AI-based Prediction Tool for COVID-19 Patient Care
Pooling data to predict outcomes and manage hospitalizations of COVID-19 patients.
Project Budget* - $1.9M
Partner Co-investment - $0.6M
Supercluster Co-investment - $1.3M
The impact of the COVID-19 pandemic has been felt in every part of the globe. Even with screening, monitoring and prevention measures, there have been millions of cases and deaths.
The spectrum of symptoms and impacts seen in hospitalized patients has ranged from mild respiratory symptoms to multi-organ failure and death.
No existing tool gives doctors, administrators and healthcare officials a better understanding of in-hospital health trajectories and a clear view of how to effectively plan patient care. Predictions have been based on tiny patient samples, making them less accurate.
The AI-based Prediction Tool for COVID-19 Patient Care project is collating and sharing a higher-quality dataset of hospitalized COVID-19 patients that is 10 times bigger than before. AI prediction tools are being built that will improve patient outcomes by correlating early signs and symptoms with a long-term prognosis for a patient.
Led by 16 Bit, the project brings together Sunnybrook Health Sciences Centre, London Health Sciences Centre, Layer 6, SofTx Innovations, Roche and the Vector Institute. It’s the first initiative of its kind to bring together high-quality clinical data from COVID-19 hospital patients with powerful AI tools.
This combination of better data and the AI-driven analysis will allow new algorithms to predict COVID-19 outcomes, such as the requirement for ICU admission and the time to mechanical ventilation.
With this tool, frontline clinicians can discover early signs and monitor symptoms that suggest a more severe prognosis, and triage patients based on those predictions. Facility administrators will have a better understanding of resource needs, while patients will have earlier and more informed discussions about the care they will receive.
Next steps for this tool could include continuous data collection via remote monitoring technology for at-risk patients discharged from hospital. This could alert patients, caregivers and providers of ongoing medical management needs to potential COVID-19 health impacts like clotting and cardiovascular issues.