Chapter by Alessandro Blasimme and Effy Vayena: “While data-enabled health care systems are in their infancy, biomedical research is rapidly adopting the big data paradigm. Digital epidemiology for example, already employs data generated outside the public health care system – that is, data generated without the intent of using them for epidemiological research – to understand and prevent patterns of diseases in populations (Salathé 2018)(Salathé 2018). Precision medicine – pooling together genomic, environmental and lifestyle data – also represents a prominent example of how data integration can drive both fundamental and translational research in important medical domains such as oncology (D. C. Collins et al. 2017). All of this requires the collection, storage, analysis and distribution of massive amounts of personal information as well as the use of state-of-the art data analytics tools to uncover healthand disease related patterns.
The realization of the potential of big data in health evokes a necessary commitment to a sense of “continuity” articulated in three distinct ways: a) from data generation to use (as in the data enabled learning health care ); b) from research to clinical practice e.g. discovery of new mutations in the context of diagnostics; c) from strictly speaking health data (Vayena and Gasser 2016) e.g. clinical records, to less so e.g. tweets used in digital epidemiology. These continuities face the challenge of regulatory and governance approaches that were designed for clear data taxonomies, for a less blurred boundary between research and clinical practice, and for rules that focused mostly on data generation and less on their eventual and multiple uses.
The result is significant uncertainty about how responsible use of such large amounts of sensitive personal data could be fostered. In this chapter we focus on the uncertainties surrounding the use of biomedical big data in the context of health research. Are new criteria needed to review biomedical big data research projects? Do current mechanisms, such as informed consent, offer sufficient protection to research participants’ autonomy and privacy in this new context? Do existing oversight mechanisms ensure transparency and accountability in data access and sharing? What monitoring tools are available to assess how personal data are used over time? Is the equitable distribution of benefits accruing from such data uses considered, or can it be ensured? How is the public being involved – if at all – with decisions about creating and using large data
repositories for research purposes? What is the role that IT (information technology) players, and especially big ones, acquire in research? And what regulatory instruments do we have to ensure that such players do not undermine the independence of research?…(More)”.