The application of analytics in healthcare has been transforming over the past five to six years. Prior to this transformation, analytics applied to patient data were mostly descriptive in nature. That is to say, the simple reports generated by healthcare providers were basic and only told the story of “what happened.” In this era of big data, more and more healthcare organizations are looking to take advantage of their data in a more meaningful way. Their goal is to extract business relevant information that enables providers, managers, and executives to derive actionable insight from their data. Recently, I had the pleasure of researching this topic for a graduate class I took. I feel strongly that we are seeing a paradigm shift in how providers and payers are looking at their data (both structured and unstructured). This research addresses the key issues facing the healthcare industry today as well as in the future.
This research will examine the role of Analytics in Healthcare. In 2010, healthcare costs in the U.S. accounted for 17% of GDP (2.2 trillion dollars) (1). It is projected that by 2017, these costs will reach 4 trillion dollars per annum (2). Trends in the public and private sectors indicate that these high costs are not sustainable. In the public sector, title XIII of the American Recovery and Reinvestment Act (HITECH Act), includes incentives to providers for the adoption of electronic health records (EHR) over traditional paper (3) (4). The private sector is also working to address the problem. They are striving to drive down costs, improve coordination and outcome, and provide more with less (5) by leveraging advanced analytics. Descriptive analytics in healthcare is not a new concept (5). What this research paper will show is that for both the public and private sectors, the need to move beyond descriptive analytics is critical in solving the problems of modern day healthcare. Adoption of predictive and prescriptive analytics against clinical data is key in addressing these issues.