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.
Current Projects
Maternal Health & Disparities
PIs: Prof. Myrtede Alfred & Dr. Dulaney Wilson (MUSC)
Collaborators: Medical University of South Carolina (US), University of Texas, El Paso (US), University of Texas, San Antonio (US)
Lab Members: Anna Szatan, Esosa Igbinakenzua, Deenar Virani, Rob (Hongbo) Chen
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A significant portion of maternal deaths and severe maternal morbidity in the US are preventable with timely and appropriate care. The study proposes to conduct a comprehensive systems investigation of maternal care by combining prospective systems analysis based on human factors methods; retrospective analysis of unsafe condition reports; and data analysis of patient safety incidences disaggregated by race/ethnicity. The findings from this research will inform interventions that improve safety in maternal care and reduce racial/ethnic disparities.
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.
Checklists in healthcare – A Systematic Review and Database Project
PI: Prof. Myrtede Alfred
Collaborators: Laura Barg-Walkow, Joe Keebler, Alex Chaparro
Current Lab Members: Matthew Chambers, Izhaan Junaid, Jasmine Zhan
Former Lab Members: Andrea Bolanos Mendez, Lisa Mtui, Skye Wongapisumpo, Jingjing (Isabel) Zhan
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Checklists can support patient care in a range of clinical settings. While many healthcare institutions develop their own, agencies such as the World Health Organization (WHO) have also designed checklists for hospitals worldwide. Although checklists are effective interventions, overuse, and poor design or implementation limit their utility and sustainability in healthcare environments.
The purpose of our research was to examine the effectiveness of checklists utilized in different clinical settings in order to support proper design and implementation of checklists. Additionally, as checklists developed for clinical use may not be disseminated outside the organization or the research community, we collected checklists found in these studies to develop a checklist database.
Link to database: https://borealisdata.ca/dataverse/Checklist_Project
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.
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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.
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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Using Patient Journey Mapping to Design Equitable Maternal Care
PI: Prof. Myrtede Alfred
Current Lab Members: Nicole Hicks, Layla Atallah, LinQiao Zhang
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With over 3.8 million births annually, giving birth is among the most common causes of hospitalization in the United States (US).Despite having the most expensive maternity care in the world, outcomes for US mothers are poor, worsening and reflect deep racial and ethnic health disparities. The maternal mortality rate in the US was 23.8 deaths in 2020; rising from 20.1 in 2019. Women of color are 2-3 times more likely to die and experience higher rates of severe maternal morbidity (SMM). An estimated 45-60% of all maternal deaths and SMM are preventable with timely, respectful, and risk-appropriate care.
Maternity care is a critical time for health care teams to establish and maintain patient trust and for patients to establish healthy habits and positive routines for utilizing clinical and community resources during. Women have identified these goals as requiring thorough and continuous health care support through the continuum of prenatal and postpartum periods.
Documenting patients’ experiences of prenatal, in-patient, and postpartum care through patient journey mapping about their information needs, emotions, and logistics centers the patient perspective on facilitators and barriers to care. This innovative approach is critical to improving maternal care, reducing disparities, and providing women with agency in the redesign of systems of care. Few studies have examined the contribution of clinical systems that disadvantage particular groups to the persistent disparities in maternal health care and adverse outcomes. Therefore, we propose to 1) examine latent threats in maternal care through direct observation and 2) map patients’ care experiences during pregnancy and postpartum to identify modifiable processes and evaluate practices to improve safety, equity, and patient-centered maternal care.
Dashboard Project: Developing and Testing a Visual Feedback Mechanism to Monitor System Equity
PI: Prof. Myrtede Alfred
Current Lab Members: Tosin Akintunde, Fiona Sun, Max Bao
Current systems analysis approaches used in patient safety and risk mitigation do investigate inequities nor account for unique and disproportionate risks for marginalized populations created by systemic design biases. Process and outcome measures are not typically disaggregated or stratified by race/ethnicity, however, doing so facilitates identification of variations, and provide insight on sociotechnical factors contribute to variations in adverse outcomes. My research lab has completed preliminary analysis on maternal health data to statistically test for racial/ethnic inequities in processes and outcome. However, in order to monitor system equity, data analysis results need to be analyzed in real-time and presented to in a manner that facilitates understanding and decision-making. The proposed project will involve designing and test a feedback mechanism (e.g., dashboard) to visually represent the findings of the statistical and qualitative data analysis. The dashboard will include visual analytics that mimic statistical process control charts and distributions to compare expected and actual outcomes according to the defined comparison (racialised women) and reference group (white women), historical outcomes (adverse events), processes (delivery type), and goals (reducing blood transfusions). This tool will be able to support ongoing monitoring of inequitable process and outcome variances for racialised women. The interface will be tested with stakeholders including patient safety coordinators. While the dashboard will not present real-time data, we can examine historical data to establish a baseline to support quarterly or monthly equity assessments.