title: Validation of Test Performance and Clinical Time Zero for an Electronic Health Record Embedded Severe Sepsis Alert.
abstract: Increasing use of EHRs has generated interest in the potential of computerized clinical decision support to improve treatment of sepsis. Electronic sepsis alerts have had mixed results due to poor test characteristics, the inability to detect sepsis in a timely fashion and the use of outside software limiting widespread adoption. We describe the development, evaluation and validation of an accurate and timely severe sepsis alert with the potential to impact sepsis management.
  
  
  
  To develop, evaluate, and validate an accurate and timely severe sepsis alert embedded in a commercial EHR.
  
  
  
  The sepsis alert was developed by identifying the most common severe sepsis criteria among a cohort of patients with ICD 9 codes indicating a diagnosis of sepsis. This alert requires criteria in three categories: indicators of a systemic inflammatory response, evidence of suspected infection from physician orders, and markers of organ dysfunction. Chart review was used to evaluate test performance and the ability to detect clinical time zero, the point in time when a patient develops severe sepsis.
  
  
  
  Two physicians reviewed 100 positive cases and 75 negative cases. Based on this review, sensitivity was 74.5%, specificity was 86.0%, the positive predictive value was 50.3%, and the negative predictive value was 94.7%. The most common source of end-organ dysfunction was MAP less than 70 mm/Hg (59%). The alert was triggered at clinical time zero in 41% of cases and within three hours in 53.6% of cases. 96% of alerts triggered before a manual nurse screen.
  
  
  
  We are the first to report the time between a sepsis alert and physician chart-review clinical time zero. Incorporating physician orders in the alert criteria improves specificity while maintaining sensitivity, which is important to reduce alert fatigue. By leveraging standard EHR functionality, this alert could be implemented by other healthcare systems.
authors.affiliation: Santa Clara Valley Medical Center.
authors.forename: Joshua
authors.initials: J
authors.lastname: Rolnick
authors.affiliation: Stanford Hospital & Clinics.
authors.forename: N Lance
authors.initials: NL
authors.lastname: Downing
authors.affiliation: Stanford Hospital & Clinics.
authors.forename: John
authors.initials: J
authors.lastname: Shepard
authors.affiliation: Stanford School of Medicine.
authors.forename: Weihan
authors.initials: W
authors.lastname: Chu
authors.affiliation: Stanford Hospital & Clinics.
authors.forename: Julia
authors.initials: J
authors.lastname: Tam
authors.affiliation: Sagacious Consultants.
authors.forename: Alexander
authors.initials: A
authors.lastname: Wessels
authors.affiliation: Stanford Hospital & Clinics.
authors.forename: Ron
authors.initials: R
authors.lastname: Li
authors.affiliation: Stanford Hospital & Clinics.
authors.forename: Brian
authors.initials: B
authors.lastname: Dietrich
authors.affiliation: Stanford Hospital & Clinics.
authors.forename: Michael
authors.initials: M
authors.lastname: Rudy
authors.affiliation: Stanford Hospital & Clinics.
authors.forename: Leon
authors.initials: L
authors.lastname: Castaneda
authors.affiliation: Stanford School of Medicine.
authors.forename: Lisa
authors.initials: L
authors.lastname: Shieh
mesh_terms: D020000:Decision Support Systems, Clinical; D057286:Electronic Health Records; D006801:Humans; D010820:Physicians; D018805:Sepsis / Q000175:diagnosis* / Q000628:therapy; D013997:Time Factors
keywords: Testing and evaluation; clinical decision support; inpatient care; medicine; performance improvement