A new sepsis screening tool developed using machine learning was timelier and more discriminating than several benchmark screening tools, according to data published in the Annals of Emergency Medicine. The new tool, the Risk of Sepsis (RoS) score, was developed using machine learning and compared with benchmark sepsis-screening tools such as the systemic inflammatory response syndrome, sequential organ failure assessment, quick sequential organ failure assessment, modified early warning score, and national early warning score. Investigators used retrospective electronic health record data from adult patients from 49 urban community hospital emergency departments over a 22-month period to derive and test the model. A total of 2,759,529 records were obtained using the Rhee, et al1 standard for clinical surveillance criteria as the definition of sepsis and the primary target for developing the model. The selection process consisted of 3 stages: (1) existing models for sepsis screening were reviewed, (2) consultation with local subject matter experts, and (3) supervised machine learning called gradient boosting.