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Natural Language Processing for Real-Time Catheter-Associated Urinary Tract Infection Surveillance: Results of a Pilot Implementation Trial

Published online by Cambridge University Press:  26 May 2015

Westyn Branch-Elliman ,
Judith Strymish ,
Valmeek Kudesia ,
Amy K. Rosen  and
Kalpana Gupta
Westyn Branch-Elliman*
Affiliation:
Department of Medicine, Eastern Colorado Veterans Affairs (VA) Healthcare System, Denver, Colorado Department of Medicine, University of Colorado School of Medicine, Denver, Colorado
Judith Strymish
Affiliation:
Department of Medicine, VA Boston Healthcare System, Boston, Massachusetts Harvard University Medical School, Boston, Massachusetts
Valmeek Kudesia
Affiliation:
Department of Medicine, VA Boston Healthcare System, Boston, Massachusetts Massachusetts Veterans Epidemiology and Information Center, Boston VA Medical Center, Boston, Massachusetts
Amy K. Rosen
Affiliation:
Boston University School of Medicine, Boston, Massachusetts Center for Healthcare Organization and Implementation Research, VA Boston Healthcare System, Boston, Massachusetts Department of Surgery, Boston University School of Medicine, Boston, Massachusetts
Kalpana Gupta
Affiliation:
Department of Medicine, VA Boston Healthcare System, Boston, Massachusetts Boston University School of Medicine, Boston, Massachusetts Center for Healthcare Organization and Implementation Research, VA Boston Healthcare System, Boston, Massachusetts
*
Address correspondence to Westyn Branch-Elliman, MD, MMSc, Medical Director of Infection Prevention, ECHCS, 1055 Clermont Avenue, Mailstop 111L, Denver, CO 80220 (westyn.branch-elliman@ucdenver.edu).
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Abstract

BACKGROUND

Incidence of catheter-associated urinary tract infection (CAUTI) is a quality benchmark. To streamline conventional detection methods, an electronic surveillance system augmented with natural language processing (NLP), which gathers data recorded in clinical notes without manual review, was implemented for real-time surveillance.

OBJECTIVE

To assess the utility of this algorithm for identifying indwelling urinary catheter days and CAUTI.

SETTING

Large, urban tertiary care Veterans Affairs hospital.

METHODS

All patients admitted to the acute care units and the intensive care unit from March 1, 2013, through November 30, 2013, were included. Standard surveillance, which includes electronic and manual data extraction, was compared with the NLP-augmented algorithm.

RESULTS

The NLP-augmented algorithm identified 27% more indwelling urinary catheter days in the acute care units and 28% fewer indwelling urinary catheter days in the intensive care unit. The algorithm flagged 24 CAUTI versus 20 CAUTI by standard surveillance methods; the CAUTI identified were overlapping but not the same. The overall positive predictive value was 54.2%, and overall sensitivity was 65% (90.9% in the acute care units but 33% in the intensive care unit). Dissimilarities in the operating characteristics of the algorithm between types of unit were due to differences in documentation practice. Development and implementation of the algorithm required substantial upfront effort of clinicians and programmers to determine current language patterns.

CONCLUSIONS

The NLP algorithm was most useful for identifying simple clinical variables. Algorithm operating characteristics were specific to local documentation practices. The algorithm did not perform as well as standard surveillance methods.

Infect. Control Hosp. Epidemiol. 2015;36(9):1004–1010

Type
Original Articles
Information
Infection Control & Hospital Epidemiology , Volume 36 , Supplement 9 , September 2015 , pp. 1004 - 1010
Copyright
© 2015 by The Society for Healthcare Epidemiology of America. All rights reserved 

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