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A concentric circles view of health data relations facilitates understanding of sociotechnical challenges for learning health systems and the role of federated data networks

The ability to use clinical and research data at scale is central to hopes for data-driven medicine. However, in using such data researchers often encounter hurdles–both technical, such as differing data security requirements, and social, such as the terms of informed consent, legal requirements and...

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Autores principales: Milne, Richard, Sheehan, Mark, Barnes, Brendan, Kapper, Janek, Lea, Nathan, N'Dow, James, Singh, Gurparkash, Martín-Uranga, Amelia, Hughes, Nigel
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9552575/
https://www.ncbi.nlm.nih.gov/pubmed/36238653
http://dx.doi.org/10.3389/fdata.2022.945739
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author Milne, Richard
Sheehan, Mark
Barnes, Brendan
Kapper, Janek
Lea, Nathan
N'Dow, James
Singh, Gurparkash
Martín-Uranga, Amelia
Hughes, Nigel
author_facet Milne, Richard
Sheehan, Mark
Barnes, Brendan
Kapper, Janek
Lea, Nathan
N'Dow, James
Singh, Gurparkash
Martín-Uranga, Amelia
Hughes, Nigel
author_sort Milne, Richard
collection PubMed
description The ability to use clinical and research data at scale is central to hopes for data-driven medicine. However, in using such data researchers often encounter hurdles–both technical, such as differing data security requirements, and social, such as the terms of informed consent, legal requirements and patient and public trust. Federated or distributed data networks have been proposed and adopted in response to these hurdles. However, to date there has been little consideration of how FDNs respond to both technical and social constraints on data use. In this Perspective we propose an approach to thinking about data in terms that make it easier to navigate the health data space and understand the value of differing approaches to data collection, storage and sharing. We set out a socio-technical model of data systems that we call the “Concentric Circles View” (CCV) of data-relationships. The aim is to enable a consistent understanding of the fit between the local relationships within which data are produced and the extended socio-technical systems that enable their use. The paper suggests this model can help understand and tackle challenges associated with the use of real-world data in the health setting. We use the model to understand not only how but why federated networks may be well placed to address emerging issues and adapt to the evolving needs of health research for patient benefit. We conclude that the CCV provides a useful model with broader application in mapping, understanding, and tackling the major challenges associated with using real world data in the health setting.
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spelling pubmed-95525752022-10-12 A concentric circles view of health data relations facilitates understanding of sociotechnical challenges for learning health systems and the role of federated data networks Milne, Richard Sheehan, Mark Barnes, Brendan Kapper, Janek Lea, Nathan N'Dow, James Singh, Gurparkash Martín-Uranga, Amelia Hughes, Nigel Front Big Data Big Data The ability to use clinical and research data at scale is central to hopes for data-driven medicine. However, in using such data researchers often encounter hurdles–both technical, such as differing data security requirements, and social, such as the terms of informed consent, legal requirements and patient and public trust. Federated or distributed data networks have been proposed and adopted in response to these hurdles. However, to date there has been little consideration of how FDNs respond to both technical and social constraints on data use. In this Perspective we propose an approach to thinking about data in terms that make it easier to navigate the health data space and understand the value of differing approaches to data collection, storage and sharing. We set out a socio-technical model of data systems that we call the “Concentric Circles View” (CCV) of data-relationships. The aim is to enable a consistent understanding of the fit between the local relationships within which data are produced and the extended socio-technical systems that enable their use. The paper suggests this model can help understand and tackle challenges associated with the use of real-world data in the health setting. We use the model to understand not only how but why federated networks may be well placed to address emerging issues and adapt to the evolving needs of health research for patient benefit. We conclude that the CCV provides a useful model with broader application in mapping, understanding, and tackling the major challenges associated with using real world data in the health setting. Frontiers Media S.A. 2022-09-16 /pmc/articles/PMC9552575/ /pubmed/36238653 http://dx.doi.org/10.3389/fdata.2022.945739 Text en Copyright © 2022 Milne, Sheehan, Barnes, Kapper, Lea, N'Dow, Singh, Martín-Uranga and Hughes. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Big Data
Milne, Richard
Sheehan, Mark
Barnes, Brendan
Kapper, Janek
Lea, Nathan
N'Dow, James
Singh, Gurparkash
Martín-Uranga, Amelia
Hughes, Nigel
A concentric circles view of health data relations facilitates understanding of sociotechnical challenges for learning health systems and the role of federated data networks
title A concentric circles view of health data relations facilitates understanding of sociotechnical challenges for learning health systems and the role of federated data networks
title_full A concentric circles view of health data relations facilitates understanding of sociotechnical challenges for learning health systems and the role of federated data networks
title_fullStr A concentric circles view of health data relations facilitates understanding of sociotechnical challenges for learning health systems and the role of federated data networks
title_full_unstemmed A concentric circles view of health data relations facilitates understanding of sociotechnical challenges for learning health systems and the role of federated data networks
title_short A concentric circles view of health data relations facilitates understanding of sociotechnical challenges for learning health systems and the role of federated data networks
title_sort concentric circles view of health data relations facilitates understanding of sociotechnical challenges for learning health systems and the role of federated data networks
topic Big Data
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9552575/
https://www.ncbi.nlm.nih.gov/pubmed/36238653
http://dx.doi.org/10.3389/fdata.2022.945739
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