Adverse Selection and Provider Networks in Medicaid Managed Care: Evidence from a Large Urban Health Care Market

Monday, June 11, 2018: 3:30 PM
Oak Amphitheater - Garden Level (Emory Conference Center Hotel)

Author(s): Mark Shepard; Amanda Kreider; Timothy J. Layton; Jacob Wallace

Discussant: Joel Segel

Over the past three decades there has been a dramatic shift away from fee-for-service Medicaid programs and toward contracting out the provision of Medicaid benefits to private managed care plans. Many states have invoked the model of “managed competition,” hoping to improve efficiency by allowing managed care plans to compete for enrollees in a market. However, the managed competition model brings with it the potential for often overlooked distortions caused by adverse selection. Specifically, insurers are incentivized to design their provider networks to make their plans unattractive to Medicaid beneficiaries who have chronic conditions, such as cancer or diabetes, resulting in inefficiently limited access to the types of providers who treat those conditions.

In this paper, we study an example of this type of provider network distortion in a large, urban Medicaid Managed Care (MMC) market. Using an audited dataset of MMC plans’ provider networks, we observe a large MMC plan add a large cancer specialty hospital to its network in 2005 and subsequently remove it again in 2006. We exploit this natural experiment, using claims and enrollment data for Medicaid enrollees ages 0-64, to examine changes in plan enrollment and spending in response to the network change. We show that the inclusion of the specialty hospital in the plan’s network attracted a large group of beneficiaries with cancer, driving up the plan’s average­ enrollee cost, and we conclude that the specialty hospital may have been removed from the plan’s network due to adverse selection. These findings suggest that while MMC may bring benefits associated with competition, it may also introduce distortions related to adverse selection that limit access to specialty care.