Machine learning, materiality and governance: A health and social care case study

Abstract

There is a widespread belief that machine learning tools can be used to improve decision-making in health and social care. At the same time, there are concerns that they pose threats to privacy and confidentiality. Policy makers therefore need to develop governance arrangements that balance benefits and risks associated with the new tools. This article traces the history of developments of information infrastructures for secondary uses of personal datasets, including routine reporting of activity and service planning, in health and social care. The developments provide broad context for a study of the governance implications of new tools for the analysis of health and social care datasets. We find that machine learning tools can increase the capacity to make inferences about the people represented in datasets, although the potential is limited by the poor quality of routine data, and the methods and results are difficult to explain to other stakeholders. We argue that current local governance arrangements are piecemeal, but at the same time reinforce centralisation of the capacity to make inferences about individuals and populations. They do not provide adequate oversight, or accountability to the patients and clients represented in datasets.

Materiality and the mediating roles of eHealth: A qualitative study and comparison of three cases

“… 29 – 31 Although eHealth solutions have been studied for years, there remains a gap in understanding of how the materiality of eHealth solutions affects care and care work. 18 , 32 – 34 Leornadi 35 defines materiality as ‘the arrangement of an artefact's physical and/or digital materials into particular forms that endure across differences in place and time and are important to users’. He argues that the material aspects of technology become materialised in a context and evolve in that context, that is, they are not fixed.…”