AI Early Warning Tool Cut Hospital Deaths by 18%: Landmark Rutgers & RWJBarnabas Study Reveals
the staff of the Ridgewood blog
New Brunswick NJ, Artificial intelligence in healthcare is no longer just a promise for the future—it is actively saving lives in hospitals today.
A groundbreaking study published in NEJM AI reveals that an AI-powered early warning system significantly reduced patient mortality across 11 New Jersey hospitals within the RWJBarnabas Health network. Conducted in partnership with Rutgers Robert Wood Johnson Medical School, the research demonstrates how predictive technology and clinical teamwork can spot patient deterioration before it becomes fatal.
The Stats: An 18% Reduction in In-Hospital Mortality
The study tracked outcomes across 23,132 high-risk adult patients in academic medical centers, community teaching hospitals, and regional healthcare facilities.
The results were striking:
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Mortality Rate Drop: In-hospital deaths among high-risk patients fell from 23.1% down to 18.6% after the system was deployed.
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Risk Reduction: This shift represents an 18% reduction in the risk-adjusted odds of in-hospital death.
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Proactive Response: Rapid response team activations jumped from 25.3% to 37.5% of hospital stays, ensuring doctors and nurses stepped in earlier.
Crucially, despite the rise in evaluations, transfers to intensive care units (ICUs) did not surge—proving that early intervention allowed care teams to stabilize patients on general floors without overwhelming ICU resources.
How the AI Tool Works: Spotting Signs Every 15 Minutes
At the center of this initiative is the Epic Deterioration Index, an AI algorithm directly embedded into patient electronic health records (EHR).
Instead of waiting for visible symptoms, the algorithm continuously analyzes existing clinical data, including:
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Vital signs
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Laboratory test results
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Nursing assessments
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Patient age
Recalculating risk scores every 15 minutes, the system automatically alerts hospital rapid response teams the moment a patient enters a critical risk threshold.
“Our goal was to identify patients earlier, before they reached a point where intervention becomes much more difficult,” explained Dr. Thomas Nahass, lead author of the study, Vice President of Health Informatics at RWJBarnabas Health, and intensive care physician. “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.”
It’s Not Just the Algorithm—It’s the Partnership
While the technology is advanced, researchers emphasize that software alone doesn’t save lives. Success required years of preparation, workflow refinement, and clinician training.
RWJBarnabas Health first piloted the system at Robert Wood Johnson University Hospital to fine-tune alert thresholds, train clinical staff, and automate response workflows before expanding it across 10 other network hospitals.
“This is what an integrated academic health system is for,” noted Dr. Stephen P. O’Mahony, Senior Vice President and senior author of the study. “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 pinpointed four key factors driving the mortality drop:
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Continuous clinical awareness
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Dedicated staff education
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Automated electronic health record alerts
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Instant notification to rapid response units
What’s Next for AI in Patient Care?
With the initial rollout showing clear success, researchers are launching the next phase of the project: identifying patients whose risk scores are rising rapidly, even if they haven’t yet hit the highest threshold. The goal is simple—move intervention times even earlier to save more lives.
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