The age of big data is here and many health plans have been building and leveraging their data analytics capabilities for some time. Plans that have not invested in the technology infrastructure and data building necessary to maximize the benefits of data analytics could soon find themselves lagging behind. Here are some ways health plans can start differentiating themselves by using big data.
1/ Build a foundation
Data analytics is only as powerful as the underlying data. That is why many health plans are investing heavily in upgrading their technology infrastructure and cleaning up and standardizing their data.
“Some plans are relatively sophisticated in using their data and others are struggling,” says Pamela Peele, chief analytics officer for UPMC Insurance Services Division in Pittsburgh. “The constraining factor is how much investment health plans have in their IT infrastructure and that varies widely across health plans.”
UPMC Health Plan, which has invested some $1.5 billion in its IT infrastructure, has created a large data source of what Peele calls “a harmonized, groomed layer of information holdings and data from multiple disparate sources.”
UPMC Health Plan is using data analytics in a number of areas. For example, it focuses on reducing hospital readmissions before a member is even admitted rather than waiting until the patient is discharged. The plan has developed data models that calculate the probability of readmission among its entire health plan membership.
“Every month, we are predicting readmission probability based on whether a plan member who is admitted to the hospital today would be readmitted to the hospital within 30 days after discharge,” says Peele. “When someone is admitted to one of our hospitals, that readmission risk is displayed on the opening screen.” At that point, the hospital creates the authorization for the admission and also begins the work on reducing that readmission risk as much as possible.
2/ Set guidelines
Using data analytics to bolster existing priorities may be tempting, but doing so will not allow health plans to maximize their return on their investments. Ken Park, vice president of payer and provider solutions at WellPoint in Indianapolis, offers three suggestions that can serve
as broad guidelines when using
Don’t bend the data in order to prove an ongoing hypothesis. Look at what the data is actually showing you.
The focus should be on ways to deliver the highest quality healthcare at the most affordable prices rather than ways to provide the lowest cost healthcare regardless of the quality.
The most effective data analytics focus on a valid clinical question that is not already answered by the academic literature, are relevant to the business and promise a significant business impact, and begin with a clear idea of how the organization will use the resulting information.
3/ Learn from other industries
As health plans shift to more consumer-oriented business models, data analytics will become more important.
“Health plans need to learn to use data the same way that American Express, Disney, Harrah’s and others have,” says Jack Newsom, vice president of marketing analytics at Silverlink Communications, Inc. “This means understanding what motivates individuals and learning how to communicate with them in order to build trust and loyalty, and ultimately change behavior.”
For example, UnitedHealthcare has leveraged its data analytics in an effort to increase colorectal cancer screening rates among minority populations. This effort included analyzing the screening rates among 500,000 plan members in different ethnic groups to identify barriers to screening and to determine the most effective methods of encouraging specific groups to complete recommended screenings. Based on the results, UnitedHealthcare created customized outreach programs to increase screening rates. The analysis found that a phone call from a plan representative to one group of men increased cancer screening nearly 11% compared to another group who received a recorded call.