Machine learning, healthcare resource allocation, and patient consent

Abstract

The impact of machine learning in healthcare on patient informed consent is now the subject of significant inquiry in bioethics. However, the topic has predominantly been considered in the context of black box diagnostic or treatment recommendation algorithms. The impact of machine learning involved in healthcare resource allocation on patient consent remains undertheorized. This paper will establish where patient consent is relevant in healthcare resource allocation, before exploring the impact on informed consent from the introduction of black box machine learning into resource allocation. It will then consider the arguments for informing patients about the use of machine learning in resource allocation, before exploring the challenge of whether individual patients could principally contest algorithmic prioritization decisions involving black box machine learning. Finally, this paper will examine how different forms of opacity in machine learning involved in resource allocation could be a barrier to patient consent to clinical decision-making in different healthcare contexts.

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