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建立新的英国基础设施以寻找和获取全人群 COVID-19 数据用于研究和公共卫生分析的挑战和经验教训:CO-CONNECT 项目。

The Challenges and Lessons Learned Building a New UK Infrastructure for Finding and Accessing Population-Wide COVID-19 Data for Research and Public Health Analysis: The CO-CONNECT Project.

机构信息

Population Health and Genomics, School of Medicine, University of Dundee, Dundee, United Kingdom.

Health Data Research UK, London, United Kingdom.

出版信息

J Med Internet Res. 2024 Nov 20;26:e50235. doi: 10.2196/50235.

Abstract

The COVID-19-Curated and Open Analysis and Research Platform (CO-CONNECT) project worked with 22 organizations across the United Kingdom to build a federated platform, enabling researchers to instantaneously and dynamically query federated datasets to find relevant data for their study. Finding relevant data takes time and effort, reducing the efficiency of research. Although data controllers could understand the value of such a system, there were significant challenges and delays in setting up the platform in response to COVID-19. This paper aims to present the challenges and lessons learned from the CO-CONNECT project to support other similar initiatives in the future. The project encountered many challenges, including the impacts of lockdowns on collaboration, understanding the new architecture, competing demands on people's time during a pandemic, data governance approvals, different levels of technical capabilities, data transformation to a common data model, access to granular-level laboratory data, and how to engage public and patient representatives meaningfully on a highly technical project. To overcome these challenges, we developed a range of methods to support data partners such as explainer videos; regular, short, "touch base" videoconference calls; drop-in workshops; live demos; and a standardized technical onboarding documentation pack. A 4-stage data governance process emerged. The patient and public representatives were fully integrated team members. Persistence, patience, and understanding were key. We make 8 recommendations to change the landscape for future similar initiatives. The new architecture and processes developed are being built upon for non-COVID-19-related data, providing an infrastructural legacy.

摘要

COVID-19 策划和开放分析与研究平台(CO-CONNECT)项目与英国 22 个组织合作,构建了一个联合平台,使研究人员能够即时动态地查询联合数据集,以找到其研究相关的数据。查找相关数据需要时间和精力,从而降低了研究效率。尽管数据控制者可以理解此类系统的价值,但在针对 COVID-19 建立该平台时仍面临重大挑战和延迟。本文旨在展示 CO-CONNECT 项目中遇到的挑战和经验教训,以支持未来的其他类似计划。该项目遇到了许多挑战,包括封锁对合作的影响、理解新架构、大流行期间人们时间上的竞争需求、数据治理审批、不同程度的技术能力、将数据转换为通用数据模型、访问细粒度实验室数据,以及如何在高度技术性的项目上有意义地吸引公众和患者代表。为了克服这些挑战,我们开发了一系列方法来支持数据合作伙伴,例如解释性视频;定期、简短的“保持联系”视频电话会议;随时参加的研讨会;实时演示;以及标准化的技术入职文档包。出现了一个 4 阶段的数据治理流程。患者和公众代表是完全融入团队的成员。坚持、耐心和理解是关键。我们提出了 8 条建议,以改变未来类似计划的格局。正在为非 COVID-19 相关数据构建新的架构和流程,提供基础设施遗产。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c98f/11618003/517a07b61092/jmir_v26i1e50235_fig1.jpg

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