Factors Driving Provider Adoption Of The TREWS Machine Learning-Based Early Warning System And Its Effects On Sepsis Treatment Timing
07-21-22 – Nature Medicine – Factors Driving Provider Adoption Of The TREWS Machine Learning-Based Early Warning System And Its Effects On Sepsis Treatment Timing
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 first of three studies, centered on characterizing system accuracy, provider adoption, and impact of adoption of treatment timing. It was conducted over a two-year period at five hospitals from both academic and community-based hospital settings. The study looked at front-line usage by 2,000+ providers.
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.