eMERGE Phase IV Clinical Center at Partners HealthCare

冠状病毒病2019年(新冠肺炎)的大流行让世界措手不及，重塑了生活方式， 经济，以及医疗保健服务。电子健康记录(EHR)中的数据应广泛提供给 研究新冠肺炎，但尚未在临床站点、公共卫生机构或 与政策制定者的关系。有几个大型、国内和国际项目来建设信息学基础设施 分析新冠肺炎患者的电子病历资料。但是，聚合来自多个EHR的数据仅起作用 如果你能相信最终结果的话。这意味着能够返回到每个站点，并与了解 最好的数据，以了解当地的临床指南、编码实践、数据质量问题等 影响数据的因素。2020年3月，我们发起了一项名为临床联盟的国际努力 EHR(4CE)对新冠肺炎的表征。它汇集了100多名信息学专家、统计学家、 以及来自世界各地的重症监护室医生。4CE的新奇之处在于我们认识到了EHR的复杂性 数据和需要让当地数据专家直接参与，不仅是在数据收集方面，而且是在 研究问题的发展和数据分析。我们试图快速行动，相信早期的情报 比后来的完整情报更有价值。为了做到这一点，我们避免了通常会减速的路障 信息学项目，如构建或安装新软件，或共享涉及的监管障碍 患者级别的数据。相反，我们要求参与站点使用简单的现有工具在本地运行分析，例如 SQL、R和Python脚本，并且仅与4CE的其余部分集中共享聚合计数和统计信息 财团。我们对数据进行分组审查和验证，识别和修复数据质量问题，并要求站点 重复分析，直到一切都正确为止。通过多个周期的数据验证，我们迭代清理 并使人们相信我们看到的调查结果是真实的。因为我们可以快点做，所以我们走 在短短几周内从研究问题到结果。这一拟议项目将使4CE方法“产品化”， 通过三个具体目标：(1)将4CE过渡到“第二阶段”，在这一阶段，站点将开始进行更复杂的地方分析。 我们将开发阶段2分析脚本；更新我们的数据上传、验证和可视化网站；以及 在扩展到联盟的其余部分之前，先在三个站点测试阶段2脚本。(2)示范和 通过两个使用案例评估4CE。我们将改进和验证识别新冠肺炎患者的算法 并用4CE来表征新冠肺炎患者的中枢神经系统并发症。(3) 制定与相辅相成的努力和长期可持续性相结合的计划。作为这项工作的一部分，我们将 创建一份指南，向网站展示如何使用4CE数据提取和质量检查来支持其他新冠肺炎 信息学项目，包括OMOP文件的生成。

The Coronavirus Disease 2019 (COVID-19) pandemic has caught the world off guard, reshaping ways of life, the economy, and healthcare delivery. Data in electronic health records (EHRs) should be widely available to study COVID-19 but have not yet been effectively shared across clinical sites, with public health agencies, or with policy makers. There are several large, national and international projects to build informatics infrastructure to analyze the EHR data of patients with COVID-19. However, aggregating data from multiple EHRs only works if you can trust the final results. This means being able to go back to each site and talk to the people who know the data best, to understand the local clinical guidelines, coding practices, data quality problems, and other factors that affect the data. In March, 2020, we launched an international effort called the Consortium for Clinical Characterization of COVID-19 by EHR (4CE). It brings together more than 100 informatics experts, statisticians, and ICU doctors from around the world. The novel aspect of 4CE is that we recognize the complexities of EHR data and the need to directly involve the local data experts, not only in the data collection, but also in the development of research questions and the data analyses. We try to move fast, believing that early intelligence is worth more than complete intelligence later. To do this, we avoid roadblocks that typically slow down informatics projects, such as building or installing new software, or the regulatory hurdles involved in sharing patient-level data. Instead, we ask participating sites to run analyses locally, using simple existing tools, like SQL, R, and Python scripts, and only share aggregate counts and statistics centrally with the rest of the 4CE consortium. We review and validate the data as a group, identify and fix data quality problems, and ask sites to repeat the analyses until everything is right. Through multiple cycles of data verification, we iteratively clean up the data and gain confidence that the findings we are seeing are real. Because we can do this quickly, we go from research question to results in just a few weeks. This proposed project will “productize” the 4CE approach, through three Specific Aims: (1) Transition 4CE to “Phase 2”, where sites will begin more complex local analyses. We will develop Phase 2 analysis scripts; update our data upload, validation, and visualization websites; and, test the Phase 2 scripts at three sites before expanding to the rest of the consortium. (2) Demonstrate and evaluate 4CE through two use cases. We will refine and validate an algorithm for identifying COVID-19 patients with “severe” disease and use 4CE to characterize central nervous system complications in COVID-19. (3) Develop a plan for integrating with complementary efforts and long-term sustainability. As part of this, we will create a guide that shows sites how to use 4CE data extracts and quality checks to support other COVID-19 informatics projects, including the generation of OMOP files.