@Article{信息:doi 10.2196 / / jmir。9901，作者=“Musy, Sarah N和Ausserhofer, Dietmar和Schwendimann, Ren{\'e}和Rothen, Hans Ulrich和Jeitziner, Marie-Madlen和Rutjes, Anne WS和Simon, Michael”，标题=“基于触发工具的电子健康记录中的自动不良事件检测:系统评价”，期刊=“J Med Internet Res”，年=“2018”，月=“5”，日=“30”，卷=“20”，数=“5”，页=“e198”，关键词=“患者安全;电子健康记录;病人的伤害;背景:卫生保健中的不良事件给卫生保健系统、机构和患者带来了巨大的负担。回顾性触发工具通常手动用于检测不良事件，尽管使用电子健康记录的自动化方法可能提供实时不良事件检测，从而允许及时的纠正干预。目的:本系统综述的目的是描述当前的研究方法和挑战，涉及在电子健康记录中使用基于自动触发工具的不良事件检测方法。此外，我们旨在评价应用研究的设计，并综合估计以手动触发工具作为参考标准的自动检测方法的不良事件发生率和诊断测试准确性。方法:对PubMed、EMBASE、CINAHL、Cochrane Library进行查询。我们纳入了观察性研究，在急性护理环境中应用触发工具，并排除了非医院和门诊环境的研究。 Eligible articles were divided into diagnostic test accuracy studies and prevalence studies. We derived the study prevalence and estimates for the positive predictive value. We assessed bias risks and applicability concerns using Quality Assessment tool for Diagnostic Accuracy Studies-2 (QUADAS-2) for diagnostic test accuracy studies and an in-house developed tool for prevalence studies. Results: A total of 11 studies met all criteria: 2 concerned diagnostic test accuracy and 9 prevalence. We judged several studies to be at high bias risks for their automated detection method, definition of outcomes, and type of statistical analyses. Across all the 11 studies, adverse event prevalence ranged from 0{\%} to 17.9{\%}, with a median of 0.8{\%}. The positive predictive value of all triggers to detect adverse events ranged from 0{\%} to 100{\%} across studies, with a median of 40{\%}. Some triggers had wide ranging positive predictive value values: (1) in 6 studies, hypoglycemia had a positive predictive value ranging from 15.8{\%} to 60{\%}; (2) in 5 studies, naloxone had a positive predictive value ranging from 20{\%} to 91{\%}; (3) in 4 studies, flumazenil had a positive predictive value ranging from 38.9{\%} to 83.3{\%}; and (4) in 4 studies, protamine had a positive predictive value ranging from 0{\%} to 60{\%}. We were unable to determine the adverse event prevalence, positive predictive value, preventability, and severity in 40.4{\%}, 10.5{\%}, 71.1{\%}, and 68.4{\%} of the studies, respectively. These studies did not report the overall number of records analyzed, triggers, or adverse events; or the studies did not conduct the analysis. Conclusions: We observed broad interstudy variation in reported adverse event prevalence and positive predictive value. The lack of sufficiently described methods led to difficulties regarding interpretation. To improve quality, we see the need for a set of recommendations to endorse optimal use of research designs and adequate reporting of future adverse event detection studies. ", issn="1438-8871", doi="10.2196/jmir.9901", url="//www.mybigtv.com/2018/5/e198/", url="https://doi.org/10.2196/jmir.9901" }