title: Preliminary assessment of an automated surveillance system for infection control.
abstract: OBJECTIVE
  Rapid identification and investigation of potential outbreaks is key to limiting transmission in the healthcare setting. Manual review of laboratory results remains a cumbersome, time-consuming task for infection control practitioners (ICPs). Computer-automated techniques have shown promise for improving the efficiency and accuracy of surveillance. We examined the use of automated control charts, provided by an automated surveillance system, for detection of potential outbreaks.
  
  
  METHODS
  A 656-bed academic medical center.
  
  
  METHODS
  We retrospectively reviewed 13 months (November 2001 through November 2002) of laboratory-patient data, comparing an automated surveillance application with standard infection control practices. We evaluated positive predictive value, sensitivity, and time required to investigate the alerts. An ICP created 75 control charts. A standardized case investigation form was developed to evaluate each alert for the likelihood of nosocomial transmission based on temporal and spatial overlap and culture results.
  
  
  RESULTS
  The 75 control charts were created in 75 minutes and 18 alerts fired above the 3-sigma level. These were independently reviewed by an ICP and associate hospital epidemiologist. The review process required an average of 20 minutes per alert and the kappa score between the reviewers was 0.82. Eleven of the 18 alerts were determined to be potential outbreaks, yielding a positive predictive value of 0.61. Routine surveillance identified 5 of these 11 alerts during this time period.
  
  
  CONCLUSIONS
  Automated surveillance with user-definable control charts for cluster identification was more sensitive than routine methods and is capable of operating with high specificity and positive predictive value in a time-efficient manner.
authors.affiliation: Department of Infection Control and Hospital Epidemiology, University of Maryland Medical Center, 29 South Greene Street, Suite 400, Baltimore, MD 21201, USA.
authors.forename: Marc-Oliver
authors.initials: MO
authors.lastname: Wright
authors.forename: Eli N
authors.initials: EN
authors.lastname: Perencevich
authors.forename: Christopher
authors.initials: C
authors.lastname: Novak
authors.forename: Joan N
authors.initials: JN
authors.lastname: Hebden
authors.forename: Harold C
authors.initials: HC
authors.lastname: Standiford
authors.forename: Anthony D
authors.initials: AD
authors.lastname: Harris
mesh_terms: D003428:Cross Infection / Q000453:epidemiology / Q000517:prevention & control*; D006801:Humans; D017053:Infection Control / Q000379:methods*; D008396:Maryland / Q000453:epidemiology; D016347:Medical Records Systems, Computerized; D011237:Predictive Value of Tests; D012189:Retrospective Studies; D018571:Sentinel Surveillance