University of Toronto Background
Advances in AI, telemedicine, and wearable sensor technologies (e.g. smartwatches and mobile phones) represent novel and feasible methods for improved disease management and diagnosis through long-term, continuous patient monitoring, and are reshaping healthcare delivery. Despite these advances, there is still a need for clinically-validated, turnkey solutions for clinicians integrate into their care.
From the Department of Computer Science, the Vector Institute for Artificial Intelligence, and proven in U of T’s affiliated hospitals, this technology platform connects doctors to patients and enables real-time insights from wearables.
The platform allows for clinical-grade data analysis from wearable sensors (e.g. smartwatches, mobile devices) through proprietary machine learning (ML)-based algorithms that extract clinically relevant data and filter out unreliable sensor data. This platform can provide real-time feedback on patient health, generate more accurate predictions, and enable actionable insights and recommendations to improve care.
ML-based software to monitor and predict clinically-relevant changes in patient health
Patient mobile app for self-reporting
Clinician dashboard that seamlessly integrates into workflows to provide clinically-relevant patient health metrics and actionable insights
By combining computer science expertise with clinical experience, the platform helps:
Reduce burden on healthcare workers
Improve patient outcomes
Provide peace of mind to patients
The platform has been deployed for remote patient monitoring in 4 hospitals serving over 300 patients for monitoring COVID-19 at home, and for patients with Chronic Obstructive Pulmonary Disease (COPD):
PCT application filed and additional IP protected by trade secrets and copyright
A startup has been formed to commercialize the technologies: Tabiat