Researchers from RWJBarnabas Health and Rutgers Robert Wood Johnson Medical School found that an artificial intelligence-enabled early warning system helped identify hospitalized patients at risk of rapid clinical decline sooner, contributing to fewer deaths among high-risk patients, according to a July 29 report.
The study, published in NEJM AI, evaluated outcomes among 23,132 high-risk patients across 11 RWJBarnabas Health hospitals. Deaths among these patients fell from 23.1 percent to 18.6 percent after the implementation of the AI-enabled early warning system, representing an 18 percent reduction in the risk-adjusted odds of in-hospital death.
Researchers assessed the Epic Deterioration Index (EDI), an AI-enabled tool that continuously analyzes information already captured in electronic health records—such as vital signs, laboratory results, nursing assessments and age—to identify patients at increased risk of serious clinical decline. The EDI recalculates risk scores every 15 minutes and automatically alerts rapid response teams when a patient reaches the highest-risk category.
Before evaluating this technology in their study, RWJBarnabas Health and Rutgers developed and implemented a systemwide approach for using the EDI across its hospitals. This process included piloting the tool at Robert Wood Johnson University Hospital to refine alert delivery methods and notifications for rapid response teams, training clinicians on its use, and monitoring performance before rolling out the platform to other hospitals.
Following implementation of automated notifications sent directly to hospital rapid response teams when patients reached highest-risk thresholds, activations among high-risk patient stays increased from 25.3 percent to 37.5 percent. Despite more frequent evaluations by rapid response teams after alerts were triggered by rising EDI scores, transfers to intensive care units did not significantly increase, while mortality rates declined substantially.
"Every minute matters when a patient's condition begins to worsen," said Andy Anderson, MD, Chief Medical and Quality Officer at RWJBarnabas Health and co-author of the study. "This study demonstrates how AI-enabled tools, when paired with experienced clinical teams, can help us identify patients at risk sooner and deliver the right care at the right time. These findings highlight the potential for innovation to improve quality, safety and outcomes for the patients we serve." Stephen P. O'Mahony, MD, said, "We combined Rutgers methodological rigor with the operational reach of 11 RWJBarnabas hospitals. The mortality benefit was not produced by an algorithm but by the partnership around the algorithm." Researchers noted that factors such as staff education on alerts contributed alongside technological advances.
