Data-Driven Incentive Design in the Medicare Shared Savings Program
53 Pages Posted: 13 Jul 2016 Last revised: 17 Jun 2018
Date Written: June 10, 2018
The Medicare Shared Savings Program (MSSP) was created under the Patient Protection and Affordable Care Act to control escalating Medicare spending by incentivizing providers to deliver healthcare more efficiently. Medicare providers that enroll in the MSSP earn bonus payments for reducing spending to below a risk-adjusted financial benchmark that depends on the provider's historical spending. To generate savings, a provider must invest to improve efficiency, which is a cost that is absorbed entirely by the provider under the current contract. This has proven to be challenging for the MSSP, with a majority of participating providers unable to generate savings due to the associated costs. In this paper, we propose a predictive analytics approach to redesigning the MSSP contract with the goal of better aligning incentives and improving financial outcomes from the MSSP. We formulate the MSSP as a principal-agent model and propose an alternate contract that includes a performance-based subsidy to partially reimburse the provider's investment. We prove the existence of a subsidy-based contract that dominates the current MSSP contract by producing a strictly higher expected payoff for both Medicare and the provider. We then propose an estimator based on inverse optimization for estimating the parameters of our model. We use a dataset containing the financial performance of providers enrolled in the MSSP, which together accounts for 7 million beneficiaries and over $70 billion in Medicare spending. We estimate that introducing performance-based subsidies to the MSSP can boost Medicare savings by up to 40% without compromising provider participation in the MSSP. We also find that the subsidy-based contract performs well in comparison to a fully flexible, non-parametric contract.
Keywords: Health policy, contract design, principal-agent model, structural estimation, simulation
Suggested Citation: Suggested Citation