@Article{信息:doi/10.2196/40035，作者=“杰斐逊，艾米丽和科尔，克里斯蒂安和蒙塔兹，沙赫扎德和考克斯，塞缪尔和贾尔斯，托马斯·查尔斯和阿德朱莫，山姆和厄温，埃斯蒙德和利亚，丹尼尔和麦克唐纳，卡勒姆和贝斯特，约瑟夫和马苏德，厄拉姆和米利根，戈登和约翰斯顿，珍妮和霍本，斯科特和伯塞德，伊佩克和霍尔，克里斯托弗和杰克逊，亚伦和柯林斯，克莱尔和瑞辛，山姆和多斯利，夏洛特和汉普顿，吉尔和哈德菲尔德，安德鲁和桑托斯，罗伯托和塔尔，西蒙和帕纳吉、瓦西里基和拉瓦尼亚、约瑟夫和杰克逊、特雷西和丘特、安东尼和贝格斯、吉莉安和马丁内斯-奎波、玛格达莱娜和沃德、海伦和冯·齐根威特、朱莉和伯恩斯、弗朗西斯和马丁、乔安妮和塞比、尼尔和莫里斯、卡罗尔和布拉德利、德克兰和巴克斯特、罗布和阿霍南-毕晓普、安妮和史密斯、保罗和休马克、阿米莉亚和瓦尔德斯、安娜和奥利弗、本杰明和马尼斯蒂、夏洛特和艾尔、大卫和加兰特、斯蒂芬妮和乔伊、乔治和麦考利、安德鲁和康奈尔、大卫和诺斯斯通、凯特和杰弗瑞、凯蒂和迪安吉尔安东尼奥、伊曼纽尔和麦克马洪、艾米和沃克、马特和森普尔、马尔科姆·格雷西和西姆斯、杰西卡·梅和劳伦斯、艾玛和戴维斯、贝森和贝利、约翰·肯尼斯和唐、明和利明、加里和鲍尔、琳达和布雷兹、托马斯和默里、邓肯和奥尔顿、克里斯和皮尔斯、伊恩和霍尔、伊恩和拉哈尼、沙米兹和吉尔森、娜塔莉和惠特克、马修和沙尔克罗斯、劳拉和西摩、大卫和瓦尔玛、Susheel和Reilly, Gerry和Morris, Andrew和Hopkins, Susan和Sheikh, Aziz和Quinlan, Philip”，标题=“一个混合架构(CO-CONNECT)促进快速发现和访问数据在英国应对COVID-19大流行:发展研究”，期刊=“J Med Internet Res”，年=“2022”，月=“Dec”，日=“27”，卷=“24”，数=“12”，页=“e40035”，关键词=“COVID-19;临床护理;公共卫生;基础设施模型;健康数据;荟萃分析;联合网络; health care record; data extraction; data privacy; data governance; health care", abstract="Background: COVID-19 data have been generated across the United Kingdom as a by-product of clinical care and public health provision, as well as numerous bespoke and repurposed research endeavors. Analysis of these data has underpinned the United Kingdom's response to the pandemic, and informed public health policies and clinical guidelines. However, these data are held by different organizations, and this fragmented landscape has presented challenges for public health agencies and researchers as they struggle to find relevant data to access and interrogate the data they need to inform the pandemic response at pace. Objective: We aimed to transform UK COVID-19 diagnostic data sets to be findable, accessible, interoperable, and reusable (FAIR). Methods: A federated infrastructure model (COVID - Curated and Open Analysis and Research Platform [CO-CONNECT]) was rapidly built to enable the automated and reproducible mapping of health data partners' pseudonymized data to the Observational Medical Outcomes Partnership Common Data Model without the need for any data to leave the data controllers' secure environments, and to support federated cohort discovery queries and meta-analysis. Results: A total of 56 data sets from 19 organizations are being connected to the federated network. The data include research cohorts and COVID-19 data collected through routine health care provision linked to longitudinal health care records and demographics. The infrastructure is live, supporting aggregate-level querying of data across the United Kingdom. Conclusions: CO-CONNECT was developed by a multidisciplinary team. It enables rapid COVID-19 data discovery and instantaneous meta-analysis across data sources, and it is researching streamlined data extraction for use in a Trusted Research Environment for research and public health analysis. CO-CONNECT has the potential to make UK health data more interconnected and better able to answer national-level research questions while maintaining patient confidentiality and local governance procedures. ", issn="1438-8871", doi="10.2196/40035", url="//www.mybigtv.com/2022/12/e40035", url="https://doi.org/10.2196/40035", url="http://www.ncbi.nlm.nih.gov/pubmed/36322788" }