Reference number : GC-2017-Q4-01790, amendment number : , agreement type : G, recipient legal name : Trbovich, Patricia (University of Toronto), recipient country : CA, recipient province : nan, recipient city : Ontario, recipient postal code : nan, program name english : Engage Grants for universities, program name french : Subventions d'engagement partenarial pour les universités, program purpose english : Operating rooms present one of the most complex, high-risk situations in clinical settings, and the Operating Room Black Box_x000D_
system seeks to enable the analysis of surgical procedures to improve patient safety. The Black Box is a device that records_x000D_
audio, video, environmental, and biometric data within the operating room during a surgical case. This information can be used for_x000D_
surgical safety improvement. For example, retrospectively, analyzing this data can enable researchers to understand the origin of_x000D_
an adverse event. Prospectively, it can allow for the identification of intraoperative factors that potentially risk or enhance patient_x000D_
safety. Currently, deriving such insight from the Black Box data is labour intensive, requiring significant amount of time and manual_x000D_
effort in reviewing recordings. Thus, we aim to improve the efficiency and effectiveness of Black Box analysis by automating the_x000D_
detection of interested segments within Black Box recordings, specifically episodic uncertainty. Episodic uncertainty is a state_x000D_
experienced by individuals or teams during abnormal situations that may lead to adverse events if not managed correctly._x000D_
Research will first be conducted to uncover the association between recorded data and observed instances of episodic_x000D_
uncertainties. Machine learning techniques informed by the correlation will be developed to process Black Box data and identify_x000D_
instances of episodic uncertainty. Such detection capability in Black Box will decrease the resource demands associated with data_x000D_
analysis, expand its availability to more hospitals, expedite the identification of uncertainty during patient care, and enable rapid_x000D_
improvements and higher quality management in patient safety., program purpose french : Operating rooms present one of the most complex, high-risk situations in clinical settings, and the Operating Room Black Box_x000D_
system seeks to enable the analysis of surgical procedures to improve patient safety. The Black Box is a device that records_x000D_
audio, video, environmental, and biometric data within the operating room during a surgical case. This information can be used for_x000D_
surgical safety improvement. For example, retrospectively, analyzing this data can enable researchers to understand the origin of_x000D_
an adverse event. Prospectively, it can allow for the identification of intraoperative factors that potentially risk or enhance patient_x000D_
safety. Currently, deriving such insight from the Black Box data is labour intensive, requiring significant amount of time and manual_x000D_
effort in reviewing recordings. Thus, we aim to improve the efficiency and effectiveness of Black Box analysis by automating the_x000D_
detection of interested segments within Black Box recordings, specifically episodic uncertainty. Episodic uncertainty is a state_x000D_
experienced by individuals or teams during abnormal situations that may lead to adverse events if not managed correctly._x000D_
Research will first be conducted to uncover the association between recorded data and observed instances of episodic_x000D_
uncertainties. Machine learning techniques informed by the correlation will be developed to process Black Box data and identify_x000D_
instances of episodic uncertainty. Such detection capability in Black Box will decrease the resource demands associated with data_x000D_
analysis, expand its availability to more hospitals, expedite the identification of uncertainty during patient care, and enable rapid_x000D_
improvements and higher quality management in patient safety., agreement title english : Development of an automated detection algorithm to improve efficiency of Operating Room Black Box_x000D_
analyses, agreement title french : Development of an automated detection algorithm to improve efficiency of Operating Room Black Box_x000D_
analyses, agreement number : EGP, agreement value : 25000.0, agreement start date : 2018-03-22, agreement end date : nan, description english : nan, description french : nan, expected results english : nan, expected results french : nan, additional information english : Grant or Award spanning more than one fiscal year (2017-2018 to 2018-2019)., additional information french : Subvention ou bourse octroyée s'appliquant à plus d'un exercice financier (2017-2018 à 2018-2019)., owner organization : nserc-crsng, owner organization title : Natural Sciences and Engineering Research Council of Canada | Conseil de recherches en sciences naturelles et en génie du Canada