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

Jones, Makoto; DuVall, Scott L; Spuhl, Joshua; Samore, Matthew H; Nielson, Christopher; Rubin, Michael

doi:10.1186/1472-6947-12-34

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

Published Date:

Jul 11 2012

Publisher's site:

http://dx.doi.org/10.1186/1472-6947-12-34 New Site Icon

Source:

BMC Med Inform Decis Mak. 2012; 12:34.

[PDF-560.88 KB]

Details:

Personal Authors:

Jones, Makoto ; DuVall, Scott L ; Spuhl, Joshua ; Samore, Matthew H ; Nielson, Christopher ; ... More ▼

Keywords:

[+]

Pubmed ID:

22533507

Pubmed Central ID:

PMC3394221

Description:

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.

Document Type:

Journal Article

Collection(s):

CDC Public Access

Funding:

07FED706504/PHS HHS/United States
5U01CI000334/CI/NCPDCID CDC HHS/United States
UL1 RR024153/RR/NCRR NIH HHS/United States
UL1 TR000005/TR/NCATS NIH HHS/United States

Supporting Files:

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