We leveraged the largely untapped resource of electronic health record data to address critical clinical and epidemiological questions about Coronavirus Disease 2019 (COVID-19). To do this, we formed an international consortium (4CE) of 96 hospitals across five countries (www.covidclinical.net). Contributors utilized the Informatics for Integrating Biology and the Bedside (i2b2) or Observational Medical Outcomes Partnership (OMOP) platforms to map to a common data model. The group focused on temporal changes in key laboratory test values. Harmonized data were analyzed locally and converted to a shared aggregate form for rapid analysis and visualization of regional differences and global commonalities. Data covered 27,584 COVID-19 cases with 187,802 laboratory tests. Case counts and laboratory trajectories were concordant with existing literature. Laboratory tests at the time of diagnosis showed hospital-level differences equivalent to country-level variation across the consortium partners. Despite the limitations of decentralized data generation, we established a framework to capture the trajectory of COVID-19 disease in patients and their response to interventions.

我们利用大量未开发的电子健康记录数据资源来解决有关 2019 年冠状病毒病 (COVID-19) 的关键临床和流行病学问题。为此，我们成立了一个由五个国家的 96 家医院组成的国际联盟 (4CE) (www.covidclinical.net)。贡献者利用整合生物学和床边信息学 (i2b2) 或观察性医疗结果合作伙伴 (OMOP) 平台来映射到通用数据模型。该小组重点关注关键实验室测试值的时间变化。统一数据在本地进行分析，并转换为共享的聚合形式，以便快速分析和可视化区域差异和全球共性。数据涵盖 27,584 例 COVID-19 病例和 187,802 项实验室检测。病例数和实验室轨迹与现有文献一致。诊断时的实验室测试显示，医院层面的差异相当于联盟合作伙伴国家层面的差异。尽管分散式数据生成存在局限性，但我们建立了一个框架来捕获患者的 COVID-19 疾病轨迹及其对干预措施的反应。