An Effective Approach for Extending Medical Data to the Cloud Through Synthetic Data Generation for Educational Environments.
Cloud evaluation
Cloud first
Data generation
Digital transformation
Education
Healthcare
Information governance
Medical data
Privacy
Security
Simulation
Synthetic data
United Kingdom
Wales
Journal
Studies in health technology and informatics
ISSN: 1879-8365
Titre abrégé: Stud Health Technol Inform
Pays: Netherlands
ID NLM: 9214582
Informations de publication
Date de publication:
31 Aug 2022
31 Aug 2022
Historique:
entrez:
8
9
2022
pubmed:
9
9
2022
medline:
11
9
2022
Statut:
ppublish
Résumé
When taking advantage of technology, healthcare is often met with considerably more barriers to entry than business. Cloud platforms can offer great benefits such as scalability, reduced cost and the ability to effortlessly collaborate across services, and indeed, across the world [6] yet healthcare has been slow to take advantage of these gains. This paper explores the challenges faced by healthcare, how using synthetic data can avoid the initial information governance barriers, provide the experience to effectively evaluate cloud platforms, enable effective research collaboration with education and industry, and support the digital transformation journey.
Identifiants
pubmed: 36073474
pii: SHTI220925
doi: 10.3233/SHTI220925
doi:
Types de publication
Journal Article
Langues
eng