Emily Jefferson, Gordon Milligan, Jenny Johnston, Shahzad Mumtaz, Christian Cole, Joseph Best, Thomas Charles Giles, Samuel Cox, Erum Masood, Scott Horban, Esmond Urwin, Jillian Beggs, Antony Chuter, Gerry Reilly, Andrew Morris, David Seymour, Susan Hopkins, Aziz Sheikh, Philip Quinlan
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引用次数: 0
摘要
COVID-19-Curated and Open Analysis and Research Platform(CO-CONNECT)项目与英国的 22 家机构合作建立了一个联合平台,使研究人员能够即时、动态地查询联合数据集,为其研究找到相关数据。查找相关数据费时费力,降低了研究效率。尽管数据控制者能够理解这样一个系统的价值,但在针对 COVID-19 建立平台的过程中却遇到了巨大的挑战和延误。本文旨在介绍 CO-CONNECT 项目所面临的挑战和汲取的经验教训,以便为今后其他类似项目提供支持。该项目遇到了许多挑战,包括封锁对合作的影响、对新架构的理解、大流行期间对人们时间的竞争性需求、数据治理审批、不同级别的技术能力、向通用数据模型的数据转换、对细粒度实验室数据的访问,以及如何让公众和患者代表有意义地参与到一个高度技术性的项目中。为了克服这些挑战,我们开发了一系列方法为数据合作伙伴提供支持,例如讲解视频、定期、简短的 "接触式 "视频会议电话、随到随学的研讨会、现场演示以及标准化的入职技术文档包。形成了 4 个阶段的数据管理流程。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 项建议,以改变未来类似计划的格局。开发的新架构和流程将用于与 COVID-19 无关的数据,从而为基础架构提供遗产。
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.
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.
期刊介绍:
The Journal of Medical Internet Research (JMIR) is a highly respected publication in the field of health informatics and health services. With a founding date in 1999, JMIR has been a pioneer in the field for over two decades.
As a leader in the industry, the journal focuses on digital health, data science, health informatics, and emerging technologies for health, medicine, and biomedical research. It is recognized as a top publication in these disciplines, ranking in the first quartile (Q1) by Impact Factor.
Notably, JMIR holds the prestigious position of being ranked #1 on Google Scholar within the "Medical Informatics" discipline.