Literature DB >> 25200901

An evaluation of eye tracking technology in the assessment of 12 lead electrocardiography interpretation.

Cathal J Breen1, Raymond Bond2, Dewar Finlay3.   

Abstract

INTRODUCTION: This study investigated eye tracking technology for 12 lead electrocardiography interpretation to Healthcare Scientist students.
METHODS: Participants (n=33) interpreted ten 12 lead ECG recordings and randomized to receive objective individual appraisal on their efforts either by traditional didactic format or by eye tracker software.
RESULTS: One hundred percent of participants reported the experience positively at improving their ECG interpretation competency. ECG analysis time ranged between 13.2 and 59.5s. The rhythm strip was the most common lead studied and fixated on for the longest duration (mean 9.9s). Lead I was studied for the shortest duration (mean 0.25s). Feedback using eye tracking data during ECG interpretation did not produce any significant variation between the assessment marks of the study and the control groups (p=0.32).
CONCLUSIONS: Although the hypothesis of this study was rejected active teaching and early feedback practices are recommended within this discipline.
Copyright © 2014 Elsevier Inc. All rights reserved.

Entities:  

Keywords:  Assessment; ECG; Eye tracking; Healthcare Science; Pedagogy

Mesh:

Year:  2014        PMID: 25200901     DOI: 10.1016/j.jelectrocard.2014.08.008

Source DB:  PubMed          Journal:  J Electrocardiol        ISSN: 0022-0736            Impact factor:   1.438


  6 in total

1.  Using High-Fidelity Simulation and Eye Tracking to Characterize EHR Workflow Patterns among Hospital Physicians.

Authors:  Julie W Doberne; Ze He; Vishnu Mohan; Jeffrey A Gold; Jenna Marquard; Michael F Chiang
Journal:  AMIA Annu Symp Proc       Date:  2015-11-05

2.  TBC: A simple algorithm to rule out abnormalities in electrocardiograms of patients with pacemakers.

Authors:  Javier Higueras; Carmen Olmos; Julián Palacios-Rubio; Juan Carlos Gómez-Polo; Pedro Martínez-Losas; Virginia Ruiz-Pizarro; Ramón Bover; Julián Pérez-Villacastín
Journal:  Cardiol J       Date:  2018-08-29       Impact factor: 2.737

3.  Exploring the Relationship Between Eye Movements and Electrocardiogram Interpretation Accuracy.

Authors:  Alan Davies; Gavin Brown; Markel Vigo; Simon Harper; Laura Horseman; Bruno Splendiani; Elspeth Hill; Caroline Jay
Journal:  Sci Rep       Date:  2016-12-05       Impact factor: 4.379

4.  Wearable technology-based metrics for predicting operator performance during cardiac catheterisation.

Authors:  Jonathan Currie; Raymond R Bond; Paul McCullagh; Pauline Black; Dewar D Finlay; Stephen Gallagher; Peter Kearney; Aaron Peace; Danail Stoyanov; Colin D Bicknell; Stephen Leslie; Anthony G Gallagher
Journal:  Int J Comput Assist Radiol Surg       Date:  2019-02-07       Impact factor: 2.924

5.  Use of Eye-Tracking Technology by Medical Students Taking the Objective Structured Clinical Examination: Descriptive Study.

Authors:  M D Grima-Murcia; Francisco Sanchez-Ferrer; Jose Manuel Ramos-Rincón; Eduardo Fernández
Journal:  J Med Internet Res       Date:  2020-08-21       Impact factor: 5.428

6.  Understanding Cardiology Practitioners' Interpretations of Electrocardiograms: An Eye-Tracking Study.

Authors:  Mohammed Tahri Sqalli; Dena Al-Thani; Mohamed B Elshazly; Mohammed Al-Hijji; Alaa Alahmadi; Yahya Sqalli Houssaini
Journal:  JMIR Hum Factors       Date:  2022-02-09
  6 in total

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