In Oklahoma, a Regional HMO Deploys Predictive Analytics to Drive Better Health Outcomes | Healthcare Informatics Magazine | Health IT | Information Technology Skip to content Skip to navigation

In Oklahoma, a Regional HMO Deploys Predictive Analytics to Drive Better Health Outcomes

August 30, 2016
by Heather Landi
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Using predictive analytics and prescriptive health insights, GlobalHealth can now predict nearly 70 percent of its hospital admissions
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Two years ago, GlobalHealth, an Oklahoma City-based health maintenance organization (HMO), began a proactive outreach program utilizing predictive and prescriptive analytics as part of its care management efforts. The goal was to identify plan members whose health was most likely to change for the worse in the next 12 months in order to intervene to reduce medical costs and improve health and wellness for plan members.

GlobalHealth is a regional HMO that covers more than 45,000 individuals in all 77 Oklahoma counties and its membership includes state and education employees, federal employees, municipal employees, Medicare Advantage members and private employers.

According to a case study released by GlobalHealth, the organization has seen notable results since implementing a predictive analytics platform from VitreosHealth as an Insights-as-a-Service (IaaS) delivery model for population risk models for predictive and prescriptive health data insights. GlobalHealth essentially combines the data insights with human outreach to better understand its members’ needs.

Since the initiative began in early 2014, GlobalHealth has experienced an 18 percent reduction in emergency room encounters and emergent hospital admissions among the target population as well as a 22 percent reduction in readmissions. GlobalHealth also has seen per-member, per-month (PMPM) medical costs for that target population reduced by 16 percent, and, more generally, spread across all members, there has been a 6 to 8 percent reduction in PMPM medical costs. In 2015, the organization realized $10 million annual savings, and GlobalHealth can now predict nearly 70 percent of its hospital admissions, according to David Thompson, GlobalHealth chief operating officer.

Back in 2013, GlobalHealth executive leaders recognized the need to utilize predictive analytics technology and prescriptive health insights to identify at-risk members and strengthen its care management program in order to provide better health outcomes for its members.

Thompson specifically recalls a meeting three years ago with staff members and executives discussing case management rounds that proved to be the impetus for the analytics initiative. During the meeting, the discussion turned to a plan member who suffered a recent diabetic coma. “The member was a relatively healthy, young person who did have diabetes and other conditions. So we asked ourselves if there was anything we could have done to prevent this. The directors of case management said yes, we could have prevented it, if we had done this or that at this point in time, so we went through the case in reverse chronology to piece together the patterns of that case and we realized it would be helpful to have all this data in one central, manageable place. And that was the lightbulb moment when we decided we needed an analytics engine that could help predict these types of events. In the past few years we have been working on building predictive modeling into our care management techniques.”

Thompson continues, “Because we are a small plan, we had a lack of available, combined sophisticated data sets and we were challenged with identifying members for these negative health events, or what we considered to be avoidable, manageable events, such as emergent hospitalizations or readmissions. We just didn’t have great data to in order to intervene to prevent that. We’d always been good at the point of service and managing care when a member is hospitalized and transitioning them back home and identifying the community resources around them. But we were not good at predicting the diabetic comas or the members who had co-morbid, complex conditions which often manifests into a future unhealthy event.”

At the same time, Oklahoma also is a state with significant health concerns as it is consistently ranked near the bottom of all 50 states based on the healthiness of its population. Oklahoma’s adult obesity rate is 33 percent, which is the sixth highest rate in the nation, according to data from Trust for America’s Health and the Robert Wood Johnson Foundation. Nearly 22 percent of adult Oklahomans reported having a mental health issue and close to 10 percent experienced a substance abuse issue, according to Mental Health America. And, among Oklahoma’s adult population, 24 percent are smokers, compared to the national average of 19 percent, according to 2015 data from the Centers for Disease Control and Prevention.

Thompson says for organizational leaders the first step on this journey was to examine options for building an analytics program. The organization’s internal information technology team believed it could develop the necessary data infrastructure, however, it was estimated it would take two years to build. So, GlobalHealth executive leaders reviewed the options offered by external vendors.

One of GlobalHealth’s priorities was finding an analytics partner with a solution that could seamlessly integrate and evolve with the organization’s existing IT structure.

“We needed a partner who could do several things. One, have a strong data architecture and legitimate interfacing capabilities,” Thompson says. “A lot of companies say they are doing predictive modeling, but they’re not doing anything that couldn’t be done in house, as it’s still reactive and closing care gaps, but it doesn’t impact the full spectrum. From a vendor standpoint, we wanted a partner who was willing to customize and be flexible, and was on the bleeding edge.”


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