Big Data Analytics and Processing Platform in Czech Republic Healthcare

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aut.relation.issue5en_NZ
aut.relation.journalApplied Sciences (Switzerland)en_NZ
aut.relation.volume10en_NZ
aut.researcherHutcheson, Catherine
dc.contributor.authorŠtufi, Men_NZ
dc.contributor.authorBačić, Ben_NZ
dc.contributor.authorStoimenov, Len_NZ
dc.date.accessioned2021-09-07T00:03:12Z
dc.date.available2021-09-07T00:03:12Z
dc.date.copyright2020en_NZ
dc.date.issued2020en_NZ
dc.description.abstractBig data analytics (BDA) in healthcare has made a positive difference in the integration of Artificial Intelligence (AI) in advancements of analytical capabilities, while lowering the costs of medical care. The aim of this study is to improve the existing healthcare eSystem by implementing a Big Data Analytics (BDA) platform and to meet the requirements of the Czech Republic National Health Service (Tender-Id. VZ0036628, No. Z2017-035520). In addition to providing analytical capabilities on Linux platforms supporting current and near-future AI with machine-learning and data-mining algorithms, there is the need for ethical considerations mandating new ways to preserve privacy, all of which are preconditioned by the growing body of regulations and expectations. The presented BDA platform, has met all requirements (N > 100), including the healthcare industry-standard Transaction Processing Performance Council (TPC-H) decision support benchmark in compliance with the European Union (EU) and the Czech Republic legislations. Currently, the presented Proof of Concept (PoC) that has been upgraded to a production environment has unified isolated parts of Czech Republic healthcare over the past seven months. The reported PoC BDA platform, artefacts, and concepts are transferrable to healthcare systems in other countries interested in developing or upgrading their own national healthcare infrastructure in a cost-effective, secure, scalable and high-performance manner.en_NZ
dc.identifier.citationApplied Sciences, 10(5), 1705. doi:10.3390/app10051705
dc.identifier.doi10.3390/app10051705en_NZ
dc.identifier.issn2076-3417en_NZ
dc.identifier.urihttps://hdl.handle.net/10292/14475
dc.publisherMDPI
dc.relation.urihttps://www.mdpi.com/2076-3417/10/5/1705en_NZ
dc.rights© 2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/)
dc.rights.accessrightsOpenAccessen_NZ
dc.subjectTPC-H; NoSQL database cluster; Vertica; Real-time epidemic mapping; Real-time pandemic tracking and integration; Outbreak spread and risks data visualisation
dc.titleBig Data Analytics and Processing Platform in Czech Republic Healthcareen_NZ
dc.typeJournal Article
pubs.elements-id373610
pubs.organisational-data/AUT
pubs.organisational-data/AUT/Faculty of Design & Creative Technologies
pubs.organisational-data/AUT/Faculty of Design & Creative Technologies/School of Engineering, Computer & Mathematical Sciences
pubs.organisational-data/AUT/Faculty of Design & Creative Technologies/School of Engineering, Computer & Mathematical Sciences/Centre for Robotics & Vision
pubs.organisational-data/AUT/Faculty of Health & Environmental Science
pubs.organisational-data/AUT/Faculty of Health & Environmental Science/School of Sport & Recreation
pubs.organisational-data/AUT/Faculty of Health & Environmental Science/School of Sport & Recreation/Sports Performance Research Institute New Zealand
pubs.organisational-data/AUT/Faculty of Health & Environmental Science/School of Sport & Recreation/Sports Performance Research Institute New Zealand/Sports Kinesiology Injury Prevention & Performance Research Group
pubs.organisational-data/AUT/PBRF
pubs.organisational-data/AUT/PBRF/PBRF Design and Creative Technologies
pubs.organisational-data/AUT/PBRF/PBRF Design and Creative Technologies/PBRF ECMS
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