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Health Aff (Millwood). 2014 Jul;33(7):1123-31. doi: 10.1377/hlthaff.2014.0041.

Big data in health care: using analytics to identify and manage high-risk and high-cost patients.

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David W. Bates ( is chief of the Division of General Medicine, Brigham and Women's Hospital, in Boston, Massachusetts.
Suchi Saria is an assistant professor of computer science and health policy management at the Center for Population Health and IT, Johns Hopkins University, in Baltimore, Maryland.
Lucila Ohno-Machado is associate dean for informatics and technology in the Division of Biomedical Informatics, University of California, San Diego, in La Jolla.
Anand Shah is vice president of clinical services at PCCI, in Dallas, Texas.
Gabriel Escobar is regional director of hospital operations research and director of the Systems Research Initiative, Division of Research, Kaiser Permanente, in Oakland, California.


The US health care system is rapidly adopting electronic health records, which will dramatically increase the quantity of clinical data that are available electronically. Simultaneously, rapid progress has been made in clinical analytics--techniques for analyzing large quantities of data and gleaning new insights from that analysis--which is part of what is known as big data. As a result, there are unprecedented opportunities to use big data to reduce the costs of health care in the United States. We present six use cases--that is, key examples--where some of the clearest opportunities exist to reduce costs through the use of big data: high-cost patients, readmissions, triage, decompensation (when a patient's condition worsens), adverse events, and treatment optimization for diseases affecting multiple organ systems. We discuss the types of insights that are likely to emerge from clinical analytics, the types of data needed to obtain such insights, and the infrastructure--analytics, algorithms, registries, assessment scores, monitoring devices, and so forth--that organizations will need to perform the necessary analyses and to implement changes that will improve care while reducing costs. Our findings have policy implications for regulatory oversight, ways to address privacy concerns, and the support of research on analytics.


Cost of Health Care; Information Technology; Quality Of Care

[Indexed for MEDLINE]

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