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Learning Health Systems

The O’Brien Institute for Public Health supports a learning health system that integrates evidence into health decisions

What is a learning health system?

Traditional research is often separate from clinical care and slow to impact practice. 

While knowledge translation moves findings into care, it lacks the continuous feedback and integration. A Learning Health System (LHS) blurs the lines between research and health-care delivery by incorporating research into everyday care, helping health-care providers, patients, administrators and policy makers make informed and timely decisions. 

The O'Brien Institute Learning Health System Accelerator is the engine driving this strategic priority. 

Learn about the LHS Accelerator
Learning health system action framework

Learning health system action framework

The Cumming School of Medicine has adopted the Reid Learning Health System Framework which describes the LHS as an engine with gears that drive research and care delivery. 

"The engine’s five learning gears are animated by formed affinities between researchers and health system operators, the loci where partnerships are built and sustained, and where patients’ and citizens’ insights and perspectives, equitably garnered, are integrated. Learning gears are situated within a larger engine, that specifies the “fuel” and “accelerants” needed for the gears to rotate, produce motion and achieve equity-centered outcomes, as well as “moderators” and “brakes” that change and restrict the engine’s direction and velocity."

Reid et al., Actioning the Learning Health System: An applied framework for integrating research into health systems, SSM - Health Systems, Volume 2, 2024, 100010, ISSN 2949-8562, https://doi.org/10.1016/j.ssmhs.2024.100010.

Components of a learning health system

Learning health systems leverage big data and informatics to improve the integration and accessibility of digital health tools, making health information more readily available to providers, patients, administrators and policy makers. LHSs can significantly address the challenges faced by the Canadian health-care system by fostering continuous improvement and innovation, helping to create a more efficient, equitable and responsive health-care system. 

Analytics & population insights

LHSs employ ‘big data’ from electronic medical records and qualitative sources to develop a comprehensive picture of a patient’s health needs, diagnostics, behaviors, communications, interventions, care processes costs and clinical outcomes. By utilizing data analytics, including artificial intelligence and machine learning, LHSs generate actionable insights to address individual and population health needs.

Evidence synthesis

Evidence synthesis complements population analytics by drawing on existing research to understand what solutions have been tried elsewhere, how effective they are and under what conditions they succeed or fail. These syntheses can take many forms, including quantitative reviews, qualitative analyses, mixed-methods approaches. Beyond assessing the strength of evidence, they help identify which components of interventions are essential and which can be adapted to local contexts, informing guidelines, technology assessments and implementation decisions.

Patient, caregiver, provider & community co-design

LHSs directly engage those impacted by the problem at hand, including patients, caregivers, care providers, community members, healthcare professionals, managers and health system operators. Co-design activities must consider cross-sectoral approaches and build trusting relationships, paying attention to power differences among participants, especially those from equity-deserving groups. Early engagement, expectation management and assessing learning needs are crucial, along with removing barriers to engagement.  

Implementation

LHSs employ implementation science methods to systematically design interventions and improve health service effectiveness. This involves understanding current behaviors, identifying strategies for desired behavior change and specifying implementation details.  

Evaluation, feedback and adaptation

LHS approaches rely on realist evaluation methods to measure the effectiveness of multicomponent interventions for specific patient populations under different conditions. Evaluations help identify and eliminate inefficient care processes, creating new capacity. 

The health system

Canada's health system is facing increasing pressures. Health systems need to rapidly learn and apply evidence to combat these pressures. 

While efforts are being made to modernize the health system through digital tools, there is still a need for better integration and access to health information.   

Family physician shortage

Many Canadians do not have a family doctor or nurse practitioner they can see regularly.

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ER overcrowding

Emergency rooms are often overcrowded, leading to long wait times.

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Workforce shortage and burnout

There is a pervasive shortage of health-care workers, including doctors and nurses, which is exacerbated by burnout and high levels of stress among existing staff.

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Inequity in care

Patients experience inequities in receiving care. 

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Advancing the Quintuple Aim

Overall, the Learning Health System approach advances the Quintuple Aim by enhancing the patient and family experience, improving patient and population health, enriching health care provider experience, ensuring value for money and promoting health equity.  

The Cumming School of Medicine and the O'Brien Institute for Public Health aim to support Alberta's health system in delivering on the Quintuple Aim, and to become a leading integrated health data analytics centre in Canada. We will pursue this by integrating research into best clinical practice, engaging leadership across Alberta's four provincial health agencies and the broader learning health system, and applying data science, predictive analytics and clinical research.