Managing high-cost members with analytics
As the health demands of patients expand, payers are increasingly focused on a small segment of their member population that is driving the largest share of healthcare spending: high-cost members. According to the American Health Policy Institute, these high-need, high-cost individuals can cost $100,000 every year, which accounts for one-third of total healthcare spending.
As a result, payers are looking for new ways to identify and engage with these individuals using analytic tools. We recently spoke with Diane Gerdes, payer product marketing manager at MedeAnalytics, for her input on the value of using data analytics to address high-cost members’ needs and costs.
What challenges are payers facing when it comes to understanding high-cost members?
For many organizations, the biggest challenge is identifying individuals before they become a high-cost member. These individuals are dealing with acute and chronic conditions and possibly taking expensive prescription medications. Typically, it isn’t until a costly event like a hospitalization that problems finally come to the surface. Adding to this challenge is predicting who will continue to be a high-cost member in the long-term. That’s where advanced analytics can help by targeting individuals who are high-cost or at-risk and giving payers the insight to design proactive solutions that keep members healthy and minimize costly adverse health events.
What pain points do payers face around integrating and analyzing large amounts of data?
General pain points center around the overall process for collecting, integrating and analyzing data. Because data is typically housed in various operational and clinical systems, it takes an extraordinary amount of time and effort to organize and mine the data. By the time the data is ready for analysis, it is no longer timely, and payers miss critical opportunities to intervene and engage with members.