Deployed sepsis-prediction models degrade after go-live

A peer-reviewed review in Frontiers in Medicine documents a pattern governance leads should treat as the default, not the exception: clinical AI degrades after deployment. The authors note that the real-world performance of already-deployed sepsis early-warning systems, including Epic's Sepsis Prediction Model, may be lower than reported during the development phase, and that data drift over time and across sites erodes accuracy. A model validated once, at go-live, is not validated forever. The practical implication: post-deployment monitoring and periodic revalidation belong in the standard operating procedure for any predictive tool touching care, the same way you would not run a security control once and assume it holds for years.