Making it Personal: AI, Knowledge Management and the Future of Canadian Healthcare

 

 

"Canada's healthcare system stands at a critical crossroads. For decades, our universal healthcare has been a source of national pride and identity, representing our collective belief that access to quality medical care is a fundamental right for all citizens. However, this once-sturdy pillar of Canadian society now faces unprecedented challenges that threaten its sustainability and effectiveness in serving the population it was designed to protect"

 

The symptoms of crisis are increasingly visible across the country. Emergency departments are overwhelmed, with patients often waiting hours or even days for care. Wait times for specialists and surgical procedures continue to stretch into months and sometimes years. Perhaps most alarmingly, millions of Canadians lack access to a family physician, the traditional entry point and coordinator of care within our system. These challenges are not merely inconveniences—they represent real barriers to timely, effective healthcare that can profoundly impact patient outcomes and quality of life.

The causes behind this healthcare crisis are multifaceted and complex. Canada's population is aging rapidly, creating increased demand for services at precisely the time when healthcare resources are already stretched thin. Chronic under-funding has left many facilities struggling with outdated equipment and insufficient staffing levels. The geographic vastness of our nation creates particular challenges for rural and remote communities, where access to specialists and advanced care options may be severely limited. Adding to these challenges is the fragmented nature of our provincial healthcare systems, which often operate in silos with limited data sharing or coordination—a structural inefficiency that leads to duplicated services, gaps in care, and frustration for both patients and providers.

Healthcare professionals themselves are feeling the strain acutely. Burnout rates among doctors, nurses, and other front-line staff have reached alarming levels, exacerbated by the COVID-19 pandemic but rooted in longer-standing issues of workload, bureaucracy, and system inefficiencies. These dedicated professionals find themselves trapped in a system that often seems designed around administrative requirements rather than patient needs or provider well-being.

Yet amidst these challenges, innovative approaches are emerging that could fundamentally transform how healthcare is delivered in Canada. Abbas Zavar M.D. from the University of Toronto's Institute of Health Policy, Management and Evaluation is pioneering research into personalized medicine approaches that leverage advanced technologies, to create more targeted, efficient care models. Rather than treating patients based solely on broad population averages, personalized medicine considers the unique genetic, environmental, and lifestyle factors that influence health outcomes for each individual.

Central to Abbas Zavar's vision is the strategic application of artificial intelligence to collect and analyze vast amounts of health data from diverse sources. By applying sophisticated knowledge management methodologies, this data can be transformed into actionable insights that empower physicians to make more informed, personalized treatment decisions. This approach holds promise not only for improving individual patient outcomes but also for reducing system strain by decreasing complications, re-admissions, and unnecessary treatments.

The potential benefits of this paradigm shift extend beyond clinical outcomes to the overall efficiency and sustainability of our healthcare system. By targeting interventions more precisely to patient needs, resources can be allocated more effectively, potentially reducing wait times and improving access for all Canadians. Moreover, by equipping healthcare providers with better decision-support tools, this approach could help address burnout by making their work more effective and satisfying.

Part 2: Making it Personal: How AI and Knowledge Management Can Transform Canadian Healthcare

Our current healthcare model waits for patients to become sick before intervention. This reactive approach creates the bottlenecks we see throughout the system – from overcrowded emergency rooms to lengthy specialist wait-lists. Zavar proposes flipping this model by leveraging data to identify individual risk factors before illness develops. "We are missing the opportunity of the healthcare system to be proactive, identify risk factors for each individual, and proactively support them before they need healthcare system or face a medical emergency," he explains.

At the heart of Abbas Zavar's research is the recognition that effective healthcare requires a holistic view of each patient. He identifies five critical data domains necessary for truly personalized medicine: health data (including hospital records, lab results, imaging, pharmacy records), omics data (genomics, proteomics, metabolomics), lifestyle factors (diet, activity, sleep patterns), environmental factors (exposure to toxins, radiation, biological agents), and social determinants of health (disabilities, gender identity, food insecurity, access to services).

The challenge, of course, is that this data currently exists in silos, with limited interoperability between systems. Even when digitized, different hospitals and healthcare providers often use different standards, making information sharing difficult. Provincial boundaries further complicate matters, with patients unable to access care in neighbouring provinces despite geographical proximity.

This is where artificial intelligence and knowledge management enter the picture. Zavar prefers the term "augmented intelligence" over "artificial intelligence," positioning AI as a tool to enhance physician capabilities rather than replace them. "AI can connect all those silos of information, connect dots, find risk factors, find patterns, and offer some suggestions," he explains.

The application of knowledge management principles is central to  Zavar's vision. He defines knowledge management as "providing the right knowledge to the right person at the right time" – a definition that aligns perfectly with clinical decision support systems. By transforming raw data into actionable knowledge available at the point of care, AI can help physicians become more knowledgeable practitioners and assist them in making better, more personalized decisions for each patient.

Abbas Zavar shares an example of a system that integrates genomic information with electronic medical records to guide medical treatment. For example, when prescribing an antidepressant, the system identifies which medications might be contraindicated for a specific patient based on their genetic profile, which options should be used with caution, and which would be most effective. This level of personalization represents just the beginning of what's possible when we combine comprehensive patient data with AI-powered clinical decision support.

Perhaps most importantly, these technologies could free physicians from administrative burdens that currently consume up to 20 additional hours per week beyond clinical time. AI tools can handle documentation, clean up messy EMR inboxes, suggest appropriate billing codes, and gather missing information – allowing doctors to focus on the human elements of care that machines cannot replicate: empathy, compassion, and meaningful patient engagement.

The barriers to implementing this vision aren't primarily technological – they're regulatory and policy-based. As  Zavar notes, "We are really slow in policy making." The rapid advancement of AI technology has outpaced our ability to create appropriate guardrails and frameworks for its use in healthcare. 

 

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About Abbas Zavar

Abbas Zavar is a pioneering physician and digital health leader with over two decades of experience in medicine and health information technology. His career encompasses clinical practice, digital health consulting, and program management, showcasing his tech-savvy expertise and unwavering commitment to transforming healthcare.

• Researcher: Leading several research projects, Abbas explores PM’s features and challenges with a passion for creating a ‘PM Ecosystem’ utilizing advanced clinical data sciences, AI, Machine Learning, predictive analysis, knowledge management, and clinical business intelligence.

• Leader: As the Digital Health Research Lead at OntarioMD, he evaluates and supports implementing AI-enabled solutions for community-based clinicians, ensuring alignment with the PM approach.

Abbas holds a Doctorate of Medicine (MD), a Master’s in Public Health (MPH), and a Master’s in Health Informatics (MHI) from the University of Toronto. His proficiency in clinical data sciences, Augmented Intelligence (AI), and clinical decision support systems (CDSS) enriches his academic foundation.

Abbas is a staunch advocate for the Personalized Medicine (PM) approach, which he defines as delivering the right clinical intervention—diagnosis, prevention, and treatment—to the right individual at the right time. He champions this vision across various roles in the community: