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Can Big Data Fulfill Its Promise?

Big data analytics are widely touted as a key innovation to improve health care. Although the term big data is variably defined, it generally implies the application of advanced statistical analyses, with names such as machine learning, artificial intelligence, or cognitive computing, to data sources that greatly exceed the size and complexity of databases traditionally used for healthcare analyses.1 Somewhat counterintuitively, data volume alone does not qualify an analysis as big data analytics. The term more appropriately refers to analytic and computational techniques, combined with information technology innovations, that were specifically developed to yield insights from the large quantities of data that are increasingly common in the digital economy. Big data analytics offer the promise of turning large amounts of data into superior predictive models that can be used to improve healthcare quality and outcomes.

Since its inception, Circulation: Cardiovascular Quality and Outcomes has focused on using scientifically rigorous data analyses to improve health care,2 thus the journal is a natural home for scientific investigations using big data analytic techniques. The current Special Issue highlights several high-quality studies that reveal the depth and breadth of these new methods and points toward a future where big data analytics are incorporated into the daily practice of all clinicians, much as big data analytics routinely impact the everyday online experiences of millions of users of Google, Amazon, and other internet-based companies.

Although the term big data is a relatively new invention,1 many of the techniques of big data analytics have been in existence for decades, and the field of biomedical informatics has been critical to their development.3 Several recent phenomena have converged to move big data analytics to the forefront of health care, including the widespread adoption of electronic medical records and subsequent digitization of large volumes of healthcare …