ERIC Number: EJ1484794
Record Type: Journal
Publication Date: 2025-Feb
Pages: 21
Abstractor: As Provided
ISBN: N/A
ISSN: ISSN-1382-4996
EISSN: EISSN-1573-1677
Available Date: 2024-09-09
Advancing Healthcare Practice and Education via Data Sharing: Demonstrating the Utility of Open Data by Training an Artificial Intelligence Model to Assess Cardiopulmonary Resuscitation Skills
Merryn D. Constable1; Francis Xiatian Zhang2; Tony Conner3; Daniel Monk3; Jason Rajsic1; Claire Ford3; Laura Jillian Park3; Alan Platt3; Debra Porteous3; Lawrence Grierson4; Hubert P. H. Shum2
Advances in Health Sciences Education, v30 n1 p15-35 2025
Health professional education stands to gain substantially from collective efforts toward building video databases of skill performances in both real and simulated settings. An accessible resource of videos that demonstrate an array of performances -- both good and bad -- provides an opportunity for interdisciplinary research collaborations that can advance our understanding of movement that reflects technical expertise, support educational tool development, and facilitate assessment practices. In this paper we raise important ethical and legal considerations when building and sharing health professions education data. Collective data sharing may produce new knowledge and tools to support healthcare professional education. We demonstrate the utility of a data-sharing culture by providing and leveraging a database of cardio-pulmonary resuscitation (CPR) performances that vary in quality. The CPR skills performance database (collected for the purpose of this research, hosted at UK Data Service's ReShare Repository) contains videos from 40 participants recorded from 6 different angles, allowing for 3D reconstruction for movement analysis. The video footage is accompanied by quality ratings from 2 experts, participants' self-reported confidence and frequency of performing CPR, and the demographics of the participants. From this data, we present an Automatic Clinical Assessment tool for Basic Life Support that uses pose estimation to determine the spatial location of the participant's movements during CPR and a deep learning network that assesses the performance quality.
Descriptors: Data Use, Artificial Intelligence, First Aid, Ethics, Legal Problems, Allied Health Occupations Education, Video Technology, Foreign Countries
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Publication Type: Journal Articles; Reports - Research
Education Level: N/A
Audience: N/A
Language: English
Sponsor: N/A
Authoring Institution: N/A
Identifiers - Location: United Kingdom
Grant or Contract Numbers: N/A
Author Affiliations: 1Northumbria University, Department of Psychology, Newcastle Upon Tyne, UK; 2Durham University, Department of Computer Science, Durham, UK; 3Northumbria University, Department of Nursing and Midwifery, Newcastle Upon Tyne, UK; 4McMaster University, Department of Family Medicine, Hamilton, Canada

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