The Impact of Transparent Use on Public Trust in the Utilization of Healthcare Data: An Evolutionary Game Analysis Perspective

Dandan Wang, Hao Li and Shicheng Xie

Discrete Dynamics in Nature and Society, 2026, vol. 2026, 1-16

Abstract: Amid rapid digital transformation and accelerated AI advancements, healthcare data utilization has gained global prominence. Empirical evidence demonstrates that public trust is pivotal to sustainable healthcare data sharing, with transparency being its key determinant. This study investigates the transparency-trust relationship through evolutionary game theory. A tripartite stochastic evolutionary game model is developed, incorporating Gaussian white noise to capture real-world complexity. Through analyzing interactions among data platforms, users, and patients, four key findings emerge: (1) Enhancing the regulatory efficiency and penalty intensity of data platforms can drive the system towards an effective equilibrium. (2) Both external regulatory pressure and long-term trust benefits are influential factors that encourage data users to choose TU. (3) The dynamic feedback mechanism of “risk speculation—patient withdrawal—strategy shift—system equilibrium†serves as the intrinsic driving force for the system’s self-repair and stability. (4) The intrinsic motivation for patients to choose participation is multidimensional; both privacy and security protection and the benefits derived from data sharing are influential factors driving patients to adopt PA.

Date: 2026
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Persistent link: https://EconPapers.repec.org/RePEc:hin:jnddns:6615770

DOI: 10.1155/ddns/6615770

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