Department of Emergency Medicine, St Michael's Hospital, Toronto, Ontario, Canada
Department of Medicine, University of Toronto, Faculty of Medicine, Toronto, Ontario, Canada.
BMJ Qual Saf. 2021 Sep;30(9):739-746. doi: 10.1136/bmjqs-2020-011363. Epub 2020 Oct 23.
Trauma resuscitation is a complex and time-sensitive endeavour with significant risk for error. These errors can manifest from sequential system, team and knowledge-based failures, defined as latent safety threats (LSTs). In situ simulation (ISS) provides a novel prospective approach to recreate clinical situations that may manifest LSTs. Using ISS coupled with a human factors-based video review and modified framework analysis, we sought to identify and quantify LSTs within trauma resuscitation scenarios.
At a level 1 trauma centre, we video recorded 12 monthly unannounced ISS to prospectively identify trauma-related LSTs. The on-call multidisciplinary trauma team participated in the study. Using a modified framework analysis, human factors experts transcribed and coded the videos. We identified LST events, categorised them into themes and subthemes and used a hazard matrix to prioritise subthemes requiring intervention.
We identified 843 LST events during 12 simulations, categorised into seven themes and 38 subthemes, of which 23 are considered critical. The seven themes relate to physical workspace, mental model formation, equipment, unclear accountability, demands exceeding individuals' capacity, infection control and task-specific issues. The physical workspace theme accounted for the largest number of critical LST events (n=152). We observed differences in LST events across the four scenarios; complex scenarios had more LST events.
We identified a diverse set of critical LSTs during trauma resuscitations using ISS coupled with video-based framework analysis. The hazard matrix scoring, in combination with detailed LST subthemes, supported identification of critical LSTs requiring intervention and enhanced efforts intended to improve patient safety. This approach may be useful to others who seek to understand the contributing factors to common LSTs and design interventions to mitigate them.
创伤复苏是一项复杂且时间敏感的工作，存在重大的出错风险。这些错误可能源于连续的系统、团队和基于知识的失败，被定义为潜在安全威胁（LST）。现场模拟（ISS）提供了一种新颖的前瞻性方法，可以重现可能出现 LST 的临床情况。我们使用 ISS 结合基于人为因素的视频回顾和修改后的框架分析，旨在识别和量化创伤复苏场景中的 LST。
在一家 1 级创伤中心，我们对 12 次每月的非预告现场模拟进行了视频录制，以前瞻性地识别与创伤相关的 LST。由当班的多学科创伤团队参与了这项研究。使用修改后的框架分析，人为因素专家对视频进行了转录和编码。我们识别了 LST 事件，将其分类为主题和子主题，并使用危险矩阵对需要干预的子主题进行优先级排序。
我们在 12 次模拟中发现了 843 个 LST 事件，分为七个主题和 38 个子主题，其中 23 个被认为是关键的。这七个主题与物理工作空间、心理模型形成、设备、责任不明确、个人能力超出需求、感染控制和特定任务问题有关。物理工作空间主题占了最多的关键 LST 事件（n=152）。我们观察到四个场景中的 LST 事件存在差异；复杂场景中的 LST 事件更多。
我们使用 ISS 结合基于视频的框架分析，在创伤复苏中发现了一组多样化的关键 LST。危险矩阵评分，结合详细的 LST 子主题，支持识别需要干预的关键 LST，并加强旨在提高患者安全的努力。这种方法可能对其他试图了解常见 LST 产生因素并设计干预措施以减轻这些因素的人有用。
