Delivering 1.85hr earlier treatment for sepsis at Johns Hopkins

Bayesian Health’s platform drove faster treatment for sepsis, with high provider adoption.

A large, five site study analyzing use and practice impact over two years for Bayesian Health’s sepsis module showed high sensitivity (80%+) with high precision (1 in 3 alerts were provider confirmed). Sepsis is a needle in a haystack problem, so it’s hard to achieve high precision at high sensitivity, and especially harder where you’re also optimizing for earlier detection times. But all three of these things are critical to cracking the code on earlier sepsis recognition and prevention.

Bayesian’s high quality, timely clinical signals resulted in 1.85 hour faster life-saving patient treatment driven by high provider adoption (89%).

With research showing the average adoption of clinical decision support tools to be in the low double-digits, our high adoption is exciting news, and shows physician confidence and trust in the Bayesian insights, leading to proactive, earlier care. This is great news for patient outcomes, where for sepsis every hour of delayed treatment directly impacts mortality rate.

Read the full prospective outcome study on Bayesian Health’s platform targeting sepsis, “Evaluating Adoption, Impact, and Factors Driving Adoption for TREWS, a Machine Learning-Based Sepsis Alerting System,” here.