Data-driven incentive mechanism design for chronic disease prevention from the perspective of government

• Published in: European journal of operational research Vol. 313; no. 2; pp. 652 - 668
• Main Authors: Sun, Huan, Wang, Haiyan
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 01.03.2024
• Subjects: Asymmetric information; Community chronic disease prevention; Government incentives; Mechanism design; Or in health services; Asymmetric information; Or in health services; Government incentives; Mechanism design; Community chronic disease prevention
• DOI: 10.1016/j.ejor.2023.09.005
• ISSN: 0377-2217

•We give a new mechanism that changed the inefficiency of chronic prevention.•Our mechanism aligns everyone's interests and helps patients report truthfully.•We use data to drive the whole process of modelling for the practical prevention.•We prove and verify the robustness of our mechanism.•We give a new regression method that identifies and screens patients’ concealment. Current government subsidies on prevention fail to effectively incentive primary care providers and patients with chronic diseases, resulting in ineffective prevention. It hinders the shift from “the treatment-based” to “the combination of prevention and treatment” philosophy for chronic diseases. To encourage primary care provider and patients with chronic diseases to make better prevention efforts, a sequential game model for government-primary care provider-patients is developed. Using mechanism design theory, an incentive mechanism for chronic disease prevention is proposed. This mechanism aims to increase patients’ motivation for prevention and ensure truthful reporting of their prevention efforts. Moreover, by integrating data from a primary care provider in Jiangsu Province, a closed loop with problem analysis-model construction-results testing is established. Specifically, with the data, the severity of prevention inefficiencies is quantified, the key functions required for modelling are identified with a new and identifiable regression method. Furthermore, the proposed mechanism is compared to the original mechanism in terms of its impact on the government, primary care provider, and patients.The comparison demonstrates the superiority of the proposed mechanism. This study reveals that the mechanism aligns the interests of all subjects by redistributing government subsidies between the primary care provider and the patients. The mechanism adjusts the primary care provider's authority to use the left subsidies. Besides, it enables the primary care provider to actively monitor and motivate patients to take further prevention action. Meanwhile,it successfully makes patients increase their prevention efforts and report them truthfully. Finally,the robustness of the model has also been demonstrated and verified.