Prospective, Multi-Site Study Of Patient Outcomes After Implementation Of The TREWS Machine Learning-Based Early Warning System For Sepsis
07-21-22 – Nature Medicine – Prospective, Multi-Site Study Of Patient Outcomes After Implementation Of The TREWS Machine Learning-Based Early Warning System For Sepsis
Nature Medicine published three peer-reviewed articles showing how, for the first time, AI has been shown to reduce mortality in a hospital setting using Bayesian Health’s Adaptive AI solution.
The second of three studies demonstrates how higher provider adoption of the deployed sepsis alert system is directly associated with significant reductions in patient mortality, morbidity and hospital cost. The study correlates Bayesian Health’s platform’s precision, model sensitivity, ease of use and integration within the EMR workflow with an 89% sustained adoption by physicians and nurses.
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NOTE – At the core of the research was an AI system referenced as Targeted Real-time Early Warning System (TREWS). Initially developed at Johns Hopkins, Bayesian Health has commercialized and advanced the methodology, integrating it into a broader adaptive AI platform that enables integration, monitoring, and tuning to account for real-world variations in populations and workflows and scaling to multiple condition areas. Bayesian led and managed the deployment across all five emergency departments and hospitals in the study.
