title: A two-stage clinical decision support system for early recognition and stratification of patients with sepsis: an observational cohort study.
abstract: OBJECTIVE
  To examine the diagnostic accuracy of a two-stage clinical decision support system for early recognition and stratification of patients with sepsis.
  
  
  METHODS
  Observational cohort study employing a two-stage sepsis clinical decision support to recognise and stratify patients with sepsis. The stage one component was comprised of a cloud-based clinical decision support with 24/7 surveillance to detect patients at risk of sepsis. The cloud-based clinical decision support delivered notifications to the patients' designated nurse, who then electronically contacted a provider. The second stage component comprised a sepsis screening and stratification form integrated into the patient electronic health record, essentially an evidence-based decision aid, used by providers to assess patients at bedside.
  
  
  METHODS
  Urban, 284 acute bed community hospital in the USA; 16,000 hospitalisations annually.
  
  
  METHODS
  Data on 2620 adult patients were collected retrospectively in 2014 after the clinical decision support was implemented.
  
  
  METHODS
  'Suspected infection' was the established gold standard to assess clinical decision support clinimetric performance.
  
  
  RESULTS
  A sepsis alert activated on 417 (16%) of 2620 adult patients hospitalised. Applying 'suspected infection' as standard, the patient population characteristics showed 72% sensitivity and 73% positive predictive value. A postalert screening conducted by providers at bedside of 417 patients achieved 81% sensitivity and 94% positive predictive value. Providers documented against 89% patients with an alert activated by clinical decision support and completed 75% of bedside screening and stratification of patients with sepsis within one hour from notification.
  
  
  CONCLUSIONS
  A clinical decision support binary alarm system with cross-checking functionality improves early recognition and facilitates stratification of patients with sepsis.
authors.affiliation: Population Health, Cerner Corporation, Kansas City, 64117 USA.
authors.forename: Robert C
authors.initials: RC
authors.lastname: Amland
authors.affiliation: Pulmonary Division, Department of Medicine, Unity Hospital, Rochester, 14626 USA.
authors.forename: Jason J
authors.initials: JJ
authors.lastname: Lyons
authors.affiliation: Business Intelligence and Long Term Care, Rochester Regional Health System; Rochester, 14626 USA.
authors.forename: Tracy L
authors.initials: TL
authors.lastname: Greene
authors.affiliation: Department of Medicine, Unity Hospital, Rochester, 14626 USA.
authors.forename: James M
authors.initials: JM
authors.lastname: Haley
keywords: Early recognition; cloud-based computerised clinical decision support system; detection and stratification of patients with sepsis; electronic health record; patient safety; quality and prevention