title: Electronic Surveillance of Surgical Site Infections.
abstract: BACKGROUND
  Electronic health and administrative data are increasingly being used for identifying surgical site infections (SSI). We found an unexpectedly high number of patients who could not be classified definitively as having an infection or not. To further explore this, we present an electronic classification algorithm for conservative case finding and identify alterations that would adapt the method for other purposes.
  
  
  METHODS
  Two computer algorithms were created to identify SSI. One model used a strict National Healthcare Safety Network (NHSN) based SSI algorithm, which was applied to all discharges from 443,284 all discharges from four hospitals in Manhattan, NY, 2009 through 2012. The second model used discharges that only had NHSN-defined SSI procedures during the same period.
  
  
  RESULTS
  The strict SSI algorithm was able to classify SSI status for 27.3% of discharges; there was a high number of indeterminate cases. In contrast, the modified, less strict model, classified 97.2% of discharges with NHSN-approved SSI procedures.
  
  
  CONCLUSIONS
  Electronic records provide several options for aiding with the identification of infections in healthcare settings and can be tailored to suit specific uses. While algorithms for SSI classification should reflect the NHSN definition, our research emphasizes how variations of model building can affect the number of indeterminate cases that may necessitate manual review.
authors.affiliation: 1 School of Nursing, Columbia University , New York, New York.|3 New York Presbyterian Hospital , New York, New York.
authors.forename: Kenrick D
authors.initials: KD
authors.lastname: Cato
authors.affiliation: 1 School of Nursing, Columbia University , New York, New York.
authors.forename: Jianfang
authors.initials: J
authors.lastname: Liu
authors.affiliation: 1 School of Nursing, Columbia University , New York, New York.|2 Department of Epidemiology, Mailman School of Public Health, Columbia University , New York, New York.
authors.forename: Bevin
authors.initials: B
authors.lastname: Cohen
authors.affiliation: 1 School of Nursing, Columbia University , New York, New York.|2 Department of Epidemiology, Mailman School of Public Health, Columbia University , New York, New York.
authors.forename: Elaine
authors.initials: E
authors.lastname: Larson
mesh_terms: D000465:Algorithms; D057286:Electronic Health Records; D006801:Humans; D062486:Public Health Surveillance / Q000379:methods*; D013530:Surgical Wound Infection / Q000453:epidemiology*
keywords: hospital-acquired infection; surgical site infection