Mitigating Risk Selection in Healthcare Entitlement Programs: A Beneficiary-Level Competitive Bidding Approach

Daniel Montanera (), Abhay Nath Mishra () and T. S. Raghu ()
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Daniel Montanera: Department of Economics, Seidman College of Business, Grand Valley State University, Grand Rapids, Michigan 49504
Abhay Nath Mishra: Department of Information Systems & Business Analytics, Debbie and Jerry Ivy College of Business, Iowa State University, Ames, Iowa 50011
T. S. Raghu: W. P. Carey School of Business, Department of Information Systems, Arizona State University, Tempe, Arizona 85287

Information Systems Research, 2022, vol. 33, issue 4, 1221-1247

Abstract: Healthcare entitlement programs in the United States represent a large and growing financial outlay for taxpayers. In the pursuit of operational efficiencies, program administrators often contract with private managed care organizations (MCOs) to procure insurance for beneficiaries. This, however, encourages MCOs to attract the healthiest beneficiaries and avoid the sickest, a phenomenon known as risk selection. This paper investigates whether risk selection can be mitigated with a mechanism where MCOs bid to enroll each individual beneficiary. Although procurement auctions have been studied extensively in the literature, extant research has rarely discussed individual-level bidding. Digitization can contribute to the development and introduction of efficient market structures and mechanisms for matching beneficiaries with appropriate MCOs. We model demand- and supply-side aspects in a two-sided insurance marketplace to examine three mechanisms, risk adjustment, bidding, and a mix of prospective payment and bidding, with and without reserve prices. Analytical results show that traditional risk adjustment cannot optimally be used to eliminate risk selection, whereas the bidding mechanisms eliminate it entirely. Mixed bidding eliminates risk selection at a strictly lower cost than pure bidding. The proposed mixed bidding approach is a new type of mechanism with preauction offers that strictly dominates the second-price auction without requiring additional assumptions. Numerical analysis shows bidding dominates risk adjustment in 75.1% of simulated parameter sets. Compared with risk adjustment, bidding secures coordinated care for 12.1% more allocated beneficiaries while lowering program costs by 9.2% and largely preserving MCO profits. This would amount to approximately $27.2 billion in Medicaid program savings. Sensitivity analysis reveals that the proposed bidding mechanism dominates in scenarios that closely resemble real-world healthcare entitlement environments. These results show that digital markets that enable individual-level auctions are a promising approach for achieving the dual aim of financial sustainability and expanded access to care for the most vulnerable.

Keywords: risk selection; mechanism design; digital markets; auction markets; electronic procurement; entitlement programs; health insurance marketplaces; game theory; simulations (search for similar items in EconPapers)
Date: 2022
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