False alarms. Complicated integrations. Lack of adoption. Unclear impact.
Getting AI to work in healthcare hasn’t been easy.
Best-in-class AI/machine learning
Intuitive user experience with high adoption
Focus on organizational performance optimization
Lightweight implementation
Measurable, proven outcomes*
Sensitive, precise signals
Decades of published research* has validated our machine learning strategies.
And we’re continuing to push the boundaries of what defines “best in class”, healthcare-based machine learning both in research and in practice.
Creating sustained value
Better health outcomes*
Improved patient, provider experience*
Clear financial impact
*Adams, Henry, Saria et al. Nature Medicine, 2022;
*Henry, Adams, Saria et al., Nature Medicine, 2022
Latest Research from Bayesian Health
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…
Human-Machine Teaming Is Key To AI Adoption: Clinicians’ Experiences With A Deployed Machine Learning System
07-21-22 - Nature Medicine - Human-Machine Teaming Is Key To…
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…
