Identification of methicillin-resistant Staphylococcus aureus within the Nation`s Veterans Affairs Medical Centers using natural language processing (BMC Medical Informatics and Decision Making)

Background: Accurate information is needed to direct healthcare systems` efforts to control methicillinresistantStaphylococcus aureus (MRSA). Assembling complete and correct microbiologydata is vital to understanding and addressing the multiple drug-resistant organisms in ourhospitals. Methods: Herein, we describe a system that securely gathers microbiology data from the Department ofVeterans Affairs (VA) network of databases. Using natural language processing methods, weapplied an information extraction process to extract organisms and susceptibilities from thefree-text data. We then validated the extraction against independently derived electronic dataand expert annotation. Results: We estimate that the collected microbiology data are 98.5% complete and that methicillinresistantStaphylococcus aureus was extracted accurately 99.7% of the time. Conclusions: Applying natural language processing methods to microbiology records appears to be apromising way to extract accurate and useful nosocomial pathogen surveillance data. Bothscientific inquiry and the data`s reliability will be dependent on the surveillance system`scapability to compare from multiple sources and circumvent systematic error. The datasetconstructed and methods used for this investigation could contribute to a comprehensiveinfectious disease surveillance system or other pressing needs.