title: Could prioritisation by emergency medicine dispatchers be improved by using computer-based decision support? A cohort of patients with chest pain.
abstract: BACKGROUND
  To evaluate whether a computer-based decision support system could improve the allocation of patients with acute coronary syndrome (ACS) or a life-threatening condition (LTC). We hypothesised that a system of this kind would improve sensitivity without compromising specificity.
  
  
  METHODS
  A total of 2285 consecutive patients who dialed 112 due to chest pain were asked 10 specific questions and a prediction model was constructed based on the answers. We compared the sensitivity of the dispatchers' decisions with that of the model-based decision support model.
  
  
  RESULTS
  A total of 2048 patients answered all 10 questions. Among the 235 patients with ACS, 194 were allocated the highest prioritisation by dispatchers (sensitivity 82.6%) and 41 patients were given a lower prioritisation (17.4% false negatives). The allocation suggested by the model used the highest prioritisation in 212 of the patients with ACS (sensitivity of 90.2%), while 23 patients were underprioritised (9.8% false negatives). The results were similar when the two systems were compared with regard to LTC and 30-day mortality. This indicates that computer-based decision support could be used either for increasing sensitivity or for saving resources. Three questions proved to be most important in terms of predicting ACS/LTC, [1] the intensity of pain, [2] the localisation of pain and [3] a history of ACS.
  
  
  CONCLUSIONS
  Among patients with acute chest pain, computer-based decision support with a model based on a few fundamental questions could improve sensitivity and reduce the number of cases with the highest prioritisation without endangering the patients.
authors.affiliation: University West, School of Business, Economics and IT, Trollhättan, Sweden. Electronic address: martin.gellerstedt@hv.se.
authors.forename: Martin
authors.initials: M
authors.lastname: Gellerstedt
authors.affiliation: Department of Emergency Medicine, University of Gothenburg, Göteborg, Sweden.
authors.forename: Nina
authors.initials: N
authors.lastname: Rawshani
authors.affiliation: The Pre-hospital Research Centre of Western Sweden, Prehospen, University College of Borås, Borås, Sweden; Department of Medicine, University of Gothenburg, Göteborg, Sweden.
authors.forename: Johan
authors.initials: J
authors.lastname: Herlitz
authors.affiliation: The Pre-hospital Research Centre of Western Sweden, Prehospen, University College of Borås, Borås, Sweden.
authors.forename: Angela
authors.initials: A
authors.lastname: Bång
authors.affiliation: The Pre-hospital Research Centre of Western Sweden, Prehospen, University College of Borås, Borås, Sweden; Department of Ambulance and Prehospital Emergency Care, University of Gothenburg, Göteborg, Sweden.
authors.forename: Carita
authors.initials: C
authors.lastname: Gelang
authors.affiliation: Department of Ambulance and Prehospital Emergency Care, Skaraborg, Sweden.
authors.forename: Jan-Otto
authors.initials: JO
authors.lastname: Andersson
authors.affiliation: The Pre-hospital Research Centre of Western Sweden, Prehospen, University College of Borås, Borås, Sweden.
authors.forename: Anna
authors.initials: A
authors.lastname: Larsson
authors.affiliation: Department of Medicine, University of Gothenburg, Göteborg, Sweden.
authors.forename: Araz
authors.initials: A
authors.lastname: Rawshani
mesh_terms: D054058:Acute Coronary Syndrome / Q000175:diagnosis / Q000453:epidemiology; D000368:Aged; D000369:Aged, 80 and over; D002637:Chest Pain / Q000175:diagnosis* / Q000453:epidemiology*; D000066491:Clinical Decision-Making / Q000379:methods*; D015331:Cohort Studies; D003658:Decision Making, Computer-Assisted; D003661:Decision Support Techniques; D004632:Emergency Medical Services / Q000379:methods* / Q000639:trends; D005260:Female; D006801:Humans; D008297:Male; D012189:Retrospective Studies; D013548:Sweden / Q000453:epidemiology
keywords: ACS; Chest pain; Decision support model; Mortality; Prehospital