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.
Following are some of the key use cases we see as emerging/growing in payer organization in analytics and big data solutions.
Back Office: Back office is the data warehouse of the payer organization. Critical back office functions include, enrolment and eligibility, billing, claims, member services, and provider services. It is critical to deploy Big Data management systems to capture and store the data so that it can be leveraged by other areas in the front office and middle office. Effective Big Data solutions are used to generate a 360-degree view of members which will provide a strong platform for analytics driven insights into the member population.
Middle Office: The middle office operations are focused on current members and include network management and medical management. Descriptive analytics and prescriptive analytics of the data generated/stored in the back office is critical to the transformation of middle office. Provider network and performance management is driven by analytics, and helps payers take stock of their provider network. Moreover, in a value-based care setting maintaining stable and efficient provider network is of paramount importance. Provider analytics helps understand provider behaviour, increase efficiencies and productivity, reduce costs, and grow revenue. Medical analytics can be leveraged to advocate on behalf of members and guide them to the right providers and treatment options.
Front Office: The front office operations are focused on acquiring new customers, engaging existing customers, and wellness initiatives. Predictive analytics, when compared to descriptive and prescriptive analytics, is key in this space. Real-time analytics and predictive analytics can be used to design products/plans targeted at member segments and market demand. This helps expanding the member base. When it comes to engaging existing members, more developed mobile applications are focused on helping members manage their care, identify providers, and improve their health. Gathering data from these mobile applications and by analysing the same, payers are able monitor their member population and detect trends that contribute to wellness, both at a population and an individual level.