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A novel blockchain-based architectural modal for healthcare data integrity: Covid19 screening laboratory use-case

In this paper, we are proposing a blockchain-based architectural model to ensure the integrity of healthcare-sensitive data in an AI-based medical research context. In our approach, we will use the HL7 FHIR standardized data structure to ensure the interoperability of our approach with the existing...

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Autores principales: BARBARIA, Sabri, MAHJOUBI, Halima, RAHMOUNI, Hanene BOUSSI
Formato: Online Artículo Texto
Lenguaje:English
Publicado: The Author(s). Published by Elsevier B.V. 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10030190/
https://www.ncbi.nlm.nih.gov/pubmed/36968666
http://dx.doi.org/10.1016/j.procs.2023.01.433
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author BARBARIA, Sabri
MAHJOUBI, Halima
RAHMOUNI, Hanene BOUSSI
author_facet BARBARIA, Sabri
MAHJOUBI, Halima
RAHMOUNI, Hanene BOUSSI
author_sort BARBARIA, Sabri
collection PubMed
description In this paper, we are proposing a blockchain-based architectural model to ensure the integrity of healthcare-sensitive data in an AI-based medical research context. In our approach, we will use the HL7 FHIR standardized data structure to ensure the interoperability of our approach with the existing hospital information systems (HIS). Indeed, structuring the data coming from several heterogeneous sources would enhance its quality. In addition, a standardised data structure would help establish a more accurate security and data protection model throughout the process of data collection cleansing and processing. Hence, we designed our architecture to be interoperable with all FHIR-based HISs to add a trust layer to the current medical research process. In this paper we are to achieve our goal, we will combine continua healthcare IoT architecture and Hyperledger fabric architecture. Our trust layer model is composed of four components: (1) an architecture that integrates with the HL7 FHIR data exchange framework, which extends an open protocol that supports efficient standards-based healthcare data exchange (2) a blockchain layer to support access control and auditing of FHIR health records that are stored in the data exchange network databases; (3) a distributed architecture consisting of multiple trusted nodes ensure privacy protection for health data; and (4) an application programming interface (API) will be available for use by the network.
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spelling pubmed-100301902023-03-22 A novel blockchain-based architectural modal for healthcare data integrity: Covid19 screening laboratory use-case BARBARIA, Sabri MAHJOUBI, Halima RAHMOUNI, Hanene BOUSSI Procedia Comput Sci Article In this paper, we are proposing a blockchain-based architectural model to ensure the integrity of healthcare-sensitive data in an AI-based medical research context. In our approach, we will use the HL7 FHIR standardized data structure to ensure the interoperability of our approach with the existing hospital information systems (HIS). Indeed, structuring the data coming from several heterogeneous sources would enhance its quality. In addition, a standardised data structure would help establish a more accurate security and data protection model throughout the process of data collection cleansing and processing. Hence, we designed our architecture to be interoperable with all FHIR-based HISs to add a trust layer to the current medical research process. In this paper we are to achieve our goal, we will combine continua healthcare IoT architecture and Hyperledger fabric architecture. Our trust layer model is composed of four components: (1) an architecture that integrates with the HL7 FHIR data exchange framework, which extends an open protocol that supports efficient standards-based healthcare data exchange (2) a blockchain layer to support access control and auditing of FHIR health records that are stored in the data exchange network databases; (3) a distributed architecture consisting of multiple trusted nodes ensure privacy protection for health data; and (4) an application programming interface (API) will be available for use by the network. The Author(s). Published by Elsevier B.V. 2023 2023-03-22 /pmc/articles/PMC10030190/ /pubmed/36968666 http://dx.doi.org/10.1016/j.procs.2023.01.433 Text en © 2023 The Author(s). Published by Elsevier B.V. Since January 2020 Elsevier has created a COVID-19 resource centre with free information in English and Mandarin on the novel coronavirus COVID-19. The COVID-19 resource centre is hosted on Elsevier Connect, the company's public news and information website. Elsevier hereby grants permission to make all its COVID-19-related research that is available on the COVID-19 resource centre - including this research content - immediately available in PubMed Central and other publicly funded repositories, such as the WHO COVID database with rights for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for free by Elsevier for as long as the COVID-19 resource centre remains active.
spellingShingle Article
BARBARIA, Sabri
MAHJOUBI, Halima
RAHMOUNI, Hanene BOUSSI
A novel blockchain-based architectural modal for healthcare data integrity: Covid19 screening laboratory use-case
title A novel blockchain-based architectural modal for healthcare data integrity: Covid19 screening laboratory use-case
title_full A novel blockchain-based architectural modal for healthcare data integrity: Covid19 screening laboratory use-case
title_fullStr A novel blockchain-based architectural modal for healthcare data integrity: Covid19 screening laboratory use-case
title_full_unstemmed A novel blockchain-based architectural modal for healthcare data integrity: Covid19 screening laboratory use-case
title_short A novel blockchain-based architectural modal for healthcare data integrity: Covid19 screening laboratory use-case
title_sort novel blockchain-based architectural modal for healthcare data integrity: covid19 screening laboratory use-case
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10030190/
https://www.ncbi.nlm.nih.gov/pubmed/36968666
http://dx.doi.org/10.1016/j.procs.2023.01.433
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