Analysis and prediction platform for patient monitoring

Stanford researchers have developed a software platform that integrates electronic health record (EHR) data with continuous physiologic monitoring to support real-time clinical decision-making in emergency medicine. The system leverages multimodal machine learning to deliver early insights on patient trajectory, including predicted admission needs and risk of clinical deterioration. These predictive insights are visualized in context through intuitive user interfaces to aid emergency physicians in making timely and informed workup and disposition decisions. By enhancing situational awareness and communication at the point of care, the platform aims to improve emergency department (ED) efficiency, patient safety, and care coordination.