提出健康数据隐私梯度模型，实现更灵活的隐私保护。
The Gradient of Health Data Privacy
- 用多维因素构建隐私梯度，替代二元隐私模式。
- 在青少年健康、整合医疗等场景中验证有效性。
- 适合政策制定者与数字健康从业者参考。
在数字健康与人工智能时代，患者数据隐私管理日益复杂，影响全球健康公平与患者信任。本文提出一种新的“隐私梯度”方法，相较于传统二元隐私模型，提供更细致、自适应的治理框架。该多维概念涵盖数据敏感性、利益相关者关系、使用目的和时间维度，支持情境化隐私保护。通过政策分析、伦理探讨及涵盖青少年健康、整合照护与基因组研究的案例研究，证明该方法可有效应对全球多样医疗场景中的关键隐私挑战。隐私梯度模型有望提升患者参与度、改善照护协调、加速医学研究，同时保障个人隐私权利。本文提出实施建议，考虑其对医疗体系、研究基础设施与全球健康倡议的影响。本研究旨在为政策制定者、医疗领导者与数字健康创新者提供参考，推动建立更公平、可信、高效的全球健康数据生态。
原文摘要 · Abstract (English)
In the era of digital health and artificial intelligence, the management of patient data privacy has become increasingly complex, with significant implications for global health equity and patient trust. This paper introduces a novel "privacy gradient" approach to health data governance, offering a more nuanced and adaptive framework than traditional binary privacy models. Our multidimensional concept considers factors such as data sensitivity, stakeholder relationships, purpose of use, and temporal aspects, allowing for context-sensitive privacy protections. Through policy analyses, ethical considerations, and case studies spanning adolescent health, integrated care, and genomic research, we demonstrate how this approach can address critical privacy challenges in diverse healthcare settings worldwide. The privacy gradient model has the potential to enhance patient engagement, improve care coordination, and accelerate medical research while safeguarding individual privacy rights. We provide policy recommendations for implementing this approach, considering its impact on healthcare systems, research infrastructures, and global health initiatives. This work aims to inform policymakers, healthcare leaders, and digital health innovators, contributing to a more equitable, trustworthy, and effective global health data ecosystem in the digital age.
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