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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

Information

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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References

1. Klevens, RM, Edwards, JR, Richards, CL Jr, et al. Estimating health care-associated infections and deaths in U.S. hospitals, 2002. Public Health Rep 2007;122:160–166.Google Scholar
2. Mayer, J, Greene, T, Howell, J, et al. Agreement in classifying bloodstream infections among multiple reviewers conducting surveillance. Clin Infect Dis 2012;55:364–370.Google Scholar
3. Stevens, JP, Kachniarz, B, Wright, SB, et al. When policy gets it right: variability in U.S. hospitals' diagnosis of ventilator-associated pneumonia. Crit Care Med 2014;42:497–503.CrossRefGoogle ScholarPubMed
4. Worth, LJ, Brett, J, Bull, AL, McBryde, ES, Russo, PL, Richards, MJ. Impact of revising the National Nosocomial Infection Surveillance System definition for catheter-related bloodstream infection in ICU: reproducibility of the National Healthcare Safety Network case definition in an Australian cohort of infection control professionals. Am J Infect Control 2009;37:643–648.Google Scholar
5. Klompas, M, Yokoe, DS. Automated surveillance of healthcare-associated infections. Clin Infect Dis 2009;48:1268–1275.Google Scholar
6. Meddings, J, Rogers, MA, Krein, SL, Fakih, MG, Olmsted, RN, Saint, S. Reducing unnecessary urinary catheter use and other strategies to prevent catheter-associated urinary tract infection: an integrative review. BMJ Qual Saf 2014;23:277–289.CrossRefGoogle ScholarPubMed
7. Shepard, J, Hadhazy, E, Frederick, J, et al. Using electronic medical records to increase the efficiency of catheter-associated urinary tract infection surveillance for National Health and Safety Network reporting. Am J Infect Control 2014;42:e33–e36.CrossRefGoogle ScholarPubMed
8. Lo, YS, Lee, WS, Liu, CT. Utilization of electronic medical records to build a detection model for surveillance of healthcare-associated urinary tract infections. J Med Sys 2013;37:9923.CrossRefGoogle ScholarPubMed
9. Landers, T, Apte, M, Hyman, S, Furuya, Y, Glied, S, Larson, E. A comparison of methods to detect urinary tract infections using electronic data. Jt Comm J Qual Patient Saf 2010;36:411–417.Google Scholar
10. Cass, AL, Kelly, JW, Probst, JC, Addy, CL, McKeown, RE. Identification of device-associated infections utilizing administrative data. Am J Infect Control 2013;41:1195–1199.Google Scholar
11. Friedman, C, Rindflesch, TC, Corn, M. Natural language processing: state of the art and prospects for significant progress, a workshop sponsored by the National Library of Medicine. J Biomed Inform 2013;46:765–773.CrossRefGoogle ScholarPubMed
12. Centers for Disease Control and Prevention. Urinary tract infection (catheter-associated urinary tract infection [CAUTI] and non-catheter-associated urinary tract infection [UTI] and other urinary system infection [USI]) events. Device-associated module CAUTI. http://www.cdc.gov/nhsn/PDFs/pscManual/7pscCAUTIcurrent.pdf. Published 2014. Accessed March 24, 2014.Google Scholar
13. Kudesia, V, Strymish, J, D'Avolio, L, Gupta, K. Natural language processing to identify Foley catheter–days. Infect Control Hosp Epidemiol 2012;33:1270–1272.Google Scholar
14. Choudhuri, JA, Pergamit, RF, Chan, JD, et al. An electronic catheter-associated urinary tract infection surveillance tool. Infect Control Hosp Epidemol 2011;32:757–762.CrossRefGoogle ScholarPubMed
15. Wright, MO, Fisher, A, John, M, Reynolds, K, Peterson, LR, Robicsek, A. The electronic medical record as a tool for infection surveillance: successful automation of device-days. Am J Infect Control 2009;37:364–370.CrossRefGoogle ScholarPubMed
16. Jones, M, DuVall, SL, Spuhl, J, Samore, MH, Nielson, C, Rubin, M. Identification of methicillin-resistant Staphylococcus aureus within the nation's Veterans Affairs medical centers using natural language processing. BMC Med Inform Decis Mak 2012;12:34.CrossRefGoogle ScholarPubMed
17. Forbush, TB, Gundlapalli, AV, Palmer, MN, et al. "Sitting on pins and needles": characterization of symptom descriptions in clinical notes. AMIA Jt Summits Transl Sci Proc 2013;2013:67–71.Google Scholar
18. Dublin, S, Baldwin, E, Walker, RL, et al. Natural language processing to identify pneumonia from radiology reports. Pharmacoepidemiol Drug Saf 2013;22:834–841.Google Scholar
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