Background: The use of early warning systems (EWS) to augment clinical care is of increasing interest with recent publications showing EWS algorithm alerts coupled with clinical response actions to have significant morbidity and mortality benefit.1 At Stanford Hospital we are developing an alert and response EWS using a machine learning (ML) model predicting clinical deterioration [...]

Background: Numerous early warning systems (EWS) exist as potential tools to improve patient safety. Our system recognized higher than peer rates of rapid response (RRT) utilization as well as higher than desired out-of-ICU code blue rates leading to a desire to implement a EWS system. Over a three-year period we reviewed the literature, developed, and [...]

Background: Sepsis is a leading cause of death among hospitalized patients. Early detection of sepsis has the potential to reduce mortality by facilitating timely evidence-based interventions. Past studies have used electronic health records (EHR) to trigger alerts at the onset of sepsis, or to predict general clinical deterioration. In this study we describe the impact [...]

Background: Sepsis is a leading cause of mortality among hospitalized patients. Early detection and intervention reduces sepsis-related mortality. We implemented a novel early warning system (EWS 2.0) based on a machine-learning algorithm to prospectively identify patients with increased risk of severe sepsis or septic shock. Validation suggested excellent predictive characteristics, including a positive likelihood ratio [...]