Using risk modeling to improve patient outcomes

This post is sponsored by LexisNexis Risk Solutions.
Kathy Mosbaugh
Health plans are looking to integrate risk modeling and other stratification methodologies to enable better care coordination between members and providers improve health outcomes and reduce costs.
In this post, we hear from Kathy Mosbaugh, VP and General Manager of Clinical Analytics for LexisNexis Health Care. Mosbaugh leads strategy and business operations for the clinical business, which focuses on population health management and provider performance analytics.
Question: What is driving the need for various risk modeling methodologies, and to which populations do they apply?
Kathy Mosbaugh: Risk modeling is key to proactively managing the known as well as the unforeseen health risks of member populations. In light of where the health care industry sits today, employing risk modeling or stratification methodologies has become a top priority for all at-risk organizations. We see three main drivers for this push: 1) The need and desire to improve quality both in terms of care coordination and delivery by identifying avoidable and unknown risks and engaging the right members at the right time; 2) Containing costs by deploying care management resources effectively and ensuring the right treatment plans are developed and the appropriate level of member engagement is achieved; and 3) Uncovering opportunities to enhance revenue by driving efficiencies across the health care ecosystem.
Q: Why don’t revenue enhancement approaches work for cost containment?
KM: Before you deploy a methodology, it’s important to understand the purpose for which it was created. Risk …