Big Data Governance: Solidarity and the Patient Voice

  • Simon WoodsEmail author
Part of the Law, Governance and Technology Series book series (LGTS, volume 29)


Rare diseases are individually rare but collectively form a population of 30 million people within Europe alone. Most rare diseases are genetic in origin and recent research initiatives are bringing the latest genetic technologies, including whole genome sequencing, together with medical records and natural history data. The rareness of these conditions means that strategies for data sharing are a necessity to ensure that patients are able to obtain a diagnosis and the potential for treatment. Rare disease research is therefore a preeminent example of biomedical “Big Data”. This chapter explores the social and ethical challenges of biomedical “Big Data” with a focus on two case studies of contemporary rare disease research and through the framework of “solidarity” as developed by Prainsack and Buyx (2011, 2013). The analysis presented in this chapter is sympathetic to the concept of solidarity as the basis for a governance model for biomedical “Big Data” research. However there are some limitations to the solidarity model and it is argued here that a presumption of solidarity may presume too much. The principle of solidarity is very evident within the history of rare disease patient activism but this has evolved alongside other practices, characterised here as “the patient voice” which demands a more collaborative approach to the governance of research. The collaborative approach is one which allows the patient voice to be heard and respected thereby giving research participants an opportunity to be able to negotiate the conditions of participation in research. The chapter concludes with some reflections upon the future challenges for biomedical “Big Data” governance.


National Health Service Rare Disease Patient Representative Governance Practice International Research Collaboration 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.



Simon Woods has received funding from the European Union Seventh Framework Programme (FP7/2007–2013) under grant agreement No. 305444 (RD-Connect).


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Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  1. 1.Policy Ethics and Life Sciences Research Centre Claremont Bridge (4th Floor)Newcastle UniversityNewcastle upon TyneUK

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