Pagán José A, Wang Vivian Hsing-Chun, Sur Hannah
Department of Public Health Policy and Management, School of Global Public Health, New York University, New York, NY 10003, United States.
Center for Population and Health Services Research, Department of Foundations of Medicine, NYU Grossman Long Island School of Medicine, Mineola, NY 11501, United States.
Health Aff Sch. 2025 Aug 9;3(8):qxaf158. doi: 10.1093/haschl/qxaf158. eCollection 2025 Aug.
Large-scale biobank databases link data from thousands of participants and multiple sources such as electronic health records, surveys, imaging, and biomarkers. These data infrastructure research initiatives have the potential to inform health care and public health solutions that can address the needs of different populations, facilitate cross-sector collaboration, and improve the overall responsiveness of health care and public health systems. Despite this potential, large-scale biobanks are severely underutilized to answer health policy and public health research questions. Part of the reason is that information available in these data sources is usually collected for purposes such as improving health care within an integrated health care delivery system or advancing precision medicine. Still, the depth and breadth of these data collection efforts around the world together with advances in data science and artificial intelligence provide new opportunities to answer important health policy and public health questions using large-scale biobank data.
大规模生物样本库数据库将数千名参与者的数据与多个来源的数据相链接，这些来源包括电子健康记录、调查、成像和生物标志物等。这些数据基础设施研究计划有潜力为医疗保健和公共卫生解决方案提供信息，以满足不同人群的需求，促进跨部门合作，并提高医疗保健和公共卫生系统的整体响应能力。尽管有这种潜力，但大规模生物样本库在回答卫生政策和公共卫生研究问题方面的利用率严重不足。部分原因是这些数据源中可用的信息通常是为了诸如在综合医疗保健提供系统内改善医疗保健或推进精准医学等目的而收集的。不过，世界各地这些数据收集工作的深度和广度，再加上数据科学和人工智能的进步，为利用大规模生物样本库数据回答重要的卫生政策和公共卫生问题提供了新机会。
