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

开发自动检测算法以提高手术室黑匣子分析的效率

• 批准号: 521888-2017
• 负责人: Trbovich, Patricia
• 金额: $ 1.82万
• 依托单位: University of Toronto
• 依托单位国家: 加拿大
• 项目类别: Engage Grants Program
• 财政年份: 2017
• 资助国家: 加拿大
• 起止时间: 2017-01-01 至 2018-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=639889
• 关键词: Development; automated; detection; algorithm; improve

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.

手术室是临床环境中最复杂、风险最高的情况之一，而手术室黑匣子系统旨在实现对手术过程的分析，以提高患者的安全。黑匣子是一种在手术过程中记录手术室内的音频、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，回顾分析这些数据可以使研究人员了解不良事件的起源。前瞻性地，它可以允许识别潜在风险或增强患者安全性的术中因素。目前，从黑匣子数据中得出这样的见解是劳动密集型的，需要大量的时间和人力来审查录音。因此，我们的目标是通过自动检测黑盒记录中感兴趣的片段，特别是情节不确定性，来提高黑盒分析的效率和有效性。事件不确定性是个人或团队在异常情况下经历的一种状态，如果处理不当，可能会导致不良事件。首先将进行研究，以揭示记录的数据与事件不确定性的观察实例之间的关联。根据这种相关性，将开发机器学习技术来处理黑匣子数据，并识别事件不确定性的实例。黑匣子的这种检测能力将减少与数据分析相关的资源需求，将其扩展到更多医院，加快识别患者护理过程中的不确定性，并在患者安全方面实现快速改进和更高质量的管理。