Research Overview
The SED Lab examines the role of sociotechnical systems factors in supporting or hindering safety in clinical systems, with specific interests in surgical instrument reprocessing, robotic-assisted surgery, anesthesia medication delivery, and retained foreign objects. Complementing the social determinants of health framework, our research also leverages human factors and systems engineering to examine clinical systems' contributions to healthcare disparities.
Past Projects
Remote Patient Monitoring
PI: Dr. Onil Bhattacharyya
Collaborator: Women's College Hospital
Lab Members: Jingjing (Isabel) Zhan, Andrea Bolanos, Anna Szatan
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Through remote communication and data collection, remote patient monitoring (RPM) has been integrated into newer areas of disease management, both chronic and acute conditions,
including patients with congestive heart failure, cancer, as well as COVID-19. The purpose of the project is to describe and categorize the different RPM projects into distinct remote care
management models and evaluate the effectiveness of these models. This will include describing the features of RPM programs including the nature of services delivered, the
population served, patient demographics of those who were assigned to remote care monitoring (including those not accessing the service, deemed ineligible or inappropriate for the program,
and/or clients no longer accessing the service, hereafter referred to as ‘non-users’) and provider demographics to understand provider-level enablers and barriers for who and how patients are
assigned to remote care.
Examining the Effectiveness of Hospital Command and Control Centers
PI: Prof. Myrtede Alfred
Current Lab Members: Soyun Oh, Yasmeen Smadi, Nicole Scala, Kate Ker
A command and control center (CCC) is a facility (setup, site, premise, establishment) that supports resource deployment and coordination, surveillance, and alert monitoring in a centralized location. CCCs have a long history of use in government and military operations as well as in power generation and air traffic control. In the past several years, CCC have also been adopted by hospitals and health systems across Canada and US including at institutions such as Johns Hopkins Hospital (US), Carillion Clinic (US), and Humber River Hospital (CA). Hospital CCCs have focused primarily on improving patient flow, including transfers, and bed management, however, the development of an effective CCC infrastructure can support a broad range of hospital operations. The purpose of this research is to examine hospital CCCs to evaluate the effectiveness of these centers, the investment costs associated with their development, and the range of operations that are currently monitored or will be monitored in the future. This analysis will help identify best practices in CCC development and support the design of these centers.
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Applications of Extended Reality (XR) in Informed Consent for Patients: A Narrative Review
PI: Prof. Myrtede Alfred
Current Lab Members: Zeina Shaltout, Rob (Hongbo) Chen, Halle Teh, Bella Yang, Joey Lu, Layla Atallah
Former Lab Members: Michelle Lai, Andrew Evanyshyn
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Within healthcare, acquiring informed consent is indispensable, aswhere patients must have a sufficient understanding of their medical procedure before deciding to proceed. Unfortunately, the education patients receive before a procedure is constrained by barriers including poor health literacy, and lack of patient input.
Extended reality (XR), which consists of virtual reality (VR), augmented reality (AR), and mixed reality (MR), has potential to improve patient education and informed consent by creating an immersive, interactive, and multimodal sensory experience that allows patients to develop a better understanding of their treatment options, including surgery..
The purpose of this research was to conduct a narrative review of existing XR tools that may enhance the informed consent process in healthcare. We screened fifty-two articles and ten relevant papers from PubMed, Scopus, and Compendex, were included in the review based on our eligibility criteria.
Optimizing CPR Feedback
PI: Dr. Elaine Gilfoyle
Collaborator: Hospital for Sick Children
Current Lab Members: Nicole Hicks, Francesca Fortino
Former Lab Members: Tochi Oramasionwu, Andreas Constas, Alex Zhang, Wei Fung
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To better understand the links between CPR quality and the use of real-time CPR feedback, this study analyzes the CPR performance of healthcare teams in five simulated CPR cases against the visual attention of CPR providers on the ZOLL R-Series defibrillator feedback device, along with their adherence to AHA guidelines and subjective workloads. This analysis aids in the identification of key factors for improving CPR performance. The simulations were conducted at the Hospital for Sick Children (SickKids) in Toronto Canada. Each simulation team consisted of five members, two of which delivered chest compressions. The visual attention of the compressors was captured using Tobii Pro eye-tracking glasses, while data on the subjective workloads of the team members is gathered using the NASA Task Load Index (NASA-TLX) questionnaire, which is part of the study debrief questionnaire. Lastly, the teams’ adherence to AHA guidelines is assessed based on the number of deviations from the AHA pediatric cardiac arrest algorithm.
The results from this research indicate that in addition to close adherence to AHA guidelines, teams that directed more visual attention towards the real-time feedback delivered higher quality CPR. These factors should thus be given high priority to ensure that chest compressions are delivered to a high standard.
Using Machine Learning to Aid in Retained Foreign Objects Detection
PIs: Prof. Myrtede Alfred, Eldan Cohen
Collaborators: Atrium Health, Andrew Brown (Unity Health), Angela Atinga (Sunnybrook Health Sciences Centre), Birsen Donmez, Ben Wolfe, Anna Kosovicheva
Current Lab Members: Rob (Hongbo) Chen, LinQiao Zhang, Izhaan Junaid
Former Lab Members: Anna Szatan, Russell Mo, Sacha Wachiralappaitoon, Milka Ininahazwe, Andrea Bolanos Mendez
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Retained foreign objects (RFOs) occur when surgical items such as sponges, and instruments are unintentionally left within a patient following a procedure. In Canada, an estimated 9.8 in every 100,000 hospital discharges have RFOs compared to the average rate of 3.8 per 100,000 for other high-income countries. RFOs can significantly impact patients’ health and result in financial and reputational repercussions for healthcare institutions. Radiography is commonly used to prevent and diagnose RFOs, however, 30% - 40% of RFOs are missed during intraoperative reads. This project aims to develop a machine learning algorithm that supports RFO identification and establish a secure, publicly accessible data repository containing a diverse sample of RFO radiographs to support radiology education and decision-making.
