Optimizing healthcare network design under reference pricing and parameter uncertainty

Healthcare payers are exploring cost-containing policies to steer patients, through qualified information and financial incentives, towards providers offering the best value. With Reference Pricing (RP), a payer determines a maximum amount paid for a procedure, and patients selecting a provider charging more pay the difference. In a Tiered Network (TN), providers are stratified according to criteria such as quality and cost, each tier having a different out-of-pocket price. Motivated by a program recently implemented in California, we design an optimization model for payers combining both RP and TN, filling the gap of quantitative research on these novel payment policies. The main decision is to select which providers to exempt from RP, whose patients will face no out-of-pocket costs. Patients' choice of a provider is modeled with a Multinomial Logit model. The objective is to minimize the payer's cost, while constraints provide decision makers with levers for a trade-off between cost reduction and providers quality. We build a robust counterpart of our model to account for parameter uncertainty. Numerical experiments provide insights into how tiers are scattered on a price/quality plane. We argue that this system has strong potential in terms of costs reduction, quality increase for patients and visibility for high-value providers.