Using Predictive Analytics to Avoid Unmanaged Risk

By HMS
Oct. 16, 2019

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According to the Centers for Disease Control and Prevention, the United States spends $3.3 trillion a year on healthcare and 90% of those expenses are for individuals suffering from chronic and mental health conditions. A proven way to reduce these costs is to manage chronic diseases whenever possible. Unfortunately, this is often easier said than done. Although health plans have data about their members, they may not have the analytical tools needed to understand the ever-changing risk landscape.

To Avoid Unmanaged Risk, Timing is Key

Over time, a member’s risk profile can change. For instance, individuals with hidden risks may quickly move to critical status. Alternatively, healthy patients with unknown risks may become high utilizers of the healthcare system. Identifying the inflection points when these changes occur is critical to managing risk. Health plans that predict these inflection points for members can address health risks before they gain take hold and escalate.

Predictive and prescriptive analytics enable organizations to get in front of emerging health risks. Predictive analytics leverage data to predict the likelihood of future events, while prescriptive analytics use predictive analytics to determine the next best actions.

Analytics provide visibility into the population at the time of enrollment, as well as over time. This data helps health plans create prioritized lists of members who are currently at-risk and those who are likely to experience rising health risks. Data-based insights enable organizations to proactively engage members and offer care management services.

Transforming Insight into Action

Managing member risk can be broken down into three steps:

  1. Identify members with rising risks. This is a continuous process, since low to high cost members can change every month. A risk analytics solution such as Elli from HMS can help. Elli’s predictive models leverage AI and other advanced analytical techniques to process over 1,000 predictors from claims and social determinant data. Population insights are communicated through dashboards, reports, and alerts. It’s easy to identify member cohorts who would benefit from care management programs.
  1. Address unmanaged risks through program execution. Member-specific, actionable insights make it easier for plans to understand who needs help and why, as well as what programs could help. The information in HMS’ Elli solution shows care managers what they need to know in order to have the right conversation with members at the right time and about the right things.
  1. Evaluate performance. Performance reporting is essential, since managing member risk is an ongoing process. Elli’s performance insights enable health plans to adjust their programs in ways that are most likely to move the needle in terms of lowering risks and lowering healthcare costs. Elli’s predictive models identify twice as much anticipated costs as national actuarial models.

Conclusion

Managing member risk just makes sense, but care management resources are limited. There are never enough care managers to handle the needs of every person. Fortunately, population analytics can help. Intuitive member insights and prioritized member lists mean that fewer care managers are needed to intervene and avoid costly healthcare events. Proactively closing care gaps for predicted rising risk members can also increase quality scores between 10% and 15%. To learn more about how HMS’ Elli solution for risk analytics, could help your organization, contact us.

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