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PubMed · 16140572

A Bayesian model for triage decision support.

Sarmad Sadeghi·Afsaneh Barzi·Navid Sadeghi·Brent King
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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). CONCLUSION: 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.

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Abdominal PainBayes TheoremDecision Support Systems, ClinicalDecision Support TechniquesDiagnosis, Computer-AssistedEmergency Medical ServicesExpert SystemsHumansReproducibility of ResultsRetrospective StudiesSensitivity and SpecificityTexasTriageJournal Article

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Sarmad Sadeghi, Afsaneh Barzi, Navid Sadeghi, Brent King. 2005-09-02. A Bayesian model for triage decision support.. https://doi.org/10.1016/j.ijmedinf.2005.07.028

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