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Open Access Research article

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

Makoto Jones12*, Scott L DuVall12*, Joshua Spuhl1, Matthew H Samore12, Christopher Nielson34 and Michael Rubin12

Author Affiliations

1 VA Salt Lake City Health Care System, Salt Lake City, UT, USA

2 Department of Internal Medicine, University of Utah, Salt Lake City, UT, USA

3 VA Reno Medical Center, Reno, NV, USA

4 University of Nevada, Reno, NV, USA

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BMC Medical Informatics and Decision Making 2012, 12:34  doi:10.1186/1472-6947-12-34

Published: 25 April 2012

Abstract

Background

Accurate information is needed to direct healthcare systems’ efforts to control methicillin-resistant Staphylococcus aureus (MRSA). Assembling complete and correct microbiology data is vital to understanding and addressing the multiple drug-resistant organisms in our hospitals.

Methods

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.

Results

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.

Conclusions

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.