AI-Powered Remote Patient Monitoring Platform

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.
Technology Overview
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.
Key features:

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
Reduce costs

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

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