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代码S:重新设计全院范围的同行评审流程以识别系统错误。

Code S: Redesigning Hospital-Wide Peer Review Processes to Identify System Errors.

作者信息

Au Huy D, Kim Daniel I, Garrison Roger C, Yu Minho, Thompson Gary, Fargo Ramiz, Nathaniel Brandon, Chitsazan Morteza, Puvvula Lakshmi K, Motabar Ali, Loo Lawrence K

机构信息

Internal Medicine, Riverside University Health System Medical Center, Moreno Valley, USA.

Internal Medicine, University of California Riverside School of Medicine, Moreno Valley, USA.

出版信息

Cureus. 2020 Jun 5;12(6):e8466. doi: 10.7759/cureus.8466.

Abstract

Hospital medical errors that result in patient harm and death are largely identified as system failures. Most hospitals lack the tools to effectively identify most system errors. Traditional methods used in many hospitals, such as incident reporting (IR), departmental morbidity and mortality conferences, and root cause analysis committees, are often flawed by under reporting. We introduced the Code S designation into our hospital's ongoing physician peer review process as an additional and innovative way to identify system errors that contributed to adverse clinical outcomes. The authors conducted a retrospective review of all peer review cases from January 2008 to December 2011 and determined the quantity and type of system errors that occurred. System errors were categorized based on a modified 5M model which was adapted to reflect system errors encountered in healthcare. The Code S designation discovered 204 system errors that otherwise may not have previously been identified. The addition of the Code S designation to the peer review process can be readily adopted by other healthcare organizations as another tool to help identify, quantify and categorize system errors, and promote hospital-wide process improvements to decrease errors and improve patient safety.

摘要

导致患者伤害和死亡的医院医疗差错在很大程度上被认定为系统故障。大多数医院缺乏有效识别大多数系统差错的工具。许多医院采用的传统方法,如事件报告(IR)、科室发病率和死亡率会议以及根本原因分析委员会,往往因报告不足而存在缺陷。我们将“代码S指定”引入我院正在进行的医师同行评审过程,作为识别导致不良临床结果的系统差错的一种额外且创新的方式。作者对2008年1月至2011年12月期间所有同行评审病例进行了回顾性审查,并确定了发生的系统差错的数量和类型。系统差错根据一个经过修改的5M模型进行分类,该模型经过调整以反映医疗保健中遇到的系统差错。“代码S指定”发现了204个系统差错,否则这些差错可能以前无法被识别。将“代码S指定”添加到同行评审过程中,其他医疗保健组织可以很容易地采用,作为帮助识别、量化和分类系统差错以及促进全院流程改进以减少差错和提高患者安全的另一种工具。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/da46/7336688/9678b8b4d647/cureus-0012-00000008466-i01.jpg

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