Electronic Surveillance For Catheter-Associated Urinary Tract Infection Using Natural Language Processing.

Electronic Surveillance For Catheter-Associated Urinary Tract Infection Using Natural Language Processing. AMIA Annu Symp Proc. 2017;2017:1507-1516 Authors: Sanger PC, Granich M, Olsen-Scribner R, Jain R, Lober WB, Stapleton A, Pottinger PS 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. PMID: 29854220 [PubMed - indexed for MEDLINE]
Source: AMIA Annual Symposium Proceedings - Category: Bioinformatics Tags: AMIA Annu Symp Proc Source Type: research