Gundlapalli Adi V, Olson Jonathan, Smith Sean P, Baza Michael, Hausam Robert R, Eutropius Louise J, Pestotnik Stanley L, Duncan Karen, Staggers Nancy, Pincetl Pierre, Samore Matthew H
Hospital Epidemiology Unit, Divisions of Clinical Epidemiology and Infectious Diseases, Department of Medicine, University of Utah School of Medicine, Salt Lake City, Utah 84132, USA.
Am J Infect Control. 2007 Apr;35(3):163-71. doi: 10.1016/j.ajic.2006.08.003.
Several computer biosurveillance systems are in place to detect events of public health (PH) significance; however, most lack access to timely and detailed patient-level data and investigation of alerts places a strain on PH resources.
Hospital-based infection control professionals led a multi-disciplinary team to develop a computer rule-based system that relies on the patient's electronic medical record. The rules operated on HL7 messages transmitted by clinical computing systems and encompassed a variety of types of patient-level data, including laboratory test ordering and results, radiology ordering and reports, emergency room and outpatient clinic visits, and hospital admissions. Laboratory data were mapped to standard vocabularies, and radiology data were processed using natural language-processing algorithms before the rules were applied to filter for events of PH interest. For each rule, statistical process controls were applied to generate alerts when levels exceeded two standard deviations above the mean. The system was deployed at a large hospital in Salt Lake City during the 2002 Winter Olympic Games, and it was accessed 3 times a day to perform surveillance. Daily reports were provided to local PH agencies after preliminary investigation of the alerts.
Of the 24 rules monitored, 9 generated alerts on 11 different occasions. The only significant event of PH interest that was noted during the surveillance period was an increase in influenza during the Games. The positive predictive value of the rules varied with a high value (89%) noted for identification of pneumonia from chest radiograph reports by natural language-processing algorithms.
With the assistance of a novel computer-based surveillance system linked to the electronic medical record that uses objective, quantifiable events and access to patient data, infection control practitioners could play a front-line role in biosurveillance and facilitate bidirectional communication with PH agencies.
现已有多个计算机生物监测系统用于检测具有公共卫生(PH)意义的事件;然而,大多数系统无法获取及时且详细的患者层面数据,并且对警报的调查给公共卫生资源带来了压力。
以医院为基础的感染控制专业人员带领一个多学科团队开发了一个基于计算机规则的系统,该系统依赖于患者的电子病历。这些规则基于临床计算系统传输的HL7消息运行,涵盖了各种类型的患者层面数据,包括实验室检查医嘱和结果、放射检查医嘱和报告、急诊室和门诊就诊以及住院情况。在应用规则筛选具有公共卫生意义的事件之前,实验室数据被映射到标准词汇表,放射学数据使用自然语言处理算法进行处理。对于每个规则,当水平超过均值两个标准差时,应用统计过程控制来生成警报。该系统于2002年冬季奥运会期间在盐湖城的一家大型医院部署,每天访问3次以进行监测。在对警报进行初步调查后,每天向当地公共卫生机构提供报告。
在监测的24条规则中,有9条在11个不同场合产生了警报。在监测期间注意到的唯一具有显著公共卫生意义的事件是奥运会期间流感病例增加。规则的阳性预测值各不相同,通过自然语言处理算法从胸部X光报告中识别肺炎的阳性预测值较高(89%)。
借助一个与电子病历相连的新型计算机监测系统,该系统利用客观、可量化的事件并获取患者数据,感染控制从业人员可以在生物监测中发挥一线作用,并促进与公共卫生机构的双向沟通。