title: Diagnostic performance of electronic syndromic surveillance systems in acute care: a systematic review.
abstract: BACKGROUND
  Healthcare Electronic Syndromic Surveillance (ESS) is the systematic collection, analysis and interpretation of ongoing clinical data with subsequent dissemination of results, which aid clinical decision-making.
  
  
  OBJECTIVE
  To evaluate, classify and analyze the diagnostic performance, strengths and limitations of existing acute care ESS systems.
  
  
  METHODS
  All available to us studies in Ovid MEDLINE, Ovid EMBASE, CINAHL and Scopus databases, from as early as January 1972 through the first week of September 2012.
  
  
  METHODS
  Prospective and retrospective trials, examining the diagnostic performance of inpatient ESS and providing objective diagnostic data including sensitivity, specificity, positive and negative predictive values.
  
  
  METHODS
  Two independent reviewers extracted diagnostic performance data on ESS systems, including clinical area, number of decision points, sensitivity and specificity. Positive and negative likelihood ratios were calculated for each healthcare ESS system. A likelihood matrix summarizing the various ESS systems performance was created.
  
  
  RESULTS
  The described search strategy yielded 1639 articles. Of these, 1497 were excluded on abstract information. After full text review, abstraction and arbitration with a third reviewer, 33 studies met inclusion criteria, reporting 102,611 ESS decision points. The yielded I2 was high (98.8%), precluding meta-analysis. Performance was variable, with sensitivities ranging from 21% -100% and specificities ranging from 5%-100%.
  
  
  CONCLUSIONS
  There is significant heterogeneity in the diagnostic performance of the available ESS implements in acute care, stemming from the wide spectrum of different clinical entities and ESS systems. Based on the results, we introduce a conceptual framework using a likelihood ratio matrix for evaluation and meaningful application of future, frontline clinical decision support systems.
authors.affiliation: Division of Pulmonary and Critical Care Medicine, Department of Internal Medicine, Mayo Clinic, Rochester, Minnesota, USA.
authors.forename: M
authors.initials: M
authors.lastname: Kashiouris
authors.forename: J C
authors.initials: JC
authors.lastname: O'Horo
authors.forename: B W
authors.initials: BW
authors.lastname: Pickering
authors.forename: V
authors.initials: V
authors.lastname: Herasevich
mesh_terms: D003933:Diagnosis; D006801:Humans; D008490:Medical Informatics / Q000379:methods*; D005791:Patient Care / Q000379:methods*
keywords: Alert; EMR; IC; computer systems; computer-assisted; computerized medical records systems; decision support systems; diagnostic; false; medical informatics applications; monitor; sniffers; surveillance