开发和评估俄亥俄州东北部多医院卫生系统中与流感相关的住院的全自动监测系统。

Development and Evaluation of a Fully Automated Surveillance System for Influenza-Associated Hospitalization at a Multihospital Health System in Northeast Ohio.

机构信息

Department of Infection Prevention, Enterprise Quality and Patient Safety, Cleveland Clinic, Cleveland, Ohio, United States.

Enterprise Quality and Patient Safety, Cleveland Clinic, Cleveland, Ohio, United States.

出版信息

Appl Clin Inform. 2020 Aug;11(4):564-569. doi: 10.1055/s-0040-1715651. Epub 2020 Aug 26.

DOI:10.1055/s-0040-1715651

PMID:32851617

原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC7449789/

Abstract

BACKGROUND

Performing high-quality surveillance for influenza-associated hospitalization (IAH) is challenging, time-consuming, and essential.

OBJECTIVES

Our objectives were to develop a fully automated surveillance system for laboratory-confirmed IAH at our multihospital health system, to evaluate the performance of the automated system during the 2018 to 2019 influenza season at eight hospitals by comparing its sensitivity and positive predictive value to that of manual surveillance, and to estimate the time and cost savings associated with reliance on the automated surveillance system.

METHODS

Infection preventionists (IPs) perform manual surveillance for IAH by reviewing laboratory records and making a determination about each result. For automated surveillance, we programmed a query against our Enterprise Data Vault (EDV) for cases of IAH. The EDV query was established as a dynamic data source to feed our data visualization software, automatically updating every 24 hours.To establish a gold standard of cases of IAH against which to evaluate the performance of manual and automated surveillance systems, we generated a master list of possible IAH by querying four independent information systems. We reviewed medical records and adjudicated whether each possible case represented a true case of IAH.

RESULTS

We found 844 true cases of IAH, 577 (68.4%) of which were detected by the manual system and 774 (91.7%) of which were detected by the automated system. The positive predictive values of the manual and automated systems were 89.3 and 88.3%, respectively.Relying on the automated surveillance system for IAH resulted in an average recoup of 82 minutes per day for each IP and an estimated system-wide payroll redirection of $32,880 over the four heaviest weeks of influenza activity.

CONCLUSION

Surveillance for IAH can be entirely automated at multihospital health systems, saving time, and money while improving case detection.

摘要

背景

进行高质量的流感相关住院（IAH）监测具有挑战性、耗时且至关重要。

目的

我们的目标是在我们的多医院医疗系统中开发一个完全自动化的实验室确诊 IAH 监测系统，通过比较其敏感性和阳性预测值与手动监测来评估该系统在 2018 至 2019 年流感季节在八家医院的表现，并估计依赖自动化监测系统带来的时间和成本节约。

方法

感染预防员（IP）通过查看实验室记录并对每个结果进行判断来进行 IAH 的手动监测。对于自动监测，我们针对 IAH 病例对我们的企业数据仓库（EDV）进行了编程查询。EDV 查询被确立为一个动态数据源，为我们的数据可视化软件提供数据，每 24 小时自动更新一次。为了建立一个评估手动和自动监测系统性能的 IAH 病例金标准，我们通过查询四个独立的信息系统生成了一个可能的 IAH 病例总清单。我们查看了病历并裁定每个可能的病例是否代表真正的 IAH 病例。

结果

我们发现了 844 例真正的 IAH 病例，其中 577 例（68.4%）通过手动系统检测到，774 例（91.7%）通过自动系统检测到。手动和自动系统的阳性预测值分别为 89.3%和 88.3%。依赖于自动 IAH 监测系统，每位 IP 每天平均可节省 82 分钟，在流感活动最严重的四周内，估计可在全系统范围内重新分配 32880 美元的工资。