L McLaughlin1, N Woznitza2, A Cairns3, S L McFadden4, R Bond5, C M Hughes6, A Elsayed7, D Finlay8, J McConnell9. 1. Centre for Health and Rehabilitation Technologies, Institute of Nursing and Health, School of Health Sciences, Ulster University, Northern Ireland, UK. Electronic address: mclaughlin-l16@ulster.ac.uk. 2. Radiology Department, Homerton University Hopsital; School of Allied Health Professions, Canterbury Christ Church University, England, UK. Electronic address: nicholas.woznitza@nhs.net. 3. Computer Science Research Institute, School of Computing and Mathematics, Ulster University, Northern Ireland, UK. Electronic address: cairns-a3@ulster.ac.uk. 4. Centre for Health and Rehabilitation Technologies, Institute of Nursing and Health, School of Health Sciences, Ulster University, Northern Ireland, UK. Electronic address: s.mcfadden@ulster.ac.uk. 5. Computer Science Research Institute, School of Computing and Mathematics, Ulster University, Northern Ireland, UK. Electronic address: rb.bond@ulster.ac.uk. 6. Centre for Health and Rehabilitation Technologies, Institute of Nursing and Health, School of Health Sciences, Ulster University, Northern Ireland, UK. Electronic address: cm.hughes@ulster.ac.uk. 7. Queen Elizabeth University Hospital, NHS Greater Glasgow and Clyde, Scotland, UK. Electronic address: ayman.elsayed@ggc.scot.nhs.uk. 8. Radiology Department, Homerton University Hopsital; School of Allied Health Professions, Canterbury Christ Church University, England, UK. Electronic address: d.finaly@ulster.ac.uk. 9. Queen Elizabeth University Hospital, NHS Greater Glasgow and Clyde, Scotland, UK. Electronic address: jonathan.mcconnell@ggc.scot.nhs.uk.
Abstract
INTRODUCTION: Time delays and errors exist which lead to delays in patient care and misdiagnosis. Reporting clinicians follow guidance to form their own search strategy. However, little research has tested these training guides. With the use of eye tracking technology and expert input we developed a digital training platform to be used in chest image interpretation learning. METHODS: Two sections of a digital training platform were planned and developed; A) a search strategy training tool to assist reporters during their interpretation of images, and B) an educational tool to communicate the search strategies of expert viewers to trainees by using eye tracking technology. RESULTS: A digital training platform for use in chest image interpretation was created based on evidence within the literature, expert input and two search strategies previously used in clinical practice. Images and diagrams, aiding translation of the platform content, were incorporated where possible. The platform is structured to allow the chest image interpretation process to be clear, concise and methodical. CONCLUSION: A search strategy was incorporated within the tool to investigate its use, with the possibility that it could be recommended as an evidence based approach for use by reporting clinicians. Eye tracking, a checklist and voice recordings have been combined to form a multi-dimensional learning tool, which has never been used in chest image interpretation learning before. The training platform for use in chest image interpretation learning has been designed, created and digitised. Future work will establish the efficacy of the developed approaches. Crown
INTRODUCTION: Time delays and errors exist which lead to delays in patient care and misdiagnosis. Reporting clinicians follow guidance to form their own search strategy. However, little research has tested these training guides. With the use of eye tracking technology and expert input we developed a digital training platform to be used in chest image interpretation learning. METHODS: Two sections of a digital training platform were planned and developed; A) a search strategy training tool to assist reporters during their interpretation of images, and B) an educational tool to communicate the search strategies of expert viewers to trainees by using eye tracking technology. RESULTS: A digital training platform for use in chest image interpretation was created based on evidence within the literature, expert input and two search strategies previously used in clinical practice. Images and diagrams, aiding translation of the platform content, were incorporated where possible. The platform is structured to allow the chest image interpretation process to be clear, concise and methodical. CONCLUSION: A search strategy was incorporated within the tool to investigate its use, with the possibility that it could be recommended as an evidence based approach for use by reporting clinicians. Eye tracking, a checklist and voice recordings have been combined to form a multi-dimensional learning tool, which has never been used in chest image interpretation learning before. The training platform for use in chest image interpretation learning has been designed, created and digitised. Future work will establish the efficacy of the developed approaches. Crown