Huang Xinping, Zhu Siyuan, Lv Zheng, Zhou Qianwen, Kou Tianqi
School of Business and Management, Jilin University, Changchun, China.
National Intelligent Social Governance Research Institute of Jilin University, Jilin University, Changchun, China.
Digit Health. 2025 Jun 25;11:20552076251353694. doi: 10.1177/20552076251353694. eCollection 2025 Jan-Dec.
In the evolving digital healthcare landscape, the strategic exploitation of open health data has become increasingly pivotal. The open sharing of health data is essential due to its irreplaceable role in digital health care. It is made feasible by the development of emerging technologies and the support of national policies. However, in practice, health data systems continue to face challenges such as data fragmentation, uneven data quality, poor platform connectivity, and a data regulatory vacuum, all of which hinder the effective integration of healthcare resources. This study proposes a health data governance framework to align stakeholder incentives, clarify operational responsibilities, and facilitate the compliant sharing and reuse of health data.
Innovatively employing grounded theory, this study meticulously examines the critical elements that influence health data sharing and provides a comprehensive explanation of its operational mechanisms through the Interpretive Structural Modeling - Cross-Impact Matrix Multiplication Applied to Classification model, delves into the operational mechanisms from three perspectives: the interest-driven and coordinated actions of multiple stakeholders, the open flow and secure sharing of the data ontology, and the internal support and external guarantees provided by the environmental context.
This study proposes a structured data governance framework, establishing a health data sharing system built upon four core components: top-level guidance, collaborative governance, technological empowerment, and rights protection. By standardizing data quality assessment, enhancing platform interoperability, unifying data exchange protocols, and addressing critical regulatory gaps, the framework offers a systematic solution to the key challenges associated with open health data sharing.
This study advocates an open health data-sharing framework characterized by "top-level guidance, collaborative governance, technological empowerment, and robust rights protection." Such a framework will optimize the economic and social value of open health data, address existing challenges, and harness future opportunities to create a more inclusive and efficient healthcare system.
在不断发展的数字医疗格局中，对开放健康数据进行战略利用变得越来越关键。健康数据的开放共享至关重要，因为其在数字医疗中具有不可替代的作用。新兴技术的发展和国家政策的支持使其成为可能。然而，在实践中，健康数据系统仍面临数据碎片化、数据质量参差不齐、平台连通性差以及数据监管真空等挑战，所有这些都阻碍了医疗资源的有效整合。本研究提出了一个健康数据治理框架，以协调利益相关者的激励措施，明确运营责任，并促进健康数据的合规共享和再利用。
本研究创新性地运用扎根理论，细致考察影响健康数据共享的关键要素，并通过解释结构模型-交叉影响矩阵乘法应用于分类模型对其运行机制进行全面解释，从三个角度深入探讨运行机制：多个利益相关者的利益驱动与协同行动、数据本体的开放流动与安全共享以及环境背景提供的内部支持和外部保障。
本研究提出了一个结构化的数据治理框架，建立了一个基于四个核心组件的健康数据共享系统：顶层指导、协同治理、技术赋能和权益保护。通过规范数据质量评估、增强平台互操作性、统一数据交换协议以及解决关键监管漏洞，该框架为与开放健康数据共享相关的关键挑战提供了系统解决方案。
本研究倡导一个以“顶层指导、协同治理、技术赋能、有力权益保护”为特征的开放健康数据共享框架。这样一个框架将优化开放健康数据的经济和社会价值，应对现有挑战，并利用未来机遇创建一个更具包容性和高效的医疗体系。
