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
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

Pagination

147-151

Auteurs

Alan Boyce (A)

National Data Resource (NDR), Digital Health and Care Wales (DHCW), Wales, United Kingdom.

Michael Dacey (M)

School of Computing, University of Wales Trinity Saint David.

Tim Bashford (T)

School of Computing, University of Wales Trinity Saint David.

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Classifications MeSH