Chris is an emergency physician interested in integrating informatics tools in emergency and acute care research and clinical decision making. His interest in informatics started initially as a student at UC San Diego, and during his public health training at the UNC Gilling\u2019s School of Global Public Health. His work thus far has focused on using large administrative and clinical datasets to better understand the recognition of elder abuse in the emergency department, and predictors of repeat EMS transports to emergency departments across the state of North Carolina. As an emergency medicine resident at Vanderbilt University, Chris developed an interest in optimizing clinical decision support and documentation tools for commonly encountered, but high acuity and time sensitive encounters in trauma and medical resuscitations.<\/p>\n
During his first year of fellowship, Chris worked with ISD and emergency department leadership across the health system on multiple Epic@UNC optimization requests in the ASAP environment, including integration of vital signs into provider ED track board views, and efforts with standardization of documentation tools. His informatics research has centered on understanding how Natural Language Processing (NLP) can be utilized for recognition of incidental radiology findings in the emergency department setting, as well as ongoing collaboration with researchers in the Department of Statistics and Operations studying novel modeling techniques to predict ED occupancy and crowding. Chris was recently awarded a NC TraCS pilot grant to support his research in the development of a Natural Language Processing (NLP) and Machine Learning (ML) approach to identification of incidental radiology findings among discharged ED patients. During his second year of fellowship, Chris will focus on further study of applied NLP and ML in emergency care as well as developing further skills in data science using R and SQL.<\/p>\n