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Learning Health System

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Learning Health System (LHS) for smoking and other concurrent high risk behaviours​​
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A system for continuous cycles of research, analysis, development and implementation of improvements to achieve better health for individuals and improved performance for healthcare systems. 

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​▶ Digitally-enabled for fast and wide scale-up

​▶ Adaptable to many healthcare & non-healthcare settings​​


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Aim​​​​

​Improve upstream prevention practices in primary care by targeting smoking and clustered social & biological determinants of health for all patients across the lifespan. 

SMOKING + HAZARDOUS DRINKING; DEPRESSION; PHYSICAL INACTIVITY; POOR NUTRITION​

A growing platform that adds other interventions​

Outcome

Improve patient and provider experience; Improve health outcome; Reduce cost of implementation 

Scale​​

300 primary care & community medicine organizations   |   Urban, rural, academic & non-academic settings   |   Over 1,100 practitioners   |   ~ 25​​,000 patients annually   |   Data from over 325,000 treated patients 



​Components of our LHS

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DATA COLLECTION AND DATA ACCESS (ASSEMBLE)

​​S​calable, cloud-implemente​d web-based application for data capture 
Self-administered and provider-administered instruments and surveys 
Automated follow-up outcome data collection
Standard Operating Procedures and ethics reviews to govern access to data
Privacy Impact Analysis & Threat Risk Assessments for data privacy & technical safeguards 
Customizable software for unique program needs



TECHNOLOGY FOR AGGREGATING AND ANALYZING DATA (ANALYZE): 

Data extracts with patient-level data
Ability to aggregate data at organization and system level; by geography
Linkages with external databases

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MAKE KNOWLEDGE PERSISTENT AND SHAREABLE (INTEPRET):

Validate results of data analysis for clinical relevance
Iterate the software to integrate new findings into clinical pathways and process workflows
Scalable web-based application for simple to complex workflows and rapidly deploying them in large number of organizations provincially and nationally
Direct to patient care delivery workflows
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MECHANISM FOR TAILORING MESSAGE TO DECISION MAKERS (FEEDBACK):

Increased personalization of care through automated clinical decision support driven by algorithms acting on person-level data to produce evidence-informed, on-screen and just-in-time guidance for providers
Aggregate outcome and performance reports through dashboard analytics and customized analysis reports​
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MECHANISM FOR CAPTURING CHANGED PRACTICE (CHANGE):

Mixed method evaluation framework for collecting data on provider attitude, and new workflow and intervention adoption 
Mix of user-input and passive data collection on resource access, pathway implementation on the platform and patient acceptance of intervention
Ability of the platform to enable execution and evaluation of multiple intervention workflows


Explore more...

​To learn more about our LHS, discuss its adoption to your context or collaborate with us please request a meeting or send a message to:

Dr. Peter Selby at: peter.selby@camh.ca   

Sarwar Hussain at: sarwar.hussain@camh.ca ​​​

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