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Image Credit: Scientific Frontline |
When using only data collected before patients with sepsis received treatments or medical tests, the model’s accuracy was no better than a coin toss
Proprietary artificial intelligence software designed to be an early warning system for sepsis can’t differentiate high and low risk patients before they receive treatments, according to a new study from the University of Michigan.
The tool, named the Epic Sepsis Model, is part of Epic’s electronic medical record software, which serves 54% of patients in the United States and 2.5% of patients internationally, according to a statement from the company’s CEO reported by the Wisconsin State Journal. It automatically generates sepsis risk estimates in the records of hospitalized patients every 20 minutes, which clinicians hope can allow them to detect when a patient might get sepsis before things go bad.
“Sepsis has all these vague symptoms, so when a patient shows up with an infection, it can be really hard to know who can be sent home with some antibiotics and who might need to stay in the intensive care unit. We still miss a lot of patients with sepsis,” said Tom Valley, associate professor in pulmonary and critical care medicine, ICU clinician and co-author of the study published recently in the New England Journal of Medicine AI.