title: Detecting the presence of an indwelling urinary catheter and urinary symptoms in hospitalized patients using natural language processing.
abstract: To develop a natural language processing pipeline to extract positively asserted concepts related to the presence of an indwelling urinary catheter in hospitalized patients from the free text of the electronic medical note. The goal is to assist infection preventionists and other healthcare professionals in determining whether a patient has an indwelling urinary catheter when a catheter-associated urinary tract infection is suspected. Currently, data on indwelling urinary catheters is not consistently captured in the electronic medical record in structured format and thus cannot be reliably extracted for clinical and research purposes.
  
  
  
  We developed a lexicon of terms related to indwelling urinary catheters and urinary symptoms based on domain knowledge, prior experience in the field, and review of medical notes. A reference standard of 1595 randomly selected documents from inpatient admissions was annotated by human reviewers to identify all positively and negatively asserted concepts related to indwelling urinary catheters. We trained a natural language processing pipeline based on the V3NLP framework using 1050 documents and tested on 545 documents to determine agreement with the human reference standard. Metrics reported are positive predictive value and recall.
  
  
  
  The lexicon contained 590 terms related to the presence of an indwelling urinary catheter in various categories including insertion, care, change, and removal of urinary catheters and 67 terms for urinary symptoms. Nursing notes were the most frequent inpatient note titles in the reference standard document corpus; these also yielded the highest number of positively asserted concepts with respect to urinary catheters. Comparing the performance of the natural language processing pipeline against the human reference standard, the overall recall was 75% and positive predictive value was 99% on the training set; on the testing set, the recall was 72% and positive predictive value was 98%. The performance on extracting urinary symptoms (including fever) was high with recall and precision greater than 90%.
  
  
  
  We have shown that it is possible to identify the presence of an indwelling urinary catheter and urinary symptoms from the free text of electronic medical notes from inpatients using natural language processing. These are two key steps in developing automated protocols to assist humans in large-scale review of patient charts for catheter-associated urinary tract infection. The challenges associated with extracting indwelling urinary catheter-related concepts also inform the design of electronic medical record templates to reliably and consistently capture data on indwelling urinary catheters.
authors.affiliation: IDEAS 2.0 Center, VA Salt Lake City Health Care System, Salt Lake City, UT, United States; University of Utah School of Medicine, Salt Lake City, UT, United States. Electronic address: adi.gundlapalli@hsc.utah.edu.
authors.forename: Adi V
authors.initials: AV
authors.lastname: Gundlapalli
authors.affiliation: IDEAS 2.0 Center, VA Salt Lake City Health Care System, Salt Lake City, UT, United States; University of Utah School of Medicine, Salt Lake City, UT, United States.
authors.forename: Guy
authors.initials: G
authors.lastname: Divita
authors.affiliation: IDEAS 2.0 Center, VA Salt Lake City Health Care System, Salt Lake City, UT, United States; University of Utah School of Medicine, Salt Lake City, UT, United States.
authors.forename: Andrew
authors.initials: A
authors.lastname: Redd
authors.affiliation: IDEAS 2.0 Center, VA Salt Lake City Health Care System, Salt Lake City, UT, United States; University of Utah School of Medicine, Salt Lake City, UT, United States.
authors.forename: Marjorie E
authors.initials: ME
authors.lastname: Carter
authors.affiliation: University of Utah School of Medicine, Salt Lake City, UT, United States.
authors.forename: Danette
authors.initials: D
authors.lastname: Ko
authors.affiliation: IDEAS 2.0 Center, VA Salt Lake City Health Care System, Salt Lake City, UT, United States; University of Utah School of Medicine, Salt Lake City, UT, United States.
authors.forename: Michael
authors.initials: M
authors.lastname: Rubin
authors.affiliation: IDEAS 2.0 Center, VA Salt Lake City Health Care System, Salt Lake City, UT, United States; University of Utah School of Medicine, Salt Lake City, UT, United States.
authors.forename: Matthew
authors.initials: M
authors.lastname: Samore
authors.affiliation: VA Boston Health Care System, Boston, MA, United States; Harvard Medical School, Boston, MA, United States.
authors.forename: Judith
authors.initials: J
authors.lastname: Strymish
authors.affiliation: University of Michigan Medical School, Ann Arbor, MI, United States; VA Ann Arbor Healthcare System, Ann Arbor, MI, United States.
authors.forename: Sarah
authors.initials: S
authors.lastname: Krein
authors.affiliation: VA Boston Health Care System, Boston, MA, United States; Boston University School of Medicine, Boston, MA, United States.
authors.forename: Kalpana
authors.initials: K
authors.lastname: Gupta
authors.affiliation: VA Ann Arbor Healthcare System, Ann Arbor, MI, United States; University of Michigan, School of Nursing, Ann Arbor, MI, United States.
authors.forename: Anne
authors.initials: A
authors.lastname: Sales
authors.affiliation: Houston Center for Innovations in Quality, Effectiveness, and Safety (iQuESt), Michael E. Debakey Veterans Affairs Medical Center, Houston, TX, United States; Baylor College of Medicine, Houston, TX, United States.
authors.forename: Barbara W
authors.initials: BW
authors.lastname: Trautner
mesh_terms: D057225:Data Mining; D057286:Electronic Health Records; D006801:Humans; D009323:Natural Language Processing; D062885:Urinary Catheters; D014552:Urinary Tract Infections
keywords: Cognitive support; Natural language processing; Urinary catheter