Outcome-Based Reimbursement: The Solution to High Drug Spending?

Abstract

Problem definition: The continuously soaring prices of new drugs and their uncertain effectiveness in clinical practice have put substantial risks on insurers/payers. To induce insurer coverage of their new drugs, manufacturers start to propose an innovative outcome-based reimbursement (OBR) scheme under which manufacturers refund insurers (and possibly patients) if the drugs fail to achieve a prespecified treatment target. We investigate the impact of OBR on insurers, manufacturers, and patients. Academic/practical relevance: Although OBR sounds intuitively appealing, its true impact is under much debate and depends particularly on the design of OBR. Our study sheds light on the optimal design of OBR and the debate around OBR, considering key trade-offs and key elements not covered in prior literature. Methodology: We develop a Stackelberg game under which the manufacturer designs a rebate scheme for its drug, either non-OBR or OBR, considering the trade-off between a favorable formulary position and the rebate provided. The insurer subsequently determines its formulary for the drug as well as other alternative drugs within the same disease category considering the trade-off between its spending and patient health benefits. Using data on 14 drugs treating a common disease, hyperlipidemia, we estimate through a multinomial logit model the demand of the 14 drugs and conduct counterfactual analyses on the impact of OBR. Results: Under the optimal OBR, the manufacturer lowers the insurer’s risk but inflates the wholesale price (hence, may not reduce insurer spending). OBR also induces a better formulary position for the manufacturer, which, hence, improves patient access to new drugs. Further, rebates to the insurer and patients affect demand through different mechanisms. Including patient rebates in OBR lowers patient expenses and increases drug demand but further increases insurer spending. Managerial implications: We demonstrate the structure of an optimal formulary and its application in practice. We caution insurers/payers who are seeking OBR to reduce their spending.

Product‐line pricing with dual objective of profit and consumer surplus

“…Another salient example is healthcare products or services. For healthcare firms/organizations such as insurance firms, profit/cost and consumer surplus/benefit are both important objectives (Xu et al., 2022). Both the intrinsic quality and the price of the product/service impact consumer surplus/satisfaction, affecting not only their current choice but also their propensity for future product/service choices.…”

“…Xu et al (2022) consider the optimal formulary design for prescription drugs and utilize an objective function that is a weighted sum of insurer spending and the patients' health benefit. They derive the optimal formulary decision that is an extension of the conventional assortment decision, and like Sumida et al (2021), pricing related decisions (e.g., choice of different co-payment levels) in Xu et al (2022) are discrete. There is no known work on continuous pricing under the logit choice models with the weighted objective of profit and consumer surplus.…”

“…Xu et al. (2022) consider the optimal formulary design for prescription drugs and utilize an objective function that is a weighted sum of insurer spending and the patients' health benefit. They derive the optimal formulary decision that is an extension of the conventional assortment decision, and like Sumida et al.…”

“…(2021), pricing related decisions (e.g., choice of different co‐payment levels) in Xu et al. (2022) are discrete. There is no known work on continuous pricing under the logit choice models with the weighted objective of profit and consumer surplus.…”

Please don't make me wait! Influence of customers' waiting preference and no‐show behavior on appointment systems

“…Let $$\sigma _{{\rm ES}}^{e}$$ ($$\sigma _{{\rm ES}}^{l}$$) denote the standard deviation of waiting time in the earlier (later) appointments. Following the literature (e.g., Ching & Lim, 2020; Erdem & Keane, 1996; Xu et al., 2022), we adopt the mean‐variance approach to construct consumers' appointment preference based on a multinomial logit choice model. Specifically, consumers' utility of choosing an earlier or later appointment is decided by the expected waiting time and the uncertainty of waiting, denoted as $$v_{{\rm ES}}^{j} = -t_{{\rm ES}}^{j} + \beta \sigma _{{\rm ES}}^{j}$$, where β is the parameter determining customers' risk attitude and $$j \in \lbrace e, l\rbrace$$.…”