Identifying future high-risk, or rising risk, patients can help healthcare organizations reduce population health costs through early intervention. In this paper, Milliman consultants Ksenia Whittal and Abigail Caldwell examine predictive models to determine whether an enhanced model can better identify individuals with rising risk relative to traditional prospective risk adjustment models. The authors also assess the capability of these models to select members whose costs will increase in the future year.
The group life and disability insurance sector has been slower to adopt predictive analytics than other lines of insurance. One reason for the sector’s lag is because insurers often have limited information on who they are insuring. However, there are still many ways to incorporate predictive modeling technology to improve results. Milliman consultant Jennifer Fleck provides some perspective in her article “Group insurance ‘Project Insight’.”
In the world of long-term care (LTC) insurance, making financial projections is challenging for two main reasons: a long projection horizon and complex interactions. This article by Milliman actuaries Missy Gordon and Joe Long walks through the progression from developing LTC projection assumptions using traditional methods to doing so using predictive analytics.
This article was originally published in the December 2017 issue of Long-Term Care News.
In this Society of Actuaries (SOA) video, Milliman’s Shea Parkes discusses his experience learning predictive modeling through Kaggle Competitions and how he has applied what he learned to his professional work as a healthcare actuary.