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 (Frontiers in Big Data, 2022)
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 the patient and public trust. In this Perspective we propose an approach to thinking about data in terms that make it easier to navigate the health data space. We set out a socio-technical model of data systems that we call the ‘Concentric Circles View of data-relationships’. The aim is to enable a consistent understanding of the fit between the relationships within which data are produced and the socio-technical system that enables 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 examine the potential of federated or distributed data networks to respond to both technical and social constraints on data use. We suggest that the CCV allows us to understand how and 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 that may have broader application in mapping, understanding, and tackling the major challenges associated with using real world data in the health setting.