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.
Competency-based education
Deep learning
Healthcare professional skills
Healthcare skills databases
Nursing skills
Pose estimation
Journal
Advances in health sciences education : theory and practice
ISSN: 1573-1677
Titre abrégé: Adv Health Sci Educ Theory Pract
Pays: Netherlands
ID NLM: 9612021
Informations de publication
Date de publication:
09 Sep 2024
09 Sep 2024
Historique:
received:
28
02
2024
accepted:
26
08
2024
medline:
9
9
2024
pubmed:
9
9
2024
entrez:
9
9
2024
Statut:
aheadofprint
Résumé
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.
Identifiants
pubmed: 39249618
doi: 10.1007/s10459-024-10369-5
pii: 10.1007/s10459-024-10369-5
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Informations de copyright
© 2024. Crown.
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