Data, Diagnosis and Decision-Making in Healthcare: Navigating Uncertainty Through Moral Discourse and Practice

• In Event: Precision and Uncertainty in a World of Data - I

Abstract

The emergence of both discourses and practices oriented on ‘big data’ in health has been characterised by sharp contrasts between promises of personalisation and precision, on one hand, and realities of depersonalisation and uncertainty on the other. The data-intensive approach that renders individuals as anonymised data subjects may also disrupt expectations about the relationship between care providers and patients; while uncertainty manifests both at individual level, for example in relation to the multiple uncertainties invoked by personal genomic analysis, and more generally, such as concerns over artificial intelligence (AI), (in)explicability and the ‘black box’ of algorithmic decision-making. In the context of fracturing notions of ‘personalised’ and ‘precision’ medicine, the ethical issues that become (or are made) most salient in relation to data, AI and health also reflect wider uncertainties and concerns about the provision and structuring of health care, from personal to systemic. The way in which these issues are debated and negotiated, in theory and in practice, in turn reveals the fault-lines of moral certainty and uncertainty embedded within current ethical understandings of medicine and healthcare. In this paper, I explore how practices of ethics and moral decision-making are constructed, translated and deployed in relation to ‘big data’, AI, health care and research, as a way of navigating the different forms of uncertainty generated across these interlinked contexts.

Authors

• Sarah Chan, University of Edinburgh

• Sonja Erikainen, University of Edinburgh