2021
DOI: 10.1287/mnsc.2019.3574
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Outcome-Based Pricing for New Pharmaceuticals via Rebates

Abstract: The price of new brand-name prescription drugs has been rising fast in the United States. For example, the Amgen cholesterol drug Repatha had an initial list price of $14,523 per year. Patients, even with insurance coverage, must pay out of pocket a significant portion of this price. The treatment might not be successful, and this possibility reduces risk-sensitive patients’ incentives to purchase the drug. The high price together with the chance of negative treatment outcomes may lead payers to deny coverage … Show more

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Cited by 22 publications
(11 citation statements)
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“…Other research in this stream focuses on examining the performance of outcome-based payment in the context of healthcare services, such as referrals and outpatient medical services (Andritsos andTang 2018, Adida andBravo 2019). In a recent healthcare operations paper, Adida (2021) analyzes the effect of outcome-based pricing on patients' welfare, payer's payoff, and pharmaceutical firm's profit. She finds that the pharmaceutical firm and payer cannot simultaneously benefit from outcomebased pricing.…”
Section: Outcome-based Payment Schemesmentioning
confidence: 99%
“…Other research in this stream focuses on examining the performance of outcome-based payment in the context of healthcare services, such as referrals and outpatient medical services (Andritsos andTang 2018, Adida andBravo 2019). In a recent healthcare operations paper, Adida (2021) analyzes the effect of outcome-based pricing on patients' welfare, payer's payoff, and pharmaceutical firm's profit. She finds that the pharmaceutical firm and payer cannot simultaneously benefit from outcomebased pricing.…”
Section: Outcome-based Payment Schemesmentioning
confidence: 99%
“…In these studies, the input–output relationship is often modeled at a high level with time aggregation, without the detailed structures to enable decision optimization. In the second stream of healthcare research, analytical models are formulated and techniques including stochastic program, approximate dynamic program, Bayesian dynamic program, queuing analysis, and equilibrium analysis are used to derive the decisions (recent examples include Adida, 2021; Ahuja et al., 2021; Ata et al., 2020; Bavafa et al., 2022; Carew et al., 2021; Slaugh et al., 2018; Tian et al., 2022). In these studies, data are often not explicitly modeled as an input to the decision models, though some demonstrate how to apply the model to real data (e.g., Aswani et al., 2019).…”
Section: Healthcare Operationsmentioning
confidence: 99%
“…To model the patient's purchasing decision, we consider a utility function that is based on the Willingness‐To‐Pay model with uniform valuation (e.g., Adida, 2021; Yu et al., 2018). If the off‐label drug (or on‐label drug if there is drug conversion) is successful, patients live and enjoy an “extra” utility V .…”
Section: The Modelmentioning
confidence: 99%
“…To capture patient heterogeneity, we assume V is a random variable uniformly distributed in the range false[0,truev¯false]$[0,\bar{v}]$ (i.e., VUfalse[0,truev¯false]$V \sim U [0,\bar{v}]$). The realization of V may depend on several factors, such as illness severity, budget of patients, opportunity cost of being ill, demographics, and remaining quality‐adjusted life years (Adida, 2021). For any realized value of V , the patient decides to purchase the off‐label drug (or on‐label drug if there is drug conversion) by taking the effective rate enormalf${e_{\text{f}}}$ (or enormaln${e_{\text{n}}}$) and the selling price pnormalf${p_{\text{f}}}$ (or pnormaln${p_{\text{n}}}$) into consideration.…”
Section: The Modelmentioning
confidence: 99%