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 proposition. With Reference Pricing (RP), a payer or insurer determines a maximum amount paid for a procedure, and patients who select a provider charging more pay the difference. In a Tiered Network (TN), providers are stratified according to a set of criteria (such as quality, cost and sometimes location) and each tier corresponds to a different out-of-pocket price. Motivated by a recent CalPERS program, we design two MIP optimization models for payers that combine both RP and TN, filling the gap of quantitative research on these novel payment policies. The main decision variables are, for each provider, whether he is exempted from the reference pricing scheme, so that his patients will face no out-of-pocket costs, no matter his price. The objective is to minimize total payer’s cost, while problem constraints provide the decision maker with levers for a trade-off between cost reduction and patients’ satisfaction. We also build robust counterparts for both models in order to account for parameter uncertainty. Numerical experiments provide insights into how the tiers are scattered on a cost/quality plane. We argue that this system has strong potential in terms of costs reduction for public or private payers, quality increase for patients and visibility for high-value providers. ∗Doctoral Candidate, ESSEC Business School, [email protected] †Professor, Department of Information Systems, Decision Sciences and Statistics, ESSEC Business School, [email protected] ‡Associate Professor, Department of Industrial and Systems Engineering, Lehigh University, [email protected]