A landmark study published in NEJM AI reveals that an artificial intelligence-enabled early warning system successfully identified hospitalized patients at risk of rapid clinical decline sooner, resulting in a significant reduction in in-hospital mortality.
Conducted by researchers from RWJBarnabas Health and Rutgers Robert Wood Johnson Medical School, the study evaluated outcomes among 23,132 high-risk patients across 11 RWJBarnabas Health hospitals. Following the implementation of the AI tool, deaths among high-risk patients dropped from 23.1% to 18.6%, representing an 18% reduction in risk-adjusted odds of death.
The research evaluated the Epic Deterioration Index (EDI), a tool that continuously evaluates existing electronic health record data—including vital signs, lab results, nursing assessments, and patient age—every 15 minutes. When patients reached the highest-risk threshold, the system automatically notified rapid response teams.
“Our goal was to identify patients earlier, before they reached a point where intervention becomes much more difficult,” said Dr. Thomas Nahass, VP of Health Informatics and intensive care physician at RWJBarnabas Health, Assistant Professor of Medicine at Rutgers Robert Wood Johnson Medical School, and lead author of the study. “The deterioration index gives us an earlier point in time. If we can get a critical care eye on the patient sooner, we can change the course of their outcome.”
Following the rollout, rapid response activations among high-risk patients rose from 25.3% to 37.5% of hospital stays. Notably, intensive care unit transfers did not significantly increase, highlighting that earlier interventions helped stabilize patients proactively.
Because the tool is already embedded within Epic—one of the nation’s most widely used electronic health record platforms—researchers believe these findings have major implications for hospitals nationwide looking to enhance patient safety and clinical outcomes through coordinated technological partnerships.
