Cathal Breen1, Tingting Zhu2, Raymond Bond3, Dewar Finlay4, Gari Clifford5. 1. School of Health Sciences, University of Ulster, Shore Road, Newtownabbey, N. Ireland. Electronic address: cj.breen@ulster.ac.uk. 2. Institute of Biomedical Engineering, Department of Engineering science, University of Oxford, Oxford, UK. 3. School of Computing and Mathematics, University of Ulster, Shore Road, Newtownabbey, N. Ireland. 4. School of Engineering, University of Ulster, Shore Road, Newtownabbey, N. Ireland. 5. Department of Biomedical Informatics, Emory University & Georgia Institute of Technology; Department of Biomedical Engineering, Emory University & Georgia Institute of Technology.
Abstract
INTRODUCTION: The aim of this study is to present and evaluate the integration of a low resource JavaScript based ECG training interface (CrowdLabel) and a standardised curriculum for self-guided tuition in ECG interpretation. METHODS: Participants practiced interpreting ECGs weekly using the CrowdLabel interface to assist with the learning of the traditional didactic taught course material during a 6 week training period. To determine competency students were tested during week 7. RESULTS: A total of 245 unique ECG cases were submitted by each student. Accuracy scores during the training period ranged from 0-59.5% (median = 33.3%). Conversely accuracy scores during the test ranged from 30 - 70% (median = 37.5%) (p < 0.05). There was no correlation between students who interpreted high numbers of ECGs during the training period and their marks obtained. CONCLUSIONS: CrowdLabel is shown to be a readily accessible dedicated learning platform to support ECG interpretation competency.
INTRODUCTION: The aim of this study is to present and evaluate the integration of a low resource JavaScript based ECG training interface (CrowdLabel) and a standardised curriculum for self-guided tuition in ECG interpretation. METHODS:Participants practiced interpreting ECGs weekly using the CrowdLabel interface to assist with the learning of the traditional didactic taught course material during a 6 week training period. To determine competency students were tested during week 7. RESULTS: A total of 245 unique ECG cases were submitted by each student. Accuracy scores during the training period ranged from 0-59.5% (median = 33.3%). Conversely accuracy scores during the test ranged from 30 - 70% (median = 37.5%) (p < 0.05). There was no correlation between students who interpreted high numbers of ECGs during the training period and their marks obtained. CONCLUSIONS: CrowdLabel is shown to be a readily accessible dedicated learning platform to support ECG interpretation competency.