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Health care research focuses on the description and analysis of the health care system and its requirements. Research-derived innovations are the subject of trials and evaluation of the transfer to daily routine. For this purpose health care research has developed a broad theory-based spectrum of methods. On the other hand, the concept of big data is an new informatics-driven approach to large data sets independent of content. With its technical vocabulary the concept of big data does not easily fit into traditional health care research. Central tasks of health care research such as the generation of theories, norm-oriented evaluations or proof of causality can neither be supported nor replaced by big data. However, the concept of big data has the potential to support health care research, with traditional tasks such as data linkage, analysis of health care paths, quick access to up-to-date data on the distribution and acceptance of health care services, as well as prediction and the generation of hypotheses. The prerequisite for all this is a trust-based linkage of different medical and nonmedical data sources on the basis of the legal regulation of data access and data protection.

Big data denotes our capacity to gain insights from (in relative terms!) large amounts of data that we could not have had by just looking at samples. Our difficulty in working with data has shaped our methods in the small data age. As these limitations with respect to data diminish, we will have to rethink and adjust our scientific methods. In return, we will gain a wealth of new insights, perhaps leading towards a new golden era of scientific discovery. Big Data power demands, however, that we also are cognizant of its limitations and the significant dangers of abusing it.

Healthcare is one of the business fields with the highest Big Data potential. According to the prevailing definition, Big Data refers to the fact that data today is often too large and heterogeneous and changes too quickly to be stored, processed, and transformed into value by previous technologies. The technological trends drive Big Data: business processes are more and more executed electronically, consumers produce more and more data themselves - e.g. in social networks - and finally ever increasing digitalization. Currently, several new trends towards new data sources and innovative data analysis appear in medicine and healthcare. From the research perspective, omics-research is one clear Big Data topic. In practice, the electronic health records, free open data and the "quantified self" offer new perspectives for data analytics. Regarding analytics, significant advances have been made in the information extraction from text data, which unlocks a lot of data from clinical documentation for analytics purposes. At the same time, medicine and healthcare is lagging behind in the adoption of Big Data approaches. This can be traced to particular problems regarding data complexity and organizational, legal, and ethical challenges. The growing uptake of Big Data in general and first best-practice examples in medicine and healthcare in particular, indicate that innovative solutions will be coming. This paper gives an overview of the potentials of Big Data in medicine and healthcare.

The article examines the impact that legislative developments in the European Union have had, still have and are continuing to have on cross-border access to microdata for research purposes. Therefore, we describe two competing aims: the tension between the ambitions of the EU to create a European Research Area within which research communities gain access to and share data across national boundaries; and the desire within the EU to establish a harmonious legislative framework that provides protection from the misuse of personal information. We attempt to examine which new developments at the EU level will have an impact upon research plans and the challenges researchers face when analysing big data.