The study evaluated a machine learning-enabled clinical decision support tool called the Passive Digital Marker, which uses routinely collected EHR data to classify young children as having a high or low risk of developing persistent asthma.

Asthma is one of the most common chronic childhood diseases, but predicting which young children with wheezing or other respiratory symptoms will go on to develop persistent asthma remains difficult. While some children outgrow these symptoms, others require ongoing treatment, making early risk assessment an important but challenging part of pediatric care.

“This tool doesn’t replace a pediatrician’s clinical judgment,” said Arthur H. Owora, Ph.D., Regenstrief Institute research scientist and lead author of the study. “It helps bring together years of clinical information that’s already in the electronic health record, giving clinicians another source of information when making decisions about a child’s asthma risk.”