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Widespread ignorance of the existence of non-hospital emergency services affects the over-use of HES. Most users use the HES as a service of immediate PC, a rapid way of obtaining health care. Users have a very favourable opinion of HES care, which is not stated in the case of non-hospital emergency services. In order to improve use of the HES, the population needs to receive better health education.

Study objective Approximately 5% of emergency department patients present with altered mental status (AMS). AMS is diagnostically challenging because of the wide range of causes and is associated with high mortality. We sought to develop a clinical decision rule predicting admission risk among emergency department (ED) patients with AMS. Methods Using retrospective chart review of ED encounters for AMS over a 2‐month period, we recorded causes of AMS and numerous clinical variables. Encounters were split into those admitted to the hospital (“cases”) and those discharged from the ED (“controls”). Using the first month's data, variables correlated with hospital admission were identified and narrowed using univariate and multivariate statistics, including recursive partitioning. These variables were then organized into a clinical decision rule and validated on the second month's data. The decision rule results were also compared to 1‐year mortality. Results We identified 351 encounters for AMS over a 2‐month period. Significant contributors to AMS included intoxication and chronic disorder decompensation. ED data predicting hospital admission included vital sign abnormalities, select lab studies, and psychiatric/intoxicant history. The decision rule sorted patients into low, moderate, or high risk of admission (11.1%, 44.3%, and 89.1% admitted, respectively) and was predictive of 1‐year mortality (low‐risk group 1.8%, high‐risk group 34.3%). Conclusions We catalogued common causes for AMS among patients presenting to the ED, and our data‐driven decision tool triaged these patients for risk of admission with good predictive accuracy. These methods for creating clinical decision rules might be further studied and improved to optimize ED patient care.