
Improving ICU Capacity During COVID-19 Outbreaks
Harnessing AI to better manage ICU capacity during crisis.
Project Overview
The Problem
Access to intensive care is critical for public health.
Prior to the COVID-19 pandemic, Canadian intensive care units (ICUs) were already operating at about 90 per cent capacity and exceeded capacity for about 50 days a year. ICU overcrowding causes delays in critical care for patients that need it most – every hour that ICU admission is delayed for a patient result in a 1.5 per cent increase in risk of death.
Respiratory infections such as pneumonia and influenza account for 20 per cent of ICU admissions and were a leading cause of death worldwide prior to COVID-19. With COVID-19 driving an increased demand for the ventilators, specialized treatments and close monitoring by doctors in ICUs, the entire health system is at greater threat of being overwhelmed.
How We Are Solving It
Project Lead
Project Partners
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"Data-driven tools that predict patients’ risk of deterioration have the potential to affect more proactive care and anticipate operational needs to ensure that ICU capacity is available to patients who need it. Medical imaging already plays a key part in the standard of care, but we expect that our machine learning tools will help generate further actionable prognostic insights. Once the interim results are available, we will be able to move forward for prospective testing at Canadian hospitals.”
Read the Announcement (April 8, 2021) Founder & CEO, Altis Labs