Development of an automated detection algorithm to improve efficiency of Operating Room Black Box analyses

手术室是临床环境中最复杂、高风险的环境之一，而手术室黑匣子系统旨在对手术过程进行分析，以提高患者安全。黑匣子是一种记录手术过程中手术室内的音频、视频、环境和生物特征数据的设备。这些信息可用于提高手术安全性。例如，回顾性地分析这些数据可以使研究人员了解不良事件的起源。展望未来，它可以允许识别术中潜在风险或提高患者安全的因素。目前，从黑匣子数据中获得这样的见解是劳动密集型的，需要大量的时间和人工来审查记录。因此，我们的目标是通过自动检测黑匣子记录中感兴趣的片段，特别是情景不确定性，来提高黑匣子分析的效率和有效性。偶发性不确定性是个人或团队在不正常情况下经历的一种状态，如果管理不当可能导致不良事件。首先将进行研究，以揭示记录数据与观测到的偶发性不确定性之间的联系。将开发由相关性提供信息的机器学习技术来处理黑匣子数据并识别情景不确定性的实例。“黑匣子”中的这种检测能力将减少与数据分析相关的资源需求，将其扩展到更多的医院，加快识别患者护理过程中的不确定性，并实现患者安全的快速改进和更高质量的管理。

Operating rooms present one of the most complex, high-risk situations in clinical settings, and the Operating Room Black Boxsystem seeks to enable the analysis of surgical procedures to improve patient safety. The Black Box is a device that recordsaudio, video, environmental, and biometric data within the operating room during a surgical case. This information can be used forsurgical safety improvement. For example, retrospectively, analyzing this data can enable researchers to understand the origin ofan adverse event. Prospectively, it can allow for the identification of intraoperative factors that potentially risk or enhance patientsafety. Currently, deriving such insight from the Black Box data is labour intensive, requiring significant amount of time and manualeffort in reviewing recordings. Thus, we aim to improve the efficiency and effectiveness of Black Box analysis by automating thedetection of interested segments within Black Box recordings, specifically episodic uncertainty. Episodic uncertainty is a stateexperienced by individuals or teams during abnormal situations that may lead to adverse events if not managed correctly.Research will first be conducted to uncover the association between recorded data and observed instances of episodicuncertainties. Machine learning techniques informed by the correlation will be developed to process Black Box data and identifyinstances of episodic uncertainty. Such detection capability in Black Box will decrease the resource demands associated with dataanalysis, expand its availability to more hospitals, expedite the identification of uncertainty during patient care, and enable rapidimprovements and higher quality management in patient safety.