• Gregory Hackmann, Minmin Chen, Octav Chipara, Chenyang Lu, Yixin Chen, Marin Kollef, and Thomas C Bailey.
• Department of Computer Science and Engineering, Washington University, St. Louis, MO, USA.
• AMIA Annu Symp Proc. 2011 Jan 1; 2011: 511-9.

AbstractClinical study has found early detection and intervention to be essential for preventing clinical deterioration in patients at general hospital units. In this paper, we envision a two-tiered early warning system designed to identify the signs of clinical deterioration and provide early warning of serious clinical events. The first tier of the system automatically identifies patients at risk of clinical deterioration from existing electronic medical record databases. The second tier performs real-time clinical event detection based on real-time vital sign data collected from on-body wireless sensors attached to those high-risk patients. We employ machine-learning techniques to analyze data from both tiers, assigning scores to patients in real time. The assigned scores can then be used to trigger early-intervention alerts. Preliminary study of an early warning system component and a wireless clinical monitoring system component demonstrate the feasibility of this two-tiered approach.

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