ABSTRACT
The digitization of health care has led to enormous increases in health-related data, constituting big data. The health industry is complex, comprising various stakeholders, and generating heterogeneous data in enormous volumes at great speed from multiple sources; such data have great potential for deriving valuable information, if handled in an appropriate way. In addition, greater importance is being awarded to evidence-based decision making, as result of which value-added services in healthcare can be improved. Handling big data in healthcare is thus complex, and data processing requires advanced machine learning algorithms, artificial intelligence, and data mining with sophisticated tools and techniques for acquisition, storage, processing, and analysis of the data, components which do not exist in traditional healthcare systems. Data analytics is essential, providing timely strategic business decisions at reduced cost. In this chapter, we have provided an outline of big data in healthcare and presented an overview of Hadoop architecture. Furthermore, the tools and techniques used in the healthcare industry are described in detail. Various areas for the application of big data to health care are discussed in detail, along with a list of challenges faced by the health industry in the handling of big data.
