What is Big Data in Healthcare?


Healthcare information technology is a highly specialized domain with a nomenclature of its own. So what does Big Data mean in the healthcare IT context? The electronic health record (EHR) has evolved over the past few decades from its roots as a flat repository for entering text (“data in”) to a selective information-surfacing vehicle (“data out”) to a knowledge-building and decision-support tool (“data enhanced”). With this rapid evolution from glorified notepad to indispensable clinical quality-improvement adjunct, can today’s EHR be considered Big Data?

To answer the question, three characteristics of the data must be considered: their source, their timing, and their application.

Big healthcare data are derived from many, many patients (or providers or practices), often separated geographically or contextually. The flow of the information is usually understood to be very timely or even instantaneous.

What is big data in healthcare?

Big healthcare data generate results or actions that directly or indirectly affect patients or patient-facing systems and are most often based on complex, dynamic analytic methods. Note that this model is based not only on the data themselves, but also on how the data are enhanced and on how, when, and where they are used.

In healthcare, the move toward Big Data implies growth beyond the single patient or single provider, to a practice, to a hospital or health system, and ultimately to the global community [Figure 1]. While descriptive analytics (i.e., “what happened?”) can be sophisticated and meaningful, Big Data tend toward the predictive (“what will happen?”) and prescriptive (“what should I do to optimize what will happen?”) realms. Big scope plus sophisticated analytics equals Big Data. Looking at a patient’s serum glucose measurements over time would not be considered Big Data; determining from national data what the best community-based intervention might be for reducing the prevalence of diabetic neuropathy, for example, would be.

The real goal is not to make the data big, but rather to progress in the direction of putting the information to work to help improve the quality of patients’ lives and the health of communities.

To be continued tomorrow in part two…



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