulation-based training is driven by patient safety and Kohn’s 2000 report ‘To Err is Human’ revealing up to 96,000 patients die every year (USA) due to medical error. Computer-based simulation has been proven to produce a superior skill set with less errors and better transfer of training in general surgeons. Eye tracking features recently have shown to discriminate between novices and experts in surgical settings. An aspect of performance yet to be analysed is attentional capacity (AC) and the corresponding visual attention (VA) from eye tracking. A PhD level study has been designed to capture visual attention during attempts of simulated coronary angiography while AC is tested. The Initial pilot study will recruit eight registrars and consultants. We hypothesise that VA is linked with AC and that expert surgeons will demonstrate higher capacity when tested.The recording will take place in the ASSERT Centre, University College Cork, using a high-fidelity simulation suite. Participants perform a coronary angiography case twice alongside an additional task to measure AC. The task requires checking a supplementary display monitor and responding to playing cards when they appear. This added task acts as a measure of their AC. Primary outcomes will involve statistical analysis performed to determine the relationship between (1) AC and surgical performance, (2) VA and AC. If found that predictive metrics exist for good/bad performances at surgical tasks, that will have implications for research areas of Applied Computing, Human Factors and Human Computer Interaction with interventional cardiology. Wearable technology creates the opportunity for cost-effective assessment that provides insight to the trainee psychophysiology. This could predict task performance, including errors, uncertainty and more. This combined with machine learning algorithms could produce accurate computer automated assessment in training.
LanguageEnglish
Title of host publicationUnknown Host Publication
Number of pages1
Publication statusAccepted/In press - 5 Oct 2016
EventIrish Human Computer Interaction Conference - Cork
Duration: 5 Oct 2016 → …

Conference

ConferenceIrish Human Computer Interaction Conference
Period5/10/16 → …

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Angiography
Psychophysiology
Cardiology
Human computer interaction
Human engineering
Learning algorithms
Learning systems
Statistical methods
Display devices
Costs

Keywords

  • Cardiology
  • human computer interaction
  • HCI
  • cognitive psychology
  • wearable computing
  • psychophysiology
  • attentional capacity
  • catheterisation
  • Cath Lab
  • interventional cardiology
  • eye Tracking
  • simulation

Cite this

@inproceedings{babba9bc669a4deeac5d2f2bf3be468e,
title = "Attentional Capacity and Clinical Performance: Eye Tracking Cardiologists Performing Simulated Coronary Angiography",
abstract = "Simulation-based training is driven by patient safety and Kohn’s 2000 report ‘To Err is Human’ revealing up to 96,000 patients die every year (USA) due to medical error. Computer-based simulation has been proven to produce a superior skill set with less errors and better transfer of training in general surgeons. Eye tracking features recently have shown to discriminate between novices and experts in surgical settings. An aspect of performance yet to be analysed is attentional capacity (AC) and the corresponding visual attention (VA) from eye tracking. A PhD level study has been designed to capture visual attention during attempts of simulated coronary angiography while AC is tested. The Initial pilot study will recruit eight registrars and consultants. We hypothesise that VA is linked with AC and that expert surgeons will demonstrate higher capacity when tested.The recording will take place in the ASSERT Centre, University College Cork, using a high-fidelity simulation suite. Participants perform a coronary angiography case twice alongside an additional task to measure AC. The task requires checking a supplementary display monitor and responding to playing cards when they appear. This added task acts as a measure of their AC. Primary outcomes will involve statistical analysis performed to determine the relationship between (1) AC and surgical performance, (2) VA and AC. If found that predictive metrics exist for good/bad performances at surgical tasks, that will have implications for research areas of Applied Computing, Human Factors and Human Computer Interaction with interventional cardiology. Wearable technology creates the opportunity for cost-effective assessment that provides insight to the trainee psychophysiology. This could predict task performance, including errors, uncertainty and more. This combined with machine learning algorithms could produce accurate computer automated assessment in training.",
keywords = "Cardiology, human computer interaction, HCI, cognitive psychology, wearable computing, psychophysiology, attentional capacity, catheterisation, Cath Lab, interventional cardiology, eye Tracking, simulation",
author = "Jonathan Currie and Bond, {Raymond R} and McCullagh, {P. J.} and Pauline Black and Dewar Finlay and Stephen Gallagher and Anthony Gallagher and Peter Kearney",
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month = "10",
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booktitle = "Unknown Host Publication",

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Currie, J, Bond, RR, McCullagh, PJ, Black, P, Finlay, D, Gallagher, S, Gallagher, A & Kearney, P 2016, Attentional Capacity and Clinical Performance: Eye Tracking Cardiologists Performing Simulated Coronary Angiography. in Unknown Host Publication. Irish Human Computer Interaction Conference, 5/10/16.

Attentional Capacity and Clinical Performance: Eye Tracking Cardiologists Performing Simulated Coronary Angiography. / Currie, Jonathan; Bond, Raymond R; McCullagh, P. J.; Black, Pauline; Finlay, Dewar; Gallagher, Stephen; Gallagher, Anthony; Kearney, Peter.

Unknown Host Publication. 2016.

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

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AU - Bond, Raymond R

AU - McCullagh, P. J.

AU - Black, Pauline

AU - Finlay, Dewar

AU - Gallagher, Stephen

AU - Gallagher, Anthony

AU - Kearney, Peter

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N2 - Simulation-based training is driven by patient safety and Kohn’s 2000 report ‘To Err is Human’ revealing up to 96,000 patients die every year (USA) due to medical error. Computer-based simulation has been proven to produce a superior skill set with less errors and better transfer of training in general surgeons. Eye tracking features recently have shown to discriminate between novices and experts in surgical settings. An aspect of performance yet to be analysed is attentional capacity (AC) and the corresponding visual attention (VA) from eye tracking. A PhD level study has been designed to capture visual attention during attempts of simulated coronary angiography while AC is tested. The Initial pilot study will recruit eight registrars and consultants. We hypothesise that VA is linked with AC and that expert surgeons will demonstrate higher capacity when tested.The recording will take place in the ASSERT Centre, University College Cork, using a high-fidelity simulation suite. Participants perform a coronary angiography case twice alongside an additional task to measure AC. The task requires checking a supplementary display monitor and responding to playing cards when they appear. This added task acts as a measure of their AC. Primary outcomes will involve statistical analysis performed to determine the relationship between (1) AC and surgical performance, (2) VA and AC. If found that predictive metrics exist for good/bad performances at surgical tasks, that will have implications for research areas of Applied Computing, Human Factors and Human Computer Interaction with interventional cardiology. Wearable technology creates the opportunity for cost-effective assessment that provides insight to the trainee psychophysiology. This could predict task performance, including errors, uncertainty and more. This combined with machine learning algorithms could produce accurate computer automated assessment in training.

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