Weill Department of Medicine, Weill Cornell Medicine, New York, NY, United States.
Department of Population Health Sciences, Weill Cornell Medicine, New York, NY, United States.
J Biomed Inform. 2021 Jun;118:103789. doi: 10.1016/j.jbi.2021.103789. Epub 2021 Apr 14.
Patients treated in an intensive care unit (ICU) are critically ill and require life-sustaining organ failure support. Existing critical care data resources are limited to a select number of institutions, contain only ICU data, and do not enable the study of local changes in care patterns. To address these limitations, we developed the Critical carE Database for Advanced Research (CEDAR), a method for automating extraction and transformation of data from an electronic health record (EHR) system. Compared to an existing gold standard of manually collected data at our institution, CEDAR was statistically similar in most measures, including patient demographics and sepsis-related organ failure assessment (SOFA) scores. Additionally, CEDAR automated data extraction obviated the need for manual collection of 550 variables. Critically, during the spring 2020 COVID-19 surge in New York City, a modified version of CEDAR supported pandemic response efforts, including clinical operations and research. Other academic medical centers may find value in using the CEDAR method to automate data extraction from EHR systems to support ICU activities.
患者在重症监护病房（ICU）接受治疗，病情危急，需要维持生命的器官衰竭支持。现有的重症监护数据资源仅限于少数机构，仅包含 ICU 数据，并且无法研究护理模式的局部变化。为了解决这些限制，我们开发了 Critical carE Database for Advanced Research（CEDAR），这是一种从电子病历（EHR）系统中自动提取和转换数据的方法。与我们机构现有的手动收集数据的黄金标准相比，CEDAR 在大多数指标上具有统计学相似性，包括患者人口统计学和与脓毒症相关的器官衰竭评估（SOFA）评分。此外，CEDAR 自动化数据提取免除了手动收集 550 个变量的需要。至关重要的是，在 2020 年春季纽约市 COVID-19 疫情高峰期，CEDAR 的一个修改版本支持了大流行应对工作，包括临床运营和研究。其他学术医疗中心可能会发现使用 CEDAR 方法从 EHR 系统中自动提取数据以支持 ICU 活动具有价值。
