title: Administrative data fail to accurately identify cases of healthcare-associated infection.
abstract: OBJECTIVE
  Some policy makers have embraced public reporting of healthcare-associated infections (HAIs) as a strategy for improving patient safety and reducing healthcare costs. We compared the accuracy of 2 methods of identifying cases of HAI: review of administrative data and targeted active surveillance.
  
  
  METHODS
  A cross-sectional prospective study was performed during a 9-month period in 2004 at the Children's Hospital of Philadelphia, a 418-bed academic pediatric hospital. "True HAI" cases were defined as those that met the definitions of the National Nosocomial Infections Surveillance System and that were detected by a trained infection control professional on review of the medical record. We examined the sensitivity and the positive and negative predictive values of identifying HAI cases by review of administrative data and by targeted active surveillance.
  
  
  RESULTS
  We found similar sensitivities for identification of HAI cases by review of administrative data (61%) and by targeted active surveillance (76%). However, the positive predictive value of identifying HAI cases by review of administrative data was poor (20%), whereas that of targeted active surveillance was 100%.
  
  
  CONCLUSIONS
  The positive predictive value of identifying HAI cases by targeted active surveillance is very high. Additional investigation is needed to define the optimal detection method for institutions that provide HAI data for comparative analysis.
authors.affiliation: Department of Infection Prevention and Control, Children's Hospital of Philadelphia, Philadelphia, PA 19104, USA.
authors.forename: Eileen R
authors.initials: ER
authors.lastname: Sherman
authors.forename: Kateri H
authors.initials: KH
authors.lastname: Heydon
authors.forename: Keith H
authors.initials: KH
authors.lastname: St John
authors.forename: Eva
authors.initials: E
authors.lastname: Teszner
authors.forename: Susan L
authors.initials: SL
authors.lastname: Rettig
authors.forename: Sharon K
authors.initials: SK
authors.lastname: Alexander
authors.forename: Theoklis Z
authors.initials: TZ
authors.lastname: Zaoutis
authors.forename: Susan E
authors.initials: SE
authors.lastname: Coffin
mesh_terms: D003428:Cross Infection / Q000145:classification* / Q000191:economics / Q000453:epidemiology*; D003430:Cross-Sectional Studies; D030881:Disclosure / Q000331:legislation & jurisprudence*; D006284:Health Plan Implementation; D006776:Hospitals, Pediatric / Q000331:legislation & jurisprudence* / Q000592:standards; D006801:Humans; D007344:Insurance Claim Reporting / Q000706:statistics & numerical data*; D019982:Organizational Case Studies; D010414:Pennsylvania; D015143:Philadelphia / Q000453:epidemiology; D011057:Politics; D012189:Retrospective Studies; D018571:Sentinel Surveillance