As a nurse, Megan Foradori tried to make sure no child in need of developmental support fell through the cracks.
Across pediatric nursing roles in multiple states, she saw firsthand how babies and toddlers are screened for crucial social, speech and developmental milestones—and fiercely advocated for services for children who scored poorly.
“These services can transform their futures—especially at key periods of brain and physical growth,” said Foradori, a PhD candidate at Case Western Reserve University Frances Payne Bolton School of Nursing. “But I also saw service providers stretched too thin and doctors hesitating to deliver potentially distressing news to parents—instead hoping the child might outgrow the issue.”
The experiences inspired Foradori to focus her PhD thesis to better identify patterns on which children are screened and receive services—and determine who is being missed along the way. She is using machine learning algorithms—a form of artificial intelligence (AI)—to analyze large datasets, including the National Survey of Children’s Health from the U.S. Health Resources and Services Administration.
“Each child in the data has a unique constellation of characteristics,” said Foradori.
“Machine learning helps us drill down and see drivers of outcomes we couldn’t have seen ourselves because of the sheer volume of data and the complexity of each child.”
From her research, Foradori is aiming to create new clinical guidance to help more children receive key interventions—such as speech and behavioral therapy—before entering kindergarten, which research shows can significantly improve their long-term development.
“My personal experiences as a nurse showed me the realities of how kids can get left behind,” she said. “Now as a researcher, I can pair my nursing background with deep data analysis to find ways to help children when they need it the most.”