Evolutionary Game Model for Blockchain EMR Sharing

Researchers have proposed a theoretical framework designed to address the dynamics of Electronic Medical Record (EMR) sharing within the healthcare sector. The study, published in the journal Cluster Computing, introduces a blockchain-based incentive mechanism that utilizes evolutionary game theory to model the interactions among patients, medical institutions, and data consumers. The primary objective of this research is to promote the efficient flow of medical data while simultaneously ensuring the privacy and security of sensitive health information, a critical challenge in the ongoing advancement of healthcare informatization.

The proposed system is built upon a consortium blockchain architecture, which serves as the foundational infrastructure for the EMR sharing environment. Within this framework, the researchers developed an evolutionary game model that systematically analyzes the behavioral interactions and strategy evolution of the three key stakeholders involved in the process. The model specifically examines the stages of EMR authorization, sharing, and usage, providing a structured approach to understanding how these parties make decisions in a multi-party collaborative setting.

In the model, patients are the first actors to make a strategic decision, determining whether to authorize the use of their electronic medical records. This authorization is a prerequisite for any subsequent sharing activities. Once a patient has granted authorization, medical institutions evaluate their own costs and benefits to decide whether to share the EMRs with other parties. This step involves medical institutions evaluating their own costs and benefits to determine whether to share EMRs.

Data consumers, the third group of stakeholders, then choose between compliant and non-compliant usage strategies for the shared records. Data consumers choose between compliant and non-compliant usage strategies for the shared records. To enhance the overall performance of the system and encourage desirable behaviors, the researchers introduced a behavior-based incentive mechanism that incorporates both rewards and penalties. The mechanism incorporates both rewards and penalties to enhance system performance.

The core of the analytical approach involves the construction of replicator dynamic equations, which are used to examine the strategy evolution trajectories of the stakeholders over time. By applying these equations, the researchers were able to identify the Evolutionarily Stable Strategies (ESS) for each party under conditions of long-term interaction. The identification of these stable strategies is part of the analysis of strategy evolution trajectories under long-term interactions.

Simulation results from the study indicate that the proposed incentive mechanism can effectively influence the strategic choices of patients, healthcare institutions, and data consumers. The simulations demonstrate that the mechanism guides the strategies of these parties toward stability, suggesting that the framework could provide a theoretical basis for promoting the stable operation of the EMR sharing system.

Subject of Research: Blockchain-based evolutionary game model for multi-party Electronic Medical Record sharing

Article Title: Blockchain-based incentive mechanism for multi-party Collaborative EMR sharing: an evolutionary game approach

Article References: Zhu, D., Zhou, Z., Chen, Y., Zhang, H., Li, Y., & Zheng, J. (2026). Blockchain-based incentive mechanism for multi-party Collaborative EMR sharing: an evolutionary game approach. Cluster Computing, 29(16), Article 932. https://doi.org/10.1007/s10586-026-06743-0

Image Credits: AI Generated

DOI: 10.1007/s10586-026-06743-0

Keywords: blockchain, electronic medical records, evolutionary game theory, healthcare data sharing, incentive mechanism, consortium blockchain, privacy and security, strategy evolution, Blockchain-based, incentive, mechanism, multi-party