Faculty of Law, Cheng Yu Tung Tower, Centennial Campus, The University of Hong Kong, Pokfulam, Hong Kong Special Administrative Region, China.
Department of International Health, Johns Hopkins Bloomberg School of Public Health, Baltimore, United States of America.
Bull World Health Organ. 2020 Apr 1;98(4):263-269. doi: 10.2471/BLT.19.234732. Epub 2020 Feb 25.
Technological advances in big data (large amounts of highly varied data from many different sources that may be processed rapidly), data sciences and artificial intelligence can improve health-system functions and promote personalized care and public good. However, these technologies will not replace the fundamental components of the health system, such as ethical leadership and governance, or avoid the need for a robust ethical and regulatory environment. In this paper, we discuss what a robust ethical and regulatory environment might look like for big data analytics in health insurance, and describe examples of safeguards and participatory mechanisms that should be established. First, a clear and effective data governance framework is critical. Legal standards need to be enacted and insurers should be encouraged and given incentives to adopt a human-centred approach in the design and use of big data analytics and artificial intelligence. Second, a clear and accountable process is necessary to explain what information can be used and how it can be used. Third, people whose data may be used should be empowered through their active involvement in determining how their personal data may be managed and governed. Fourth, insurers and governance bodies, including regulators and policy-makers, need to work together to ensure that the big data analytics based on artificial intelligence that are developed are transparent and accurate. Unless an enabling ethical environment is in place, the use of such analytics will likely contribute to the proliferation of unconnected data systems, worsen existing inequalities, and erode trustworthiness and trust.
大数据（来自许多不同来源的大量高度多样化的数据，可以快速处理）、数据科学和人工智能方面的技术进步可以改善卫生系统的功能，促进个性化护理和公共利益。然而，这些技术不会取代卫生系统的基本组成部分，例如道德领导力和治理，也不会避免对强大的道德和监管环境的需求。在本文中，我们讨论了在医疗保险中进行大数据分析时可能需要什么样的强大道德和监管环境，并描述了应该建立的保障措施和参与机制的例子。首先，需要建立一个清晰有效的数据治理框架。需要制定法律标准，并鼓励和激励保险公司在设计和使用大数据分析和人工智能时采取以人为中心的方法。其次，需要建立一个明确的问责制流程，以解释可以使用哪些信息以及如何使用这些信息。第三，应该通过让数据主体积极参与决定如何管理和治理他们的个人数据，来赋予他们权力。第四，保险公司和治理机构（包括监管机构和政策制定者）需要共同努力，确保基于人工智能的大数据分析具有透明度和准确性。除非建立了有利的道德环境，否则此类分析的使用可能会导致无关联的数据系统泛滥，加剧现有的不平等现象，并侵蚀可信度和信任。
