Optimal pricing and healthcare warranty strategies for chronic diseases. (October 2022)

• Record Type: Journal Article
• Authors: Zhao, Heng; Liu, Zixian; Li, Mei; Pei, Zhaona; Fu, Lipeng
• Is Part Of: Computers & industrial engineering. Volume 172:Part A(2022)
• Publication Date: 2022-10
• Subjects: Healthcare warranty services -- Pricing -- Follow-up strategy -- Patient heterogeneity -- Patient learning; Engineering -- Data processing -- Periodicals; Industrial engineering -- Periodicals; 620.00285
• DOI: 10.1016/j.cie.2022.108539
• Languages: English
• ISSNs: 0360-8352

Abstract

Highlights: Optimal pricing and healthcare warranty strategies of a profit maximizing healthcare provider for chronic diseases is investigated. An intensity reduction model is introduced to model the effect of follow-up checkups on the patients' risk of developing complications. Patient heterogeneity and patient learning effects are considered. A case study involving diabetes patients is presented to demonstrate the applicability of the proposed model. Abstract: Providing warranties for healthcare is a quality and cost-effective healthcare delivery model. Under a healthcare warranty, the patient is charged a lump-sum payment for the healthcare services administered during a prespecified period regardless of the actual cost incurred by the healthcare provider. Notably, such a fixed-price healthcare delivery model increases the risks of profit maximization among healthcare providers, thereby making pricing decisions crucial. In this paper, we establish an integration model to derive an optimal price and healthcare warranty strategy composed of periodic follow-up for patients with chronic diseases considering patient heterogeneity. This paper introduces the intensity reduction model to capture the effect of follow-up checkups on patients' risk of developing complications, and accounts for the effect of patients' learning effects on the risk of complications during the planning horizon. The model allows healthcare providers to make optimal pricing and follow-up decisions under different factors, including follow-up effects, patients' learning ability, and cost parameters. A case study of diabetes is presented to illustrate the application of the proposed model and analyze the impacts of significant parameters on the optimal solutions. The model and findings form the basis in making decisions for the healthcare warranty pricing of chronic diseases.