This prospective cross sectional trial was conducted in an ED of a tertiary care hospital with an annually census of 73000 patients between 1–10 July in 2011. The local ethical committee approved the study.
Patients presented to the ED with any complaints over 17 years old included into the study. Patients under 18 years old and denied to give inform consent was excluded from the study.
Eight paramedics and one nurse working in the triage area were trained for Australia Triage Scale (ATS) (Supplement) before the study. During the training, the categories of ATS were defined firstly and clinical scenarios composed the second stage. In the third stage, a practical application was implemented with 100 patients in the triage area.
Blood pressure, body temperature, oxygen saturation, heart and respiration rate, age, gender, presenting symptom, co-morbidities, Glasgow Coma Scale (GCS) score, verbal pain score that classifying the patients as serious pain, average pain, less pain and no pain and the triage category according to ATS were recorded to the study form. Blood pressure, pulse oxymeter and pulse rate were measured by a monitor. Body temperature was measured by temporal route (Exergen Temporal Artery Thermometry).
Triage Categories for ATS
Category 1: Patients who need resuscitation and admitted to the resuscitation area immediately
Category 2: Patients with a life-threating condition or risky for any limb. These patients were admitted to the telemetry unit or resuscitation area within 10 minutes.
Category 3: Patients with pathology of potential to progress life-threatining conditions or urgent interventions. These patients were admitted to the ED in 30 minutes.
Category 4: Patients who have pathologies that may take care in one and two hours. These patients admitted to ED within 60 minutes.
Category 5: Patients with non-urgent complaints and admitted to the ED in 120 minutes.
The details of ATS have been displayed in Supplement.
A chart defining ATS was posted on the triage area for triage stuff. Patients waiting for ED admission re-triaged with 30 minutes intervals for any change in triage category.
Two independent senior residents at the end of the study determined the ultimate triage category. An associated professor on emergency medicine evaluated the patients if there was any inconsistency between two observers.
Statistical Analysis
‘Decision trees’ as the artificial intelligence model was used in the present study. ‘Mathlab in classregtree method’ was used for decision tree analysis. This method generates decision trees from the upper root point to down. There is a separation parameter and a condition in every branching point. Separation criteria providing most information were used in the algorithm (Gini’s diversity index). A branch was formed in the right and left side of every criteria and value. The values below the selected variable and value are usually localized at the left sight. The algorithm continues to construct the peripheral branches until there is little point. The smallest point number in branches for calculation was used, 1. The other analysis of the study was performed by MedCalc. Numerical data was presented as mean ± standard deviation or median (interquartile range/min-max) and categorical data as rates. The consistency between decision trees, paramedic and physicians were displayed by kappa value.