| Popis: |
Objective: Overcrowding is a challenge for emergency departments throughout the world. Triage systems categorize the patients based on medical emergencies in order to avoid the malpractices. The present study aimed to test the validity of an artificial intelligence tool, ‘Decision Trees’, in emergency medicine triage. Methods: This prospective, cross-sectional, clinical study was conducted in an emergency department of a tertiary care hospital. A total of 1999 patients over 18 years were included into the study. The triage stuff were trained before the study with the Australasian Triage Scale. Two independent observers rate the ultimate triage category of study patients. A new algorithym by ‘Decision Trees’ was constructed at the end of the study. Results: The mean age of the study patients were 41.1±17,2 and 49.1 % of them (n=981) were male. There were 867 patients (43.3%) with triage category of five and 14 (0.7%) patients with triage category one. The most common clinical descriptors of the patients were minimal pain with no high risk features 20.5% of them (n=409) and minor symptoms of low risk conditions 18.1% of them (n=362). There was an excellent consistency between two independent observers (kappa value: 0.997. The new algorithm by ‘Decision Trees’ rated wrong in only one patient. The accuracy rate was 99.9%. The consistency between ATS and ‘Decision Trees was excellent (kappa value: 0.999). There was average consistency between physicians and paramedics. (kappa value: 0.541).Conclusion: Decision trees as an artificial intelligence model should be used for producing practical triage algorithms as a decision support tool in emergency departments. |
