Analysis of Uncertain Situations in Operating Rooms
Operating rooms are one of the most complex clinical environments, and errors during surgeries can negatively impact patient outcomes, such as longer hospital stay and fatality rate. Existing analysis of surgical performance focuses heavily on adverse events reported after the surgery. This project leverages the capability of the OR Black Box, which is a platform that can collect video, audio, vital signs, and laparoscopic view of the patient during laparoscopic surgeries.
By utilizing the surgical recordings, this project sought to identify transitions from routine work to uncertain situations where the surgical team may be unclear about the current situation, the best choices, and future outcomes of the surgery. We would like to gain a deeper understanding of both the causes of uncertainties and the resilience factors (e.g., technological, environmental, technical skills, and non-technical skills) that allowed clinicians to appropriately and quickly respond.
One of the main challenges of conducting the analysis of surgical videos is the fact that manual review of the OR Black Box® videos is extremely time consuming which delays the feedback sent to the surgical team. Thus, the second aim of the project was to improve the analysis efficiency by applying machine learning algorithms to identify the transition from routine work to uncertain situations.
By utilizing the surgical recordings, this project sought to identify transitions from routine work to uncertain situations where the surgical team may be unclear about the current situation, the best choices, and future outcomes of the surgery. We would like to gain a deeper understanding of both the causes of uncertainties and the resilience factors (e.g., technological, environmental, technical skills, and non-technical skills) that allowed clinicians to appropriately and quickly respond.
One of the main challenges of conducting the analysis of surgical videos is the fact that manual review of the OR Black Box® videos is extremely time consuming which delays the feedback sent to the surgical team. Thus, the second aim of the project was to improve the analysis efficiency by applying machine learning algorithms to identify the transition from routine work to uncertain situations.
My Roles
- Launched the research project by securing funding for the project, collaborating with a cross-functional team, and designing the research protocol, including recruitment strategy, consent process, information flow across multiple departments.
- Increased buy-in, participation, and project visibility through verbal and written communications with clinicians, patients, and admin staff
- Conducted observations and surgical video analysis to understand supporting and hindering factors (e.g. technological and environmental) related to surgery performance
- Designed a data analysis framework for uncertain situations during laparoscopic surgeries by collaborating with domain experts and conducting iterative video analysis
- Coached junior researchers in grant writing, literature review, and video analysis
Impacts
My work at North York General Hospital has created many impacts in improving patient safety in the operating rooms. The project is ongoing at North York General Hospital to provide feedback to clinicians and improve surgery outcomes. The analysis framework I developed is being used to guide the analysis of surgical videos. Finally, the coded data produced through my analysis is being used in the development of a machine learning algorithm for automatically flagging events that require deeper analysis.
News
Collaborators
- Bijan Dastgheib – St. Michael’s Hospital
- Mark Fan – North York General Hospital
- Alistair Forsyth – North York General Hospital
- Teodor Grantcharov – St. Michael’s Hospital, Surgical Safety Technologies
- Usmaan Hameed – North York General Hospital
- Darryl Irwin – North York General Hospital
- Donna McRitchie – North York General Hospital
- Sonia Pinkney – University of Toronto
- Adrian Ragobar – North York General Hospital
- Michell Richardson – North York General Hospital
- Lloyd Smith – North York General Hospital
- Patricia Trbovich – University of Toronto, North York General Hospital
- Julie Zen – North York General Hospital
Acknowledgement
This project was funded through Ontario Centres of Excellence VIP I + NSERC Engage and Steinberg Fund at the North York General Hospital with support from the Surgical Safety Technologies. This work was completed as part of my postdoctoral fellowship funded through the Health System Impact Fellowship sponsored by Canadian Institutes of Health Research.