Automatic Clinical Procedure Detection for Emergency Services

Jamison Heard, Richard A. Paris, Deirdre Scully, Candace D. McNaughton, Jesse M. Ehrenfeld, Joseph R. Coco, Daniel Fabbri, Bobby Bodenheimer, Julie A. Adams

Abstract: Understanding a patient's state is critical to providing optimal care. However, information loss occurs during patient hand-offs (e.g., emergency services (EMS) transferring patient care to a receiving hospital), which hinders care quality. Augmenting the information flow from an EMS vehicle to a receiving hospital may reduce information loss and improve patient outcomes. Such augmentation requires a noninvasive system that can automatically recognize clinical procedures being performed and send near real-time information to a receiving hospital. An automatic clinical procedure detection system that uses wearable sensors, video, and machine-learning to recognize clinical procedures within a controlled environment is presented. The system demonstrated how contextual information and a majority vote method can substantially improve procedure recognition accuracy. Future work concerning computer vision techniques and deep learning are discussed.