Machine-Learning Model Helped Streamline 22% of Pediatric ED Visits
Machine learning and novel workflows enhanced test ordering processes within a pediatric emergency department, thereby streamlining care, a new study shows.
- While exploring the possibilities of integrating machine learning into clinical decision-making, a JAMA Network study found that novel machine learning-driven workflows helped improve test ordering within pediatric emergency departments (EDs).
The study included 42,238 boys up to the age of 18, all of whom came from the emergency department of the Hospital for Sick Children in Toronto, Canada. There were 77,219 patient visits, all of which took place between July 1, 2018, and June 30, 2019.
Machine-learning models predicted the need for services such as urinary dipstick testing, electrocardiogram, abdominal ultrasonography, bilirubin level testing, and forearm radiographs.
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