The benefits of big data analytics in the healthcare sector are assumed to be substantial, and early proponents have been very enthusiastic (Chen, Chiang, & Storey, 2012), but little research has been carried out to confirm just what those benefits are, and to whom they accrue (Bollier, 2010). This chapter presents an overview of existing literature that demonstrates quantifiable, measurable benefits of big data analytics, confirmed by researchers across a variety of healthcare disciplines. The chapter examines aspects of clinical operations in healthcare including Cost Effectiveness Research (CER), Clinical Decision Support Systems (CDS), Remote Patient Monitoring (RPM), Personalized Medicine (PM), as well as several public health initiatives. This examination is in the context of searching for the benefits described resulting from the deployment of big data analytics. Results indicate the principle benefits are delivered in terms of improved outcomes for patients and lower costs for healthcare providers.
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Biomedical informatics is the science of information applied to medicine and is “distinct from related fields like computer science, statistics and biomedicine, which have different objects of study”(Bernstam, Smith, & Johnson, 2010). Biomedical informatics incorporates a “core set of methodologies for managing data, information and knowledge” (Sarkar, 2010) with the goal of “improving the quality and safety of healthcare while reducing the costs” (Hersh, 2009).
Advances in biomedical informatics have been proceeding at an astonishing pace, with some notable successes and some equally notable setbacks. The following chapter offers a comprehensive review of what benefits, and what drawbacks, current research into the application of big data analytics to healthcare and biomedical engineering have revealed, in an effort to guide further research and to understand more clearly what benefits arise where big data intersects with healthcare, and to whom those benefits accrue.
According to a McKinsey report (2011) on the potential financial savings to be harvested from big data analytics, healthcare is particularly rich in opportunity. Clinical operations, payment and pricing, R&D, public health and new business models all have the potential to benefit from the analysis of large sets of data. Preliminary investigation reveals that there have been many documented, quantified gains that accumulate to improve physician performance, provide better guidance for treatment, dramatically improve patient outcomes and significantly lower costs for hospitals, insurers and co-payees.
The looming demographic shift in the United States and other developed nations portends health care costs that will consume a significant portion of national budgets in the years to come, and delivering better health care to more people for less cost will be a critical policy issue (Ahern, Smith, Topol, Mack, & Fitzgerald, 2013; Bloom, Börsch-Supan, McGee, & Seike, 2012; Morton & Weng, 2013; Vogeli et al., 2007).