Ros Francisco, Kush Rebecca, Friedman Charles, Gil Zorzo Esther, Rivero Corte Pablo, Rubin Joshua C, Sanchez Borja, Stocco Paolo, Van Houweling Douglas
Escuela Técnica Superior Ingenieros de Telecomunicación Universidad Politécnica de Madrid Madrid Spain.
Elligo Health Research and Catalysis Austin Texas USA.
Learn Health Syst. 2020 Dec 6;5(1):e10253. doi: 10.1002/lrh2.10253. eCollection 2021 Jan.
Covid-19 has already taught us that the greatest public health challenges of our generation will show no respect for national boundaries, will impact lives and health of people of all nations, and will affect economies and quality of life in unprecedented ways. The types of rapid learning envisioned to address Covid-19 and future public health crises require a systems approach that enables sharing of data and lessons learned at scale. Agreement on a systems approach augmented by technology and standards will be foundational to making such learning meaningful and to ensuring its scientific integrity. With this purpose in mind, a group of individuals from Spain, Italy, and the United States have formed a transatlantic collaboration, with the aim of generating a proposed comprehensive standards-based systems approach and data-driven framework for collection, management, and analysis of high-quality data. This framework will inform decisions in managing clinical responses and social measures to overcome the Covid-19 global pandemic and to prepare for future public health crises. We first argue that standardized data of the type now common in global regulated clinical research is the essential fuel that will power a global system for addressing (and preventing) current and future pandemics. We then present a blueprint for a system that will put these data to use in driving a range of key decisions. In the context of this system, we describe and categorize the specific types of data the system will require for different purposes and document the standards currently in use for each of these categories in the three nations participating in this work. In so doing, we anticipate some of the challenges to harmonizing these data but also suggest opportunities for further global standardization and harmonization. While we have scaled this transnational effort to three nations, we hope to stimulate an international dialogue with a culmination of realizing such a system.
新冠疫情已经让我们明白，我们这一代人面临的最大公共卫生挑战不会尊重国界，将影响所有国家人民的生命和健康，并以前所未有的方式影响经济和生活质量。应对新冠疫情及未来公共卫生危机所需的快速学习类型需要一种系统方法，以实现大规模的数据共享和经验教训分享。就一种由技术和标准增强的系统方法达成共识，将是使这种学习有意义并确保其科学完整性的基础。出于这一目的，一群来自西班牙、意大利和美国的人士开展了跨大西洋合作，旨在生成一个基于标准的全面系统方法和数据驱动框架，用于高质量数据的收集、管理和分析。该框架将为管理临床应对措施和社会措施的决策提供信息，以战胜新冠全球大流行并为未来公共卫生危机做好准备。我们首先认为，全球规范临床研究中常见的那种标准化数据是推动全球应对（和预防）当前及未来大流行系统的关键要素。然后，我们提出一个系统蓝图，该系统将利用这些数据推动一系列关键决策。在这个系统的背景下，我们描述并分类了系统针对不同目的所需的特定数据类型，并记录了参与这项工作的三个国家目前用于每个此类别的标准。这样做的过程中，我们预计了协调这些数据的一些挑战，但也提出了进一步全球标准化和协调的机会。虽然我们已将这项跨国工作扩展到三个国家，但我们希望激发一场国际对话，最终实现这样一个系统。
