Machine-Learning Model Predicts Risk of Pediatric Deterioration

Nationwide Children's Hospital researchers utilized a machine- learning tool with an EHR-integrated risk index algorithm to alert providers of early pediatric deterioration. Nationwide Children's Hospital developed and deployed a machine-learning (ML) model that uses the deterioration risk index to promptly predict hospitalized children at risk for pediatric deterioration earlier than previously implemented programs, according to a study published in the Pediatric Critical Care Medicine journal. Earlier identification of high-risk patients is crucial in preventing adverse events and code blue situations, as patient deterioration can rapidly escalate from seemingly ordinary to critical. For organizations that see large numbers of medically complex patients, risk-scoring methods are particularly helpful. The Deterioration Risk Index (DRI), based on a Watchstander program already used at Nationwide Children's Hospital, leverages familiar alert responses to promote adoption, such as patient assessments, care team huddles within 30 minutes, risk mitigation, and escalation plans.