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Implementing Automated Surveillance for Tracking Clostridium difficile Infection at Multiple Healthcare Facilities

Published online by Cambridge University Press:  02 January 2015

Erik R. Dubberke ,
Humaa A. Nyazee ,
Deborah S. Yokoe ,
Jeanmarie Mayer ,
Kurt B. Stevenson ,
Julie E. Mangino ,
Yosef M. Khan ,
Victoria J. Fraser  and
Centers for Disease Control and Prevention Epicenters Program
Erik R. Dubberke*
Affiliation:
Washington University School of Medicine, St. Louis, Missouri
Humaa A. Nyazee
Affiliation:
Washington University School of Medicine, St. Louis, Missouri
Deborah S. Yokoe
Affiliation:
Brigham and Women's Hospital and Harvard Medical School, Boston, Massachusetts
Jeanmarie Mayer
Affiliation:
University of Utah Hospital, Salt Lake City, Utah
Kurt B. Stevenson
Affiliation:
Ohio State University Medical Center, Columbus, Ohio
Julie E. Mangino
Affiliation:
Ohio State University Medical Center, Columbus, Ohio
Yosef M. Khan
Affiliation:
Ohio State University Medical Center, Columbus, Ohio
Victoria J. Fraser
Affiliation:
Washington University School of Medicine, St. Louis, Missouri
*
Box 8051, 660 South Euclid Avenue, St. Louis, MO 63110 ([email protected])
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Abstract

Automated surveillance using electronically available data has been found to be accurate and save time. An automated Clostridium difficile infection (CDI) surveillance algorithm was validated at 4 Centers for Disease Control and Prevention Epicenter hospitals. Electronic surveillance was highly sensitive, specific, and showed good to excellent agreement for hospital-onset; community-onset, study facility-associated; indeterminate; and recurrent CDI.

Infect Control Hosp Epidemiol 2012;33(3):305-308

Type
Concise Communication
Information
Infection Control & Hospital Epidemiology , Volume 33 , Issue 3 , March 2012 , pp. 305 - 308
Copyright
Copyright © The Society for Healthcare Epidemiology of America 2012

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References

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