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

Richard Milne, Mark Sheehan, Brendan Barnes, Janek Kapper, Nathan Lea, James N'Dow, Gurparkash Singh, Amelia Martín-Uranga, Nigel Hughes*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

Abstract

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.

Original languageEnglish
Article number945739
Number of pages9
JournalFrontiers in Big Data
Volume5
DOIs
Publication statusPublished - 16 Sep 2022

Keywords

  • consent
  • data
  • distributed data access
  • ethics
  • federated data access
  • trust

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