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.
Can you share more on how analytics can help?
Analytics can help payers take a more proactive approach by uncovering opportunities to impact high-cost members earlier. An intelligent analytics platform that efficiently gathers data across all systems (e.g., medical, pharmacy, lab) helps payers more quickly target individuals who have chronic conditions or who are at-risk, as well as those who are not complying with evidence-based guidelines. Payers armed with this insight are better positioned to align programs (e.g., care management and preventive care) and intervene with individuals before their conditions progress into complex, high-cost conditions. Solutions like MedeAnalytics’ Healthcare Economics makes data accessible and approachable, and empowers payers to make faster, smarter decisions that lead to improved outcomes.
MedeAnalytics worked successfully with St. Joseph Hospital, a part of Covenant Health, to help them identify and target those at risk for high-cost care within their own workforce. St. Joseph Hospital leveraged data and analytics to improve care for their employees and families, while also designing benefit plans and cutting costs. The hospital’s data revealed just 9 percent of the highest risk employees were responsible for 40 percent of employee health plan costs. Analyzing their employee data allowed the organization to identify exactly where the money was being spent and make changes regarding pharmaceutical costs and benefit plans, which put them on track to spend $2.5 million less the following year.
The challenges facing payers are endless but with the investment and use of data analytics, organizations can make even smarter decisions about the members they serve. To learn more about St. Joseph Hospital’s success – click here. Interested in learning how we can help your organization achieve similar results and combat high-cost members? Visit our solutions page here.
Get our take on industry trends
Proactively predicting ER visit trends with augmented analytics to improve revenues, asset utilization and patient outcomes
Mission critical emergency departments (EDs) are the most valuable revenue generating asset for hospitals. While visits decreased during the pandemic,…Read on...
Conversations at HIMSS23
HIMSS23 was nothing short of outstanding. I was thrilled to see the familiar faces of colleagues and clients, mingle with…Read on...
Best practice tools to build an integrated approach to multimorbidity
The traditional model of treating single diseases no longer works. Data collected from 2016 to 2019 indicated that 32.9% of…Read on...
Will adopting a risk-based approach with augmented analytics support care gap closure?
A common challenge for healthcare systems is how to properly segment its patient populations based on risk profiles and co-morbidities. Doing this well ensures a high quality of care delivery and superior patient outcomes.Read on...
Leave a Comment