SIGNAL-BASED ANALYSIS OF CLINICAL MICROBIOLOGY

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Development of a digital signal processing system is proposed to perform real-time analysis clinical microbiology datasets. This research project, code-named BugCruncher, forms the core of a set of intelligent tools and data interfaces for infection control and antibiotic resistance surveillance. Preliminary results of signal processing indicate encouraging results: combined filters of rolling averages produced analysis that clearly showed the downward trend of a resistant bacterial population and illustrated the possibility of real-time analysis. The building of a standardized interface to the Children's Hospital microbiology Laboratory Information System, allowing merger with WHONET is proposed. Research will be conducted to evaluate various signal filter combinations and algorithm development. The potential success of this research would serve as a foundation for a hospital- based surveillance system that would offer medical personnel ability to identify and resolve disease outbreaks at an early stage and to monitor and limit antimicrobial resistance. PROPOSED COMMERCIAL APPLICATION: Nosocomial infections and antimicrobial resistance are enormous problems for hospitals. Every hospital has an infection control group, and every group must review microbiology data A suite of tools built upon the research proposed herein will enable healthcare facilities to detect trends and actively combat infection.