ABSTRACT
A decade ago, the term ‘personalised medicine’ was largely synonymous with the matching of drug therapies to the genomes of individual patients (for example, Hedgecoe, 2004; see also Jones, 2013). Since then, the concept has started to become more inclusive, often referring to the consideration of individual characteristics – both molecular and otherwise – in medical research and practice (for example, Pokorska-Bocci et al., 2014). 1 A recent report of the European Science Foundation (ESF, 2013), for example, defined personalised medicine as considering individual characteristics at every stage of medical practice, from prevention, diagnosis and therapy to monitoring. Other policy papers and reports – some of which use different notions, such as stratified medicine 2 or precision medicine (for example, NAS, 2011; Eckhart et al., 2014) – also discuss the phenomenon of personalisation in medicine in similar terms. In this broader vision of personalised medicine, medical decision-making increasingly relies on the analysis and interpretation of data on the patient’s genome and gene-expression, lifestyle, or other relevant clinical and personal information. 3 The idea is that these datasets will be stored in a place where they are accessible for whatever clinical decisions need to be made. They could be stored in the clinic, or elsewhere as determined by the patient (Steinbrook, 2008; Hafen et al., 2014).
