Electronic Surveillance For Catheter-Associated Urinary Tract Infection Using Natural Language Processing. Sanger, P. C., Granich, M., Olsen-Scribner, R., Jain, R., Lober, W. B., Stapleton, A., & Pottinger, P. S. AMIA ... Annual Symposium proceedings. AMIA Symposium, 2017:1507–1516, 2017. abstract bibtex 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.
@article{sanger_electronic_2017,
title = {Electronic {Surveillance} {For} {Catheter}-{Associated} {Urinary} {Tract} {Infection} {Using} {Natural} {Language} {Processing}},
volume = {2017},
issn = {1942-597X},
abstract = {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.},
language = {eng},
journal = {AMIA ... Annual Symposium proceedings. AMIA Symposium},
author = {Sanger, Patrick C. and Granich, Marion and Olsen-Scribner, Robin and Jain, Rupali and Lober, William B. and Stapleton, Ann and Pottinger, Paul S.},
year = {2017},
pmid = {29854220},
pmcid = {PMC5977673},
keywords = {Algorithms, Catheter-Related Infections, Cross Infection, Data Mining, Documentation, Electronic Health Records, Humans, Monitoring, Physiologic, Natural Language Processing, Patient Acuity, Prospective Studies, Urinary Tract Infections},
pages = {1507--1516},
}
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