Identification of methicillin-resistant Staphylococcus aureus within the Nation’s Veterans Affairs Medical Centers using natural language processing

Herein, we describe a system that securely gathers microbiology data from the Department of Veterans Affairs (VA) network of databases. Using natural language processing methods, we applied an information extraction process to extract organisms and susceptibilities from the free-text data. We then validated the extraction against independently derived electronic data and expert annotation.We estimate that the collected microbiology data are 98.5% complete and that methicillin-resistant Staphylococcus aureus was extracted accurately 99.7% of the time.Applying natural language processing methods to microbiology records appears to be a promising way to extract accurate and useful nosocomial pathogen surveillance data. Both scientific inquiry and the data’s reliability will be dependent on the surveillance system’s capability to compare from multiple sources and circumvent systematic error. The dataset constructed and methods used for this investigation could contribute to a comprehensive infectious disease surveillance system or other pressing needs.There is a pressing need for timely, reliable, and generalizable information to guide infection control efforts directed against methicillin-resistant Staphylococcus aureus (MRSA) within hospitals. This microorganism frequently causes abscesses, bloodstream infections, post-surgical infections, and sometimes deaths; estimates from existing research and census data suggest that 17,000 attributable deaths occurred in 2008 [1,2]. With the objective of reducing MRSA transmission in hospitals, the Department of Veterans Affairs (VA) implemented the National MRSA Prevention Initiative in October 2007 [3]. The program included VA-wide MRSA testing upon admission to, discharge from, and transfers between acute care wards; rules for contact precautions; hand hygiene; a change in culture to one of shared responsibility; and new reporting systems [4]. The VA Inpatient Evaluation Center (IPEC) gathered data to evaluate this program by