The healthcare industry is undergoing a major transformation due to an increasing acceptance of big data technologies and advanced analytics. As per HIMSS, the amount of healthcare data being generated is increasing at an exponential rate of 48% a year, hence, healthcare industry is looking for ways to manage and leverage the same.
Healthcare payers generally store data in centrally controlled data warehouses. Since they are siloed systems, there is no unified view of data and this is one of the biggest hurdles that prevent payer organizations from unlocking the potential of their data.
When it comes to analytics, there are 4 kinds of analytics: descriptive, diagnostic, predictive, and prescriptive. Descriptive and diagnostic analytics are based on the past, and are focused on fixing what is currently not optimal or broken, whereas predictive analytics and prescriptive analytics influences future outcomes and can make or break situations. That said, currently the bulk of the organizations rely on descriptive and diagnostic analytics. While these forms of analytics are useful in the short term, in the long run, organizations will fall behind if they do not adopt predictive analytics in the near future and finally move on to prescriptive analytics.
To better understand the impact of Big Data technologies and analytics, we can segment the healthcare payer organization into three operational areas, i.e., front office, middle office, and back office. The rationale for this segmentation lies in the fact that back office functions, such as billing and claims, enterprise functions, and member/provider services are the producers/source of data. The middle and front offices are the consumers of this data; middle office caters to providers (B2B) and front office caters to members (B2C). However, the data that lies in back office is siloed, not integrated, and lacks a unified structure for effective consumption in the middle and front office.
Therefore, we can look at back office as the area for transformation/application for big data use cases, such as data warehousing, integration, EMPI, and centralized Data Lake. Furthermore, middle office and front office are the ripe areas for transformation from an analytics perspective. While middle office analytics is already happening in terms of descriptive analytics focused on optimizing provider networks from a cost and quality perspective, front office analytics is an ongoing journey. Front office analytics requires more intuitive capabilities to understand member’s lifestyle and generate actionable insights for both care givers and patients.