Hostname: page-component-586b7cd67f-l7hp2 Total loading time: 0 Render date: 2024-11-24T19:50:31.146Z Has data issue: false hasContentIssue false

Natural Language Processing to Identify Foley Catheter–Days

Published online by Cambridge University Press:  02 January 2015

Valmeek Kudesia
Affiliation:
Department of Medicine, Boston University School of Medicine, Boston, Massachusetts Massachusetts Veterans Epidemiology Research Informatics Center, Boston, Massachusetts Harvard Medical School, Boston, Massachusetts
Judith Strymish
Affiliation:
Harvard Medical School, Boston, Massachusetts Department of Medicine, Veterans Affairs Boston Healthcare System, Boston, Massachusetts
Leonard D'Avolio
Affiliation:
Massachusetts Veterans Epidemiology Research Informatics Center, Boston, Massachusetts Harvard Medical School, Boston, Massachusetts
Kalpana Gupta*
Affiliation:
Department of Medicine, Boston University School of Medicine, Boston, Massachusetts Massachusetts Veterans Epidemiology Research Informatics Center, Boston, Massachusetts Department of Medicine, Veterans Affairs Boston Healthcare System, Boston, Massachusetts
*
VA Boston HCS, 1400 VFW Parkway, 111 Med, West Roxbury, MA 02132 ([email protected])

Abstract

Image of the first page of this content. For PDF version, please use the ‘Save PDF’ preceeding this image.'
Type
Research Briefs
Copyright
Copyright © The Society for Healthcare Epidemiology of America 2012

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

1.Klevens, RM, Edwards, JR, Richards, CL Jret al.Estimating health care-associated infections and deaths in U.S. hospitals, 2002. Public Health Rep 2007;122(2):160166.10.1177/003335490712200205Google Scholar
2.Burns, AC, Petersen, NJ, Garza, A, et al.Accuracy of a urinary catheter surveillance protocol. Am J Infect Control 2012;40(1):5558.10.1016/j.ajic.2011.04.006Google Scholar
3.Saint, S, Kowalski, CP, Kaufman, SR, et al.Preventing hospital-acquired urinary tract infection in the United States: a national study. Clin Infect Dis 2008;46(2):243250.Google Scholar
4.Jha, AK, Classen, DC. Getting moving on patient safety: harnessing electronic data for safer care. N Engl J Med 2011;365(19):17561758.10.1056/NEJMp1109398Google Scholar
5.Choudhuri, JA, Pergamit, RF, Chan, JD, et al.An electronic catheter-associated urinary tract infection surveillance tool. Infect Control Hosp Epidemiol 2011;32(8):757762.10.1086/661103Google Scholar
6.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(5):364370.10.1016/j.ajic.2008.11.003Google Scholar
7.Murff, HJ, FitzHenry, F, Matheny, ME, et al.Automated identification of postoperative complications within an electronic medical record using natural language processing. JAMA 2011;306(8):848855.Google Scholar
8.D'Avolio, LW, Litwin, MS, Rogers, SO JrBui, AA. Facilitating clinical outcomes assessment through the automated identification of quality measures for prostate cancer surgery. J Am Med Inform Assoc 2008;15(3):341348.Google Scholar
9.D'Avolio, LW, Nguyen, TM, Goryachev, S, Fiore, LD. Automated concept-level information extraction to reduce the need for custom software and rules development. J Am Med Inform Assoc 2011;18(5):607613.10.1136/amiajnl-2011-000183Google Scholar
10.Fakih, MG, Watson, SR, Greene, MT, et al.Reducing inappropriate urinary catheter use: a statewide effort. Arch Intern Med 2012;172(3):255260.Google Scholar