Nolan Brodie, Hicks Christopher M, Petrosoniak Andrew, Jung James, Grantcharov Teodor
Department of Emergency Medicine, St Michael's Hospital, Toronto, Ontario, Canada.
Division of Emergency Medicine, Department of Medicine, University of Toronto, Toronto, Ontario, Canada.
Trauma Surg Acute Care Open. 2020 Jul 12;5(1):e000510. doi: 10.1136/tsaco-2020-000510. eCollection 2020.
Adverse events and lapses in safety are identified after the fact and often discussed through postevent review. These rounds rely on personal recollection, information from patient charts and incident reports that are limited by retrospective data collection. This results in recall bias and inaccurate or insufficient detail related to timeline, incidence and nature adverse events. To better understand the interplay of the complex team and task-based challenges in the trauma bay, we have developed a synchronized data capture and analysis platform called the Trauma Black Box (Surgical Safety Technologies, Toronto). This system continuously acquires audiovisual, patient physiological and environmental data from a sophisticated array of wall-mounted cameras, microphones and sensors. Expert analysts and software-based algorithms then populate a data timeline of case events from start to finish, retaining a handful of anonymized video clippings to supplement the review. These data also provide a consistent and reliable method to track specific quality metrics, such as time to trauma team assembly or time to blood product arrival. Furthermore, data can also be linked to patients' electronic medical records to explore relationships between initial trauma resuscitation and downstream patient-oriented outcomes. A video capture and data analysis system for the trauma bay overcomes the inherent deficiencies in the current standard for evaluating patient care in the trauma bay and offers exciting potential to enhance patient safety through a comprehensive data collection system.
不良事件和安全失误是事后才被发现的,并且常常通过事后审查来讨论。这些审查依赖于个人回忆、患者病历信息以及 incident reports(此处可能有误,推测为“事件报告”),而这些都受到回顾性数据收集的限制。这就导致了回忆偏差以及与不良事件的时间线、发生率和性质相关的不准确或不充分的细节。为了更好地理解创伤病房中复杂的团队和基于任务的挑战之间的相互作用,我们开发了一个名为创伤黑匣子(外科安全技术公司,多伦多)的同步数据捕获和分析平台。该系统通过一系列精密的壁挂式摄像头、麦克风和传感器持续获取视听、患者生理和环境数据。然后,专家分析师和基于软件的算法会生成一个从开始到结束的病例事件数据时间线,并保留一些匿名视频片段以辅助审查。这些数据还提供了一种一致且可靠的方法来跟踪特定的质量指标,例如创伤团队集合时间或血液制品到达时间。此外,数据还可以与患者的电子病历相链接,以探索初始创伤复苏与下游以患者为导向的结果之间的关系。一个用于创伤病房的视频捕获和数据分析系统克服了当前创伤病房患者护理评估标准中固有的缺陷,并通过一个全面的数据收集系统为提高患者安全提供了令人兴奋的潜力。