Training digital health innovators with the CanPath Synthetic Dataset
What happens when students are given access to real-world population health data and the freedom to imagine new digital solutions?
On November 13–14, more than 100 students, mentors, and researchers gathered at the Mila Quebec Artificial Intelligence (AI) Institute for the 1st Digital Health Hackathon organized by Réseau Santé Numérique (RSN). The collaborative event introduced students to working with population-level health data, challenged them to explore pressing public health issues, and invited them to propose their own digital solutions.
Participants worked with datasets such as the CanPath Synthetic Dataset, allowing them to gain hands-on experience with large-scale health data while learning fundamental principles of data analysis without accessing real participant information.
CanPath Data Manager, Sheraz Cheema, led an educational workshop on data preparation and Tina Wey, a Data Analyst at Maelstrom Research and CanPath collaborator, provided hands-on support with students’ analysis at the Hackathon.
Student solutions addressing real-world health challenges
The hackathon concluded with a range of thoughtful, data-driven solutions that reflected both strong technical skills and a clear understanding of public health priorities.
First place was awarded to SensAir, which proposed a personalized air-quality assessment tool that integrates individual characteristics to help users better understand environmental health risks.
Second place went to FuneStarProvider, a platform offering real-time mapping of climate-related health risks to support emergency response planning and protect vulnerable populations.
Third place was awarded to AgentCare and Refeedo. AgentCare is an ecosystem of AI agents designed to support frontline mental health workers by providing timely access to simulated, secure, and youth-friendly professional expertise. Refeedo examined the relationship between digital media consumption and mental well-being using population-level data.
Together, these projects demonstrated how accessible, privacy-preserving data resources, such as the CanPath Synthetic Dataset, can support experiential learning and innovation in digital health.
Supporting training and capacity-building in health research
As the landscape of health research continues to evolve, CanPath remains committed to supporting training initiatives that equip students, trainees, and early-career researchers with the tools needed to work confidently with population-level data.
Interested in using the CanPath Synthetic Dataset for a course, workshop, or trainee event?
For more information please contact:
Megan Fleming
Communications & Knowledge Translation Officer
Canadian Partnership for Tomorrow’s Health (CanPath)
info@canpath.ca