Introduction
Emergency Department (ED) overcrowding and resulting delayed patient care is a rapidly growing worldwide problem leading to increased mortality and morbidity.39,40,41 ED triage presents the first opportunity to promptly identify high-risk patients and efficiently allocate ED resources. Current ED triage systems have suboptimal ability to differentiate critically ill patients due to subtle signs or symptoms often being difficult to recognize. This results in delayed medical care and worsened patient outcomes. In recent years, there is growing evidence to suggests that artificial intelligence-based ED-triage systems might enhance an ED clinician’s ability to identify patients most in need of intensive ED resources. These AI-driven clinical decision support tools have the ability to predict various patient outcomes and prioritize patients’ ED care almost instantaneously based on traditional ED triage information and data available in the electronic health record (EHR). Application of AI into the ED triage process provides a novel system that can assist ED clinicians in their decision-making process: Decision-Making Support System (DMSS). AI-based approaches have incredible capacity to analyze large amount of complex data in a very short time, potentially providing near immediate high yield recommendations for ED clinicians to consider. A systematic review was performed using the AAEM CPC Statement on Protocols for literature search/grading process to identify articles that could answer the following question: “Can application of artificial intelligence improve Emergency Department triage performance?”
