IDENTIFYING HEALTHCARE INSURANCE PAYMENT ARBITRAGE OPPORTUNITIES USING A MACHINE LEARNING NETWORK

摘要

A computer-implemented method identifies insurance risk adjustment opportunities for healthcare expenses of healthcare insurance program enrollees. The method includes providing inputs for each enrollee to a machine learning network. The inputs may include Center for Medicare and Medicaid Services Hierarchical Condition Category (CMS-HCC) values, an enrollee claims history, and enrollee historical spending amounts. Based on the inputs, the machine learning network is trained to predict future healthcare spending for the enrollees. After training, the machine learning network identifies enrollees having predicted future healthcare spending that differs from an amount determined based on a base risk score. Upon identifying an enrollee whose predicted future spending is greater than the amount determined based on the base risk score, one or more actions are taken: (1) performing outreach to or intervention for the identified enrollee; (2) disenrolling or discouraging the identified enrollee from participating in the insurance program; and (3) capturing additional CMS-HCC values that may increase the payment amounts for the identified enrollee. Upon identifying an enrollee whose predicted future healthcare spending is less than the amount determined based on the base risk score, action is taken to retain the identified enrollee.