Enhance your understanding of AI, how it’s being used in family practice, and the potential of AI and other technologies to change how we learn, work, and care for our patients.
Join us in our six part monthly webinar series featuring family medicine leaders in Canada who will share their expertise in machine learning, natural language processing (teaching computers to understand human language), ethics, and AI research.
This one-credit-per hour Group Learning activity has been certified by CFPC for up to one Mainpro+® credit for each webinar session.
Introduction to AI & Applications in Primary Care
December 9, 2020 12-1PM (EST)
Join us, as we kick off the series with Andrew Pinto, clinician-scientist at MAP/C-UHS, Unity Health Toronto, and Jaky Kueper, who is the first combined PhD candidate in Epidemiology and Computer Science at Western University; she is also a recent graduate of the transdisciplinary understanding and training on research in primary health care program (TUTOR-PHC). In general Jaky’s research includes identifying areas of primary health care where machine learning may be useful, and then developing or adapting machine learning methods to support those areas and promote health equity. Jaky’s doctoral research is being done in collaboration with the Alliance for Healthier Communities in Ontario and involves developing novel machine learning methodology for decision support around multi-morbidity care. Her work places a high emphasis on careful consideration of social determinants of health data.
In this session, Jaky and Andrew will delve into an introduction of AI and Health, and discuss how AI is currently applied and being developed for primary care. This webinar will empower attendees to critically appraise AI tools, and understand how to become involved in shaping AI integration in primary care.
Machine Learning to Solve Primary Care Challenges
January 27, 2021 12-1PM (EST)
Dr. Anders Lenskjold is a Danish trained physician with clinical experience in Family, Rural, and Emergency Medicine, Trauma, and Orthopedic Surgery. Lenskjold is also working as a Medical Informatics Consultant with research and health tech industry experience in Machine Learning in healthcare. He is especially interested in advancing healthcare with the appropriate use of AI (Artificial Intelligence) technologies and guiding healthcare providers to limit harm caused by overscreening, overdiagnosing, and overtreating patients.
In this webinar, he will be discussing the use of machine learning to solve primary care challenges.
Machine Learning Applied to Primary Care EMR Data for Classification
February 24, 2021 12-1PM (EST)
Dr. Tyler Williamson is an Associate Professor of Biostatistics, Department of Community Health Sciences and Associate Director of the Centre for Health Informatics at the Cumming School of Medicine, University of Calgary. Dr. Williamson is internationally recognized as an expert in health data science and biostatistics and helped lay some of the methodological foundations for the Canadian Primary Care Sentinel Surveillance Network (CPCSSN), Canada’s first and largest primary care electronic medical record surveillance system. Dr. Williamson serves on the NAPCRG Big Data Task Force, is the past Canadian Co-Char of Research Methods in the Committee in the Advancement of the Science of Family Medicine in NAPCREG, and was winner of the NAPCREG New Investigator Award in 2018.
Stephanie Garies has a PhD in Epidemiology from the Department of Community Health Sciences and is the Assistant Director for the Southern Alberta Primary Care Research Network (SAPCReN) in the Department of Family Medicine, University of Calgary. Her research is focused on methods for improving EMR data quality, including the linkages of EMR and administrative data sources. Matt Taylor is a CPCSSN Data Manager at the University of Alberta, where his focus is on the development of CPCSSN’s machine learning-based electronic medical record coding and cleaning tools.
In this webinar, Dr. Williamson, Dr. Garies, and Dr. Matt Taylor, Data Manager will be sharing their work around use of ML for case definition development and data cleaning algorithms, and discuss why it may be important for those in family practice to be aware of the use of ML applied to EMR data.
Natural Language Processing & its Role in Primary Care
March 31, 2021 12-1PM (EST)
Dr. Noah Crampton is a family physician at the Toronto Western Family Health Team, a health informatician, and a lecturer in the Department of Family and Community Medicine at the University of Toronto. Dr. Crampton’s academic research is focused on the development and evaluation of point-of care software technology tools to improve the delivery of primary care, as well as on technical approaches to integration of patient-level data within a patient’s circle of care.
Dr. Crampton’s previous research includes analyzing the impact of the EMR on patient care, developing an educational program for trainees to learn about EMR data quality and data discipline, and studying how various AI-based technologies such as natural language processing and speech recognition are poised to transform the practice of family medicine. He is involved in developing the EMR data infrastructure for the University of Toronto’s practice-based research network (UTOPIAN), which aggregates EMR data from many clinics across the GTA into one database. In this webinar, Dr. Crampton will discuss the role of natural language processing and its role in primary care.
Machine Learning for Human Resource Management & to Predict Health Service Use
April 21, 2021 12-1PM
Dr. Mamdani is Vice President of Data Science and Advanced Analytics at Unity Health Toronto and Director of the University of Toronto Temerty Faculty of Medicine Centre for Artificial Intelligence Education and Research in Medicine (T-CAIREM). Dr. Mamdani’s team bridges advanced analytics including machine learning with clinical and management decision making to improve patient outcomes and hospital efficiency. Dr. Mamdani is also Professor in the Department of Medicine of the Faculty of Medicine, the Leslie Dan Faculty of Pharmacy, and the Institute of Health Policy, Management and Evaluation of the Dalla Lana Faculty of Public Health. He is also adjunct Senior Scientist at the Institute for Clinical Evaluative Sciences (ICES) and a Faculty Affiliate of the Vector Institute, which is a leading institution for artificial intelligence research in Canada.
Dr. Mamdani was also the Founding Director of the Li Ka Shing Centre for Healthcare Analytics Research and Training (LKS-CHART) of Unity Health Toronto and the Founding Director of the Applied Health Research Centre (AHRC) of the Li Ka Shing Knowledge Institute of Unity Health Toronto, which is Toronto’s leading academic research organization focused on the design and implementation of multicentre clinical research initiatives. In 2010, Dr. Mamdani was named among Canada’s Top 40 under 40.
Dr. Muhammad Mamdani will discuss the role of machine learning for prediction of health service use.
Ethical Concerns with AI & its Application in Primary Care
May 26, 2021 12-1PM EST
Speaker Bio and webinar description to come…
Series Host: Andrew Pinto
Dr. Andrew Pinto is the founder and director of the Upstream Lab at MAP Centre for Urban Health Solutions, a research team focused on integrating health and social care, population health management, and data to enable proactive care. Equity and potential harms are a central focus of this work. Andrew is a public health specialist and family physician at St. Michael’s Hospital and Scientist in the Li Ka Shing Knowledge Institute, Unity Health Toronto. He is an Associate Professor in the DFCM. He serves as Associate Director for Clinical Research at the DFCM’s Practice-Based Research Network (UTOPIAN) and is the AI lead of EXITE (EXploring Innovative TEchnologies in Family Medicine), an innovation collaborative form the University of Toronto’s Department of Family and Community Medicine.
Dr. Pinto will be hosting the series and co-presenting with Jaky Keuper for the Introductory webinar session.
Learn more about the DEEP Network