The term “big data” is suddenly pervasive. The New York Times deemed this the “Age of Big Data” in a 2012 article, and a Google search for the term yields over 50 million hits. “Big data” is difficult to define precisely, but it is characterized by three attributes known as “the three V's”: its large volume, its variety, and its velocity, that is, the frequency with which it is generated. Medical big data is a particularly rich but sensitive type of big data, and it holds great promise as a resource for researchers and other analysts. Numerous medical big data initiatives are being launched by public and private enterprises.

The transition from paper medical files to EHR systems has facilitated the creation of large health information databases. Computer processing of digitized records permits fast and relatively inexpensive data analysis and synthesis. These databases, therefore, can serve as invaluable research resources.

Medical big data can be derived from other, nontraditional sources as well. Google retains all users’ web searches, including those involving medical queries. It can use the data for its own purposes, and at times, the data are requested by government and law enforcement authorities as well. Customer purchasing records, tweets, and Facebook entries can also reveal a great deal of health information. Companies such as Acxiom offer to sell “demographic, behavioral, lifestyle, financial and home data” that they obtain from such sources. This book, however, focuses on medical big data that is derived from electronic health records (EHRs) or from reports submitted by healthcare providers. It does not, therefore, extensively address nontraditional sources of medical big data.

Analysts can access large-scale collections of EHR data in two primary ways. First, health information can be collected into large databases and deidentified to protect patient privacy. Such databases could be limited to particular hospital systems, be expanded to cover entire regions, or even be national in scope. In the alternative, researchers may use a federated system. A “federated network” can be defined as one that “links geographically and organizationally separate databases to allow a single query to pull information from multiple databases while maintaining the privacy and confidentiality of each database.”