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使用医疗保健领域的人因分析与分类系统(HFACS-Healthcare)识别手术期间的系统漏洞。

Using HFACS-Healthcare to Identify Systemic Vulnerabilities During Surgery.

作者信息

Cohen Tara N, Francis Sarah E, Wiegmann Douglas A, Shappell Scott A, Gewertz Bruce L

机构信息

1 Cedars-Sinai Medical Center, Los Angeles, CA.

2 University of Wisconsin-Madison, Madison, WI.

出版信息

Am J Med Qual. 2018 Nov/Dec;33(6):614-622. doi: 10.1177/1062860618764316. Epub 2018 Mar 21.

Abstract

The Human Factors Analysis and Classification System for Healthcare (HFACS-Healthcare) was used to classify surgical near miss events reported via a hospital's event reporting system over the course of 1 year. Two trained analysts identified causal factors within each event narrative and subsequently categorized the events using HFACS-Healthcare. Of 910 original events, 592 could be analyzed further using HFACS-Healthcare, resulting in the identification of 726 causal factors. Most issues (n = 436, 60.00%) involved preconditions for unsafe acts, followed by unsafe acts (n = 257, 35.39%), organizational influences (n = 27, 3.72%), and supervisory factors (n = 6, 0.82%). These findings go beyond the traditional methods of trending incident data that typically focus on documenting the frequency of their occurrence. Analyzing near misses based on their underlying contributing human factors affords a greater opportunity to develop process improvements to reduce reoccurrence and better provide patient safety approaches.

摘要

医疗保健领域的人为因素分析与分类系统(HFACS-Healthcare)被用于对一家医院事件报告系统在1年时间内上报的手术中险些发生的事件进行分类。两名经过培训的分析人员在每个事件描述中识别因果因素,随后使用HFACS-Healthcare对这些事件进行分类。在910起原始事件中,有592起可以使用HFACS-Healthcare进一步分析,从而识别出726个因果因素。大多数问题(n = 436,60.00%)涉及不安全行为的前提条件,其次是不安全行为(n = 257,35.39%)、组织影响(n = 27,3.72%)和监督因素(n = 6,0.82%)。这些发现超越了传统的事件数据趋势分析方法,传统方法通常只关注记录事件发生的频率。基于潜在的人为因素对险些发生的事件进行分析,为改进流程以减少事件再次发生和更好地提供患者安全保障措施提供了更大的机会。

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