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基于区块链的电子病历共享平台激励机制:一种演化博弈方法。

Blockchain-Based Incentive Mechanism for Electronic Medical Record Sharing Platform: An Evolutionary Game Approach.

作者信息

Zhu Dexin, Li Yuanbo, Zhou Zhiqiang, Zhao Zilong, Kong Lingze, Wu Jianan, Zhao Jian, Zheng Jun

机构信息

School of Cyberspace Science and Technology, Beijing Institute of Technology, Beijing 100081, China.

College of Computer Science and Technology, Changchun University, Changchun 130022, China.

出版信息

Sensors (Basel). 2025 Mar 19;25(6):1904. doi: 10.3390/s25061904.

Abstract

As the medical information systems continue to develop, the sharing of electronic medical records (EMRs) is becoming a vital tool for improving the quality and efficiency of medical services. However, during the process of sharing EMRs, establishing mutual-trust relationships and increasing users' participation are urgent problems to be solved. Current solutions mainly focus on incentive mechanisms for users' honest and active participation, but often ignore the potential impact of research institutions' behavior on users' trust and participation. To address this, this paper proposes an incentive mechanism based on evolutionary game theory. It combines the unchangeable nature of blockchain and the dynamic adjustment characteristics of evolutionary games to build a secure and trustworthy incentive system. This system considers the potential malicious behaviors of both users and research institutions, encouraging research institutions to protect users' privacy, reduce users' concerns, and guide users to actively contribute data. At the same time, it ensures data security and system trust through clear rewards and punishments. Based on this, we have carried out a comprehensive simulation using game theory. The results confirm that our designed incentive mechanism can effectively achieve its expected goals.

摘要

随着医疗信息系统的不断发展,电子病历(EMR)的共享正成为提高医疗服务质量和效率的重要工具。然而,在电子病历共享过程中,建立互信关系和提高用户参与度是亟待解决的问题。当前的解决方案主要侧重于激励用户诚实主动参与的机制,但往往忽视了研究机构行为对用户信任和参与度的潜在影响。为解决这一问题,本文提出一种基于演化博弈论的激励机制。它将区块链不可篡改的特性与演化博弈的动态调整特性相结合,构建一个安全可信的激励系统。该系统考虑了用户和研究机构双方的潜在恶意行为,鼓励研究机构保护用户隐私,减少用户顾虑,并引导用户积极贡献数据。同时,通过明确的奖惩措施确保数据安全和系统可信度。基于此,我们运用博弈论进行了全面的模拟。结果证实,我们设计的激励机制能够有效实现预期目标。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/71bc/11946677/1758123a73ac/sensors-25-01904-g001.jpg

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