title: A Bayesian model for triage decision support.
abstract: OBJECTIVE
  To compare triage decisions of an automated emergency department triage system with decisions made by an emergency specialist.
  
  
  METHODS
  In a retrospective setting, data extracted from charts of 90 patients with chief complaint of non-traumatic abdominal pain were used as input for triage system and emergency medicine specialist. The final disposition and diagnoses of the physicians who visited the patient in Emergency Department (ED) as reflected in the medical records were considered as control. Results were compared by chi(2)-test and a binary logistic regression model.
  
  
  RESULTS
  Compared to emergency medicine specialist, triage system had higher sensitivity (90% versus 64%) and lower specificity (25% versus 48%) for patients who required hospitalization. The triage system successfully predicted the Admit decisions made in the ED whereas the emergency medicine specialist decisions could not predict the ED disposition. Both triage system and emergency medicine specialist properly disposed 56% of cases, however, the emergency medicine specialist in this study under-disposed more patients than the triage system considering Admit disposition (p=0.004) while he appropriately discharged more patients compared to the triage system (p=0.017).
  
  
  CONCLUSIONS
  The triage system studied here shows promise as a triage decision support tool to be used for telephone triage and triage in the emergency departments. This technology may also be useful to the patients as a self-triage tool. However, the efficiency of this particular application of this technology is unclear.
authors.affiliation: University of Texas Health Science Center at Houston, School of Health Information Sciences, 7000 Fannin, Suite 600, Houston, TX 77030, USA. sarmad.sadeghi@uth.tmc.edu
authors.forename: Sarmad
authors.initials: S
authors.lastname: Sadeghi
authors.forename: Afsaneh
authors.initials: A
authors.lastname: Barzi
authors.forename: Navid
authors.initials: N
authors.lastname: Sadeghi
authors.forename: Brent
authors.initials: B
authors.lastname: King
mesh_terms: D015746:Abdominal Pain / Q000145:classification / Q000175:diagnosis*; D001499:Bayes Theorem; D020000:Decision Support Systems, Clinical; D003661:Decision Support Techniques; D003936:Diagnosis, Computer-Assisted / Q000379:methods*; D004632:Emergency Medical Services / Q000379:methods* / Q000458:organization & administration*; D005103:Expert Systems; D006801:Humans; D015203:Reproducibility of Results; D012189:Retrospective Studies; D012680:Sensitivity and Specificity; D013781:Texas; D014218:Triage / Q000379:methods* / Q000458:organization & administration