In this Data Report, Softheon shares member behavior indicative of future coverage lapses. Health plans will learn about the most influential factors for identifying members at-risk for coverage lapse and suggested retention strategies.
Softheon Report Executive Summary:
- To better understand ACA enrollment trends, Softheon created a predictive model using machine learning (ML) to analyze its 2021 billing and enrollment data, pinpointing key risk factors for individuals.
- The predictive model focused on the payment behaviors and enrollment data likely to result in a lapse due to non-payment, predicting member terminations with 80% or greater accuracy across multiple testing scenarios.
- This insight will provide health plans with early indications as to who might lapse on their health coverage and offer the opportunity to implement retention strategies for at-risk members.