title: Data elements and validation methods used for electronic surveillance of health care-associated infections: a systematic review.
abstract: BACKGROUND
  We describe the primary data sources, data elements, and validation methods currently used in electronic surveillance systems (ESS) for identification and surveillance of health care-associated infections (HAIs), and compares these data elements and validation methods with recommended standards.
  
  
  METHODS
  Using Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines, a PubMed and manual search was conducted to identify research articles describing ESS for identification and surveillance of HAIs published January 1, 2009-August 31, 2014. Selected articles were evaluated to determine what data elements and validation methods were included.
  
  
  RESULTS
  Among the 509 articles identified in the original literature search, 30 met the inclusion criteria. Whereas the majority of studies (83%) used recommended data sources and validated the numerator (80%), only 10% of studies performed external and internal validation. In addition, there was variation in the ESS data formats used.
  
  
  CONCLUSIONS
  Our findings suggest that the majority of ESS for HAI surveillance use standard definitions, but the lack of widespread internal data, denominator, and external validation in these systems reduces the reliability of their findings. Additionally, advanced programming skills are required to create, implement, and maintain these systems and to reduce the variability in data formats.
authors.affiliation: School of Nursing, Columbia University, New York, NY. Electronic address: Kdc2110@columbia.edu.
authors.forename: Kenrick D
authors.initials: KD
authors.lastname: Cato
authors.affiliation: School of Nursing, Columbia University, New York, NY; Department of Epidemiology, Mailman School of Public Health, Columbia University, New York, NY.
authors.forename: Bevin
authors.initials: B
authors.lastname: Cohen
authors.affiliation: School of Nursing, Columbia University, New York, NY; Department of Epidemiology, Mailman School of Public Health, Columbia University, New York, NY.
authors.forename: Elaine
authors.initials: E
authors.lastname: Larson
mesh_terms: D003428:Cross Infection / Q000453:epidemiology*; D001330:Electronic Data Processing / Q000379:methods*; D006801:Humans; D017053:Infection Control / Q000379:methods*; D015203:Reproducibility of Results
keywords: Automated surveillance; Automation/methods; Electronic Health Records