Umberger Reba, Indranoi Chayawat Yo, Simpson Melanie, Jensen Rose, Shamiyeh James, Yende Sachin
Department of Acute and Tertiary Care, College of Nursing, The University of Tennessee Health Science Center, Memphis, TN, USA.
University Health System, The University of Tennessee Medical Center, Knoxville, TN, USA.
SAGE Open Nurs. 2019 May 24;5:2377960819850972. doi: 10.1177/2377960819850972. eCollection 2019 Jan-Dec.
Clinical research in sepsis patients often requires gathering large amounts of longitudinal information. The electronic health record can be used to identify patients with sepsis, improve participant study recruitment, and extract data. The process of extracting data in a reliable and usable format is challenging, despite standard programming language. The aims of this project were to explore infrastructures for capturing electronic health record data and to apply criteria for identifying patients with sepsis. We conducted a prospective feasibility study to locate and capture/abstract electronic health record data for future sepsis studies. We located parameters as displayed to providers within the system and then captured data transmitted in Health Level Seven® interfaces between electronic health record systems into a prototype database. We evaluated our ability to successfully identify patients admitted with sepsis in the target intensive care unit (ICU) at two cross-sectional time points and then over a 2-month period. A majority of the selected parameters were accessible using an iterative process to locate and abstract them to the prototype database. We successfully identified patients admitted to a 20-bed ICU with sepsis using four data interfaces. Retrospectively applying similar criteria to data captured for 319 patients admitted to ICU over a 2-month period was less sensitive in identifying patients admitted directly to the ICU with sepsis. Classification into three admission categories (sepsis, no-sepsis, and other) was fair (Kappa .39) when compared with manual chart review. This project confirms reported barriers in data extraction. Data can be abstracted for future research, although more work is needed to refine and create customizable reports. We recommend that researchers engage their information technology department to electronically apply research criteria for improved research screening at the point of ICU admission. Using clinical electronic health records data to classify patients with sepsis over time is complex and challenging.
脓毒症患者的临床研究通常需要收集大量纵向信息。电子健康记录可用于识别脓毒症患者、改善研究参与者招募以及提取数据。尽管有标准编程语言，但以可靠且可用的格式提取数据的过程仍具有挑战性。本项目的目的是探索用于捕获电子健康记录数据的基础设施，并应用识别脓毒症患者的标准。我们进行了一项前瞻性可行性研究，以定位和捕获/提取电子健康记录数据，用于未来的脓毒症研究。我们定位了系统中向提供者显示的参数，然后将电子健康记录系统之间通过卫生信息交换标准第七版（Health Level Seven®）接口传输的数据捕获到一个原型数据库中。我们评估了在两个横断面时间点以及随后的2个月期间，成功识别目标重症监护病房（ICU）中脓毒症入院患者的能力。大多数选定的参数可通过迭代过程进行访问，以将其定位并提取到原型数据库中。我们使用四个数据接口成功识别了入住20张床位ICU的脓毒症患者。回顾性地将类似标准应用于2个月期间入住ICU的319名患者所捕获的数据，在识别直接入住ICU的脓毒症患者方面敏感性较低。与人工病历审查相比，分为三个入院类别（脓毒症、非脓毒症和其他）的分类结果尚可（Kappa值为0.39）。本项目证实了数据提取方面已报道的障碍。尽管需要更多工作来完善和创建可定制报告，但数据可被提取用于未来研究。我们建议研究人员让其信息技术部门在ICU入院时以电子方式应用研究标准，以改进研究筛查。随着时间的推移，使用临床电子健康记录数据对脓毒症患者进行分类既复杂又具有挑战性。
