An AI-based early warning system helped identify hospitalized patients at risk of rapid clinical decline sooner, contributing to fewer deaths among high-risk patients, according to research from RWJBarnabas Health and Rutgers Robert Wood Johnson Medical School in New Jersey. 

Published in NEJM AI, the study evaluated outcomes among 23,132 high-risk patients across 11 RWJBarnabas Health hospitals. Deaths among high-risk patients fell from 23.1 percent to 18.6 percent following implementation of the AI-enabled early warning system, representing an 18 percent reduction in the risk-adjusted odds of in-hospital death.

Researchers evaluated the Epic Deterioration Index (EDI), an AI-enabled tool that continuously analyzes information already captured in the electronic health record, including vital signs, laboratory results, nursing assessments and age, to identify patients at increased risk of serious clinical decline. The system recalculates risk scores every 15 minutes and automatically alerts rapid response teams when patients reach the highest-risk category.

Before evaluating the technology in this study, RWJBarnabas Health and Rutgers spent several years developing and implementing a systemwide approach to using the EDI across its hospitals. The health system first integrated the tool into its EHR at its academic medical center, Robert Wood Johnson University Hospital, to pilot and refine it. They focused on how and when alerts were delivered, established automatic notifications to rapid response teams, trained clinicians on its use and continuously monitored its performance. Researchers from Rutgers then partnered with RWJBarnabas Health to evaluate the impact of this approach in real world clinical practice and rapidly rolled out the platform across the other 10 hospitals.

 “Our goal was to identify patients earlier, before they reached a point where intervention becomes much more difficult,” said Thomas Nahass, M.D., vice president of  health informatics and intensive care physician at RWJBarnabas Health, in a statement. “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, added Nahass, who also is an assistant professor of medicine at Rutgers Robert Wood Johnson Medical School and lead author of the study. 

When patients reached the highest-risk threshold, automated notifications were sent directly to hospital rapid response teams, enabling critical care specialists to quickly assess patients and determine whether additional interventions were needed. Following implementation, rapid response team activations among high-risk patients increased from 25.3 percent of hospital stays to 37.5 percent.

The study evaluated outcomes among high-risk adult patients receiving care at academic medical centers, community teaching hospitals and community hospitals throughout the RWJBarnabas Health system. Despite the increase in rapid response evaluations, 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, M.D., chief medical and quality officer at RWJBarnabas Health and study co-author, in a statement. “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.”

Researchers note that the mortality benefit likely resulted from a combination of factors, including staff education, enhanced clinical awareness, EHR alerts and automated rapid response team notifications working together as a coordinated systemwide approach. 

The authors add that because the study evaluated the Epic Deterioration Index, a tool already available within Epic, the findings may have implications for hospitals nationwide seeking to improve patient outcomes. Researchers are now evaluating the next phase of the initiative, which focuses on identifying patients whose risk scores are rising rapidly in hopes of enabling even earlier intervention.