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Evolutionary game analysis of healthcare data governance in China's experimental data trading pilots.

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Titel: Evolutionary game analysis of healthcare data governance in China's experimental data trading pilots.
Autoren: Yao, Han, Liu, Yuanli
Quelle: NPJ Digital Medicine; 9/1/2025, Vol. 8 Issue 1, p1-17, 17p
Schlagwörter: POLICY sciences, STATISTICAL models, AUDITING, DATA management, RESEARCH funding, DATA analysis, CLINICAL governance, DIGITAL health, HIGH performance computing, INTERVIEWING, PRIVACY, DECISION making, INFORMATION resources, DESCRIPTIVE statistics, HEALTH planning, SURVEYS, CONCEPTUAL structures, HEALTH care industry, STAKEHOLDER analysis, DATA analysis software, ALGORITHMS, MEDICAL ethics, GOVERNMENT regulation
Geografische Kategorien: CHINA
Abstract: This study constructs a tripartite evolutionary game model involving healthcare data management authorities (DMAs), healthcare data operating departments (DODs), and data-related entities (DEs) within a triple principal-agent framework. We analyze dynamic interactions among these stakeholders in healthcare data governance, focusing on privacy security, moral hazard, and interest alignment. Results indicate that strategic instability arises under conditions of ambiguous data property rights and asymmetric risk responsibilities. However, the system converges to compliance-oriented equilibria when critical thresholds are surpassed. Notably, DMAs' strong incentive strategies are pivotal in resolving regulatory paradoxes, while DODs' risk behaviors exhibit nonlinear sensitivity to penalty intensity and revenue levels. We further demonstrate that a dual "penalty-compensation" mechanism mitigates economic losses from data breaches, and synergistic government reputation mechanisms with financial incentives reduce regulatory costs. Policy implications include a tiered dynamic regulatory system, a revenue-risk linked distribution mechanism, and a collaborative governance ecosystem driven by technology and credibility constraints. [ABSTRACT FROM AUTHOR]
Copyright of NPJ Digital Medicine is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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  Data: This study constructs a tripartite evolutionary game model involving healthcare data management authorities (DMAs), healthcare data operating departments (DODs), and data-related entities (DEs) within a triple principal-agent framework. We analyze dynamic interactions among these stakeholders in healthcare data governance, focusing on privacy security, moral hazard, and interest alignment. Results indicate that strategic instability arises under conditions of ambiguous data property rights and asymmetric risk responsibilities. However, the system converges to compliance-oriented equilibria when critical thresholds are surpassed. Notably, DMAs' strong incentive strategies are pivotal in resolving regulatory paradoxes, while DODs' risk behaviors exhibit nonlinear sensitivity to penalty intensity and revenue levels. We further demonstrate that a dual "penalty-compensation" mechanism mitigates economic losses from data breaches, and synergistic government reputation mechanisms with financial incentives reduce regulatory costs. Policy implications include a tiered dynamic regulatory system, a revenue-risk linked distribution mechanism, and a collaborative governance ecosystem driven by technology and credibility constraints. [ABSTRACT FROM AUTHOR]
– Name: Abstract
  Label:
  Group: Ab
  Data: <i>Copyright of NPJ Digital Medicine is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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        Value: 10.1038/s41746-025-01950-2
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      – Code: eng
        Text: English
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      – SubjectFull: CHINA
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      – TitleFull: Evolutionary game analysis of healthcare data governance in China's experimental data trading pilots.
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              Text: 9/1/2025
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