Subpopulation-Specific Sepsis Identification Using Machine Learning

The focus of this study will be to conduct a prospective, randomized controlled trial (RCT) at Cape Regional Medical Center (CRMC), Oroville Hospital (OH), and UCSF Medical Center (UCSF) in which a subpopulation-optimized algorithm will be applied to EHR data for the detection of severe sepsis. For patients determined to have a high risk of severe sepsis, the algorithm will generate automated voice, telephone notification to nursing staff at CRMC, OH, and UCSF. The algorithm's performance will be measured by analysis of the primary endpoint, in-hospital SIRS-based mortality. The secondary endpoints will be in-hospital severe sepsis/shock-coded mortality, SIRS-based hospital length of stay, and severe sepsis/shock-coded hospital length of stay.