Development of an Interactive Dashboard to Analyze Cognitive Workload of Surgical Teams During Complex Procedural Care.

Roger D Dias, Heather M Conboy, Jennifer M Gabany, Lori A Clarke, Leon J Osterweil, George S Avrunin, David Arney, Julian M Goldman, Giuseppe Riccardi, Steven J Yule, Marco A Zenati
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引用次数: 15

Abstract

In the surgical setting, team members constantly deal with a high-demand operative environment that requires simultaneously processing a large amount of information. In certain situations, high demands imposed by surgical tasks and other sources may exceed team member's cognitive capacity, leading to cognitive overload which may place patient safety at risk. In the present study, we describe a novel approach to integrate an objective measure of team member's cognitive load with procedural, behavioral and contextual data from real-life cardiac surgeries. We used heart rate variability analysis, capturing data simultaneously from multiple team members (surgeon, anesthesiologist and perfusionist) in a real-time and unobtrusive manner. Using audio-video recordings, behavioral coding and a hierarchical surgical process model, we integrated multiple data sources to create an interactive surgical dashboard, enabling the analysis of the cognitive load imposed by specific steps, substeps and/or tasks. The described approach enables us to detect cognitive load fluctuations over time, under specific conditions (e.g. emergencies, teaching) and in situations that are prone to errors. This in-depth understanding of the relationship between cognitive load, task demands and error occurrence is essential for the development of cognitive support systems to recognize and mitigate errors during complex surgical care in the operating room.

Abstract Image

Abstract Image

交互式仪表板的开发,用于分析复杂手术护理期间手术团队的认知工作量。
在手术环境中,团队成员不断处理高需求的手术环境,需要同时处理大量信息。在某些情况下,手术任务和其他来源施加的高需求可能会超过团队成员的认知能力,导致认知过载,这可能会危及患者的安全。在本研究中,我们描述了一种新的方法,将团队成员认知负荷的客观测量与现实心脏手术的程序、行为和背景数据相结合。我们使用心率变异性分析,以实时、不引人注目的方式同时从多个团队成员(外科医生、麻醉师和灌注师)获取数据。使用音频视频记录、行为编码和分层手术过程模型,我们集成了多个数据源,创建了一个交互式手术仪表板,能够分析特定步骤、子步骤和/或任务施加的认知负荷。所描述的方法使我们能够在特定条件下(如紧急情况、教学)和容易出错的情况下检测认知负荷随时间的波动。对认知负荷、任务需求和错误发生之间关系的深入理解对于开发认知支持系统以识别和减轻手术室复杂手术护理中的错误至关重要。
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