Our interdisciplinary team of computer science and health care researchers will develop augmented communication tools (ACTs) within a prototype Electronic Health Record (EHR) visual interface and test its usability. The focus of this project is Nurse-Nurse (RN-RN) communication of six high risk clinical events (CEs): fever, pain, changes in respiratory status, changes in level of consciousness, changes in output, and bleeding. Using combined EHR and hand-off report data, we will develop a predictive computational model for detecting these clinical events within the EHR, as well as their predicted severity. The ACTs system will then use light-weight automated inference to detect these clinical events, alert the nurse, and quickly provide highly relevant contextual data to improve quality care, patient safety, and reduce patient deaths.