Humana Using Analytics to Improve Health Outcomes
Updated · Aug 14, 2015
Earlier this year a CDW survey revealed that analytics is a top priority for two thirds of decision-makers in the health care industry. Nearly 70 percent of respondents said they were planning for or already implementing analytics.
This is no surprise, given the strong results seen by analytics from early adopters like Humana.
The health insurer has made analytics a foundational piece of its clinical operations and consumer engagement efforts. Humana uses predictive models to identify members who would benefit from regular contact with clinical professionals, helping them coordinate care and making needed changes in healthy lifestyle, diet and other areas. This proactive approach results in improved quality of life for members, at a lower cost, said Dr. Vipin Gopal, Enterprise VP, Clinical Analytics.
According to Humana, it identified 1.9 million members with high risk for some aspect of their health through predictive models in 2014. It also used analytics to detect and close 4.3 million instances where recommended care, such as an eye exam for a member with diabetes, had not been given. In those cases, Humana notified members and their physicians, through which such gaps in care were addressed.
“Every touch point with the health care system yields data, whether it’s a physician visit or a visit to a hospital or an outpatient facility,” Gopal said. “We use analytics to understand what can be done to improve health outcomes. Humana has over 15,000 care managers and other professionals who work with members to coordinate care and help them live safely at home, even when faced with medical and functional challenges. All of that work is powered by analytics.”
While health care has lagged other industries in adopting analytics, it accumulates a large volume of data which can be used to generate useful insights, Gopal said, adding, “Health care can hugely benefit from the analytics revolution.”
Until recently, Gopal said, many in health care “did not see analytics as a key component of doing business.” That is rapidly changing, however, largely based on the example of companies like Humana.
Humana also used predictive analytics to help reduce the hospital readmission rate by roughly 40 percent through its Humana at Home programs. After noting that about one in six members enrolled in Humana’s Medicare plans were readmitted within 30 days of a hospital visit, the company built a predictive model to determine which members were most likely to get readmitted. It created a score quantifying the likelihood of readmission for each member; if the score rose above a certain point, a clinician would immediately follow up with the member.
This effort is especially notable, Gopal said, because it incorporates real-time analytics.
“When you are discharged from the hospital, for instance, the score is updated in real time and sent to a nurse,” he said. “If you are trying to prevent a readmission from happening within 30 days, you cannot run a predictive model once a month or even once a week.”
One of Humana’s latest efforts involves using analytics to address the progression of diseases like diabetes, which Gopal said affects about 30 percent of senior citizens. It is classifying its members with diabetes into low, medium and high severity categories. As a person goes from low to high severity, costs of care increase by seven times and quality of life steeply declines. Foot wounds go up 36 times, for example, and the number of foot amputations rises. So Humana is using predictive models to identify members most likely to progress and, hopefully, to slow progression through clinical interventions.
“Really understanding the variables through deep analytics and helping people to not progress, will be huge for our members and for overall public health as well,” Gopal said.
Keys to Analytics Success
Humana has benefited from a relatively mature technology infrastructure, a supportive CEO and an analytics team that Gopal built by design to include a mix of professionals with varied backgrounds — not just data scientists but those with backgrounds in public health, computer science, applied math and engineering.
Of his team, Gopal said, “These are deep problems, and we need the best multidisciplinary talent working on them. It’s not something just public health people can solve, or just computer science people can solve.”
Perhaps the biggest factor in Humana’s success with its analytics program, Gopal said, is using analytics to solve meaningful challenges.
“We do not work on stuff just because it’s cool to do, we work on problems where we can make a direct impact on the business,” he said. “That is how we select projects, and see it through to implementation and right through to results.”
While not all initiatives will be as ambitious as Humana’s, predictive analytics is gaining momentum as predictive analytics tools become more user friendly.
Ann All is the editor of Enterprise Apps Today and eSecurity Planet. She has covered business and technology for more than a decade, writing about everything from business intelligence to virtualization.
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