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Authors: Sheetal Agarwal, Anupam Joshi, Tim Finin, Tim Ganous, and Yelena Yesha
Date: July 19, 2006
Abstract: We describe a prototype system to capture and interpret data in a perioper- ative environment in order to construct an Electronic Medical Encounter Record (EMR). The EMR records and correlates significant medical data and video streams with an inferred higher-level event model of the surgery. Information from Radio Frequency Identification (RFID) tags provides basic context information including the presence of medical staff, devices, instruments and medication in the operating room (OR). Patient monitoring systems and sensors such as pulse oximeters and anesthesia machines provide continuous streams of physiological data. These low level data streams are processed by the TelegraphCQ adaptive dataflow system to generate higher-level primitive events, such as a nurse entering the OR. A hierar- chical knowledge-based event detection system correlates primitive events, patient data and workflow data to infer high-level events, such as the onset of anesthesia. The resulting EMR provides medical staff with a permanent record of the surgery that can be used for subsequent evaluation and training.
Type: TechReport
Organization: UMBC
Number: TR-CS-06-05
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