As the coronavirus pandemic brings floods of people to hospital emergency rooms around the world, physicians are struggling to triage patients, trying to determine which ones will need intensive care. Volunteer doctors and nurses with no special pulmonary training must assess the condition of patients’ lungs. In Italy, at the peak of that country’s crisis, doctors faced terrible decisions about who should receive help and resources. 

Bayesian Health, a stealth-mode startup that spun out of Johns Hopkins University, previously developed a targeted prediction tool for sepsis, a life-threatening infection. Recently the team has been working on an early warning system for acute respiratory distress syndrome, a type of respiratory failure that can be caused by many diseases, including sepsis, pneumonia—and COVID. 

“The tool is already monitoring patients across five community and academic hospitals,” says Suchi Saria, founder and CEO of Bayesian Health and director of the machine learning and healthcare lab at Johns Hopkins. “We’ll be making this tool available for hospitals nationally to use.” 

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