Precision Medicine

Abstract

Precision medicine combines genetic, environmental and lifestyle variability to inform disease diagnosis, treatment and prevention, allowing exact medical interventions both on individual and population levels. Data-driven precision medicine measures constitute an informational intervention that is dynamic in time, space and in terms of actors and groups involved, as well as regarding the relevance of results and the causality of decisions. Correspondingly, normative guidance for decision making is characterised by strong proceduralisation. When justifying data processing, the changing role of patients in relation to data processing needs to be respected. It not only influences the design of informed consent, but significantly impacts data security in response to identified risks. Further issues in precision medicine include dealing with anonymisation as well as the return of research results. New tools such as machine learning and its application through neurotechnologies pose challenges to patients’ autonomy, benefit production, sharing, justice and equity. In response to the need for dynamic guidance to engage with these particular challenges, procedural measures and tools framing conduct of precision medicine have emerged, including codes of conduct, closer ethics committee scrutiny and data stewardship models. These tools enable ethics-by-design and contribute to coordination between ethical and legal rules.

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