%0 Journal Article
%T Electronic Surveillance For Catheter-Associated Urinary Tract Infection Using Natural Language Processing
%A Ann Stapleton
%A Marion Granich
%A Patrick C Sanger
%A Paul S. Pottinger
%A Robin Olsen-Scribner
%A Rupali Jain
%A William B Lober
%J Archive of "AMIA Annual Symposium Proceedings".
%D 2017
%X Catheter-associated urinary tract infection (CAUTI) is a common and costly healthcare-associated infection, yet measuring it accurately is challenging and resource-intensive. Electronic surveillance promises to make this task more objective and efficient in an era of new financial and regulatory imperatives, but previous surveillance approaches have used a simplified version of the definition. We applied a complete definition, including subjective elements identified through natural language processing of clinical notes. Through examination of documentation practices, we defined a set of rules that identified positively and negatively asserted symptoms of CAUTI. Our algorithm was developed on a training set of 1421 catheterizedpatients and prospectively validated on 1567 catheterizedpatients. Compared to gold standard chart review, our tool had a sensitivity of 97.1%, specificity of 94.5% PPV of 66.7% and NPV of 99.6% for identifying CAUTI. We discuss sources of error and suggestions for more computable future definitions
%U https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5977673/