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.

翻译：在数字健康与人工智能时代，患者数据隐私管理日趋复杂，对全球健康公平与患者信任产生深远影响。本文提出一种新颖的健康数据治理"隐私梯度"方法，相较于传统的二元隐私模型，该框架更具细微差异性与适应性。我们的多维概念综合考虑了数据敏感性、利益相关者关系、使用目的及时间维度等因素，能够实现情境感知的隐私保护。通过政策分析、伦理考量以及涵盖青少年健康、整合照护与基因组研究等领域的案例研究，我们论证了该方法如何应对全球多样化医疗场景中的关键隐私挑战。隐私梯度模型有望在保障个体隐私权利的同时，提升患者参与度、改善照护协调性并加速医学研究进展。我们结合该方法对医疗系统、研究基础设施及全球健康倡议的影响，提出了实施该方法的政策建议。本研究旨在为政策制定者、医疗健康领导者及数字健康创新者提供参考，助力构建数字时代更公平、可信且高效的全球健康数据生态系统。