Department of Biomedical Informatics, The Ohio State University College of Medicine, Columbus, OH; Division of Infectious Diseases, Department of Internal Medicine, The Ohio State University Wexner Medical Center, Columbus, OH.
Department of Clinical Epidemiology, The Ohio State University Wexner Medical Center, Columbus, OH.
Am J Infect Control. 2018 Mar;46(3):316-321. doi: 10.1016/j.ajic.2017.09.006. Epub 2017 Nov 10.
Surveillance is an important tool for infection control; however, this task can often be time-consuming and take away from infection prevention activities. With the increasing availability of comprehensive electronic health records, there is an opportunity to automate these surveillance activities. The objective of this article is to describe the implementation of an electronic algorithm for ventilator-associated events (VAEs) at a large academic medical center METHODS: This article reports on a 6-month manual validation of a dashboard for VAEs. We developed a computerized algorithm for automatically detecting VAEs and compared the output of this algorithm to the traditional, manual method of VAE surveillance.
Manual surveillance by the infection preventionists identified 13 possible and 11 probable ventilator-associated pneumonias (VAPs), and the VAE dashboard identified 16 possible and 13 probable VAPs. The dashboard had 100% sensitivity and 100% accuracy when compared with manual surveillance for possible and probable VAP. We report on the successfully implemented VAE dashboard. Workflow of the infection preventionists was simplified after implementation of the dashboard with subjective time-savings reported.
Implementing a computerized dashboard for VAE surveillance at a medical center with a comprehensive electronic health record is feasible; however, this required significant initial and ongoing work on the part of data analysts and infection preventionists.
监测是感染控制的重要工具;然而,这项任务通常很耗时,会占用感染预防活动的时间。随着综合电子健康记录的可用性不断提高,有机会实现这些监测活动的自动化。本文的目的是描述在一家大型学术医疗中心实施呼吸机相关事件(VAEs)电子算法的情况。
本文报告了对 VAEs 仪表板进行为期 6 个月的手动验证。我们开发了一种用于自动检测 VAEs 的计算机算法,并将该算法的输出与传统的手动 VAE 监测方法进行了比较。
感染预防人员通过手动监测发现了 13 例可能的和 11 例可能的呼吸机相关性肺炎(VAP),而 VAE 仪表板则发现了 16 例可能的和 13 例可能的 VAP。与手动监测可能和可能的 VAP 相比,该仪表板的敏感性为 100%,准确性为 100%。我们报告了成功实施的 VAE 仪表板。在实施仪表板后,感染预防人员的工作流程得到了简化,并报告了主观上的节省时间。
在具有综合电子健康记录的医疗中心实施 VAEs 监测的计算机化仪表板是可行的;然而,这需要数据分析师和感染预防人员付出大量的初始和持续工作。