R A Jenders1, A Shah. 1. Department of Medical Informatics, Columbia University, New York, NY, USA.
Abstract
CONTEXT: Detection of outbreaks of infection in the hospital typically requires daily manual review of microbiology laboratory test results. This process is time-consuming, tedious, prone to error and may miss trends in infection. A standard formalism for procedural knowledge representation, the Arden Syntax, provides a vehicle for implementing algorithms for detecting such infections. OBJECTIVE: To design and implement a computer-based system for detection of concerning patterns of infection or antibiotic resistance. SETTING: Computer-based event monitor and central patient data repository at the Columbia-Presbyterian Medical Center (CPMC). RESULTS: We designed a two-phase system, including initial filtering of individual patient laboratory results by Arden Syntax Medical Logic Modules (MLMs) and subsequent aggregation and analysis across patients and locations using a statistical monitor. Preliminary data for the filtration phase demonstrate a 94.8% reduction in the volume of messages that must be considered in surveillance. CONCLUSIONS: Filtering raw laboratory results using a standard formalism eases the process of aggregating data across patients and sites as well as detecting trends in infection. There is a need for augmenting such formalisms in order to enable population-based decision support.
CONTEXT: Detection of outbreaks of infection in the hospital typically requires daily manual review of microbiology laboratory test results. This process is time-consuming, tedious, prone to error and may miss trends in infection. A standard formalism for procedural knowledge representation, the Arden Syntax, provides a vehicle for implementing algorithms for detecting such infections. OBJECTIVE: To design and implement a computer-based system for detection of concerning patterns of infection or antibiotic resistance. SETTING: Computer-based event monitor and central patient data repository at the Columbia-Presbyterian Medical Center (CPMC). RESULTS: We designed a two-phase system, including initial filtering of individual patient laboratory results by Arden Syntax Medical Logic Modules (MLMs) and subsequent aggregation and analysis across patients and locations using a statistical monitor. Preliminary data for the filtration phase demonstrate a 94.8% reduction in the volume of messages that must be considered in surveillance. CONCLUSIONS: Filtering raw laboratory results using a standard formalism eases the process of aggregating data across patients and sites as well as detecting trends in infection. There is a need for augmenting such formalisms in order to enable population-based decision support.
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