The use of Artificial Intelligence in Emergency Triage versus Conventional Triage in Adult Patients Affects Emergency Department Overcrowding.

Abstract

Aim: This review aims to identify whether using artificial intelligence (AI) in emergency triage versus conventional triage in adult patients affects emergency department (ED) overcrowding.

Background: In emergency medicine, triage is an essential procedure for establishing patient care priorities according to severity (Defilippo et al., 2023). Typically, this practice depends on the experience and decision-making skills of emergency nurses, which can sometimes result in differences in how patients are evaluated and interruptions in receiving timely care. Due to several factors, such as ED overcrowding, the triage process can be delayed or misjudged. Overcrowding occurs when the number of patients waiting to be seen, examined, or discharged from the ED exceeds the ED's structural or personnel capacity (Cameron et al., 2009). Since the issue of human resources is a complex issue, finding alternative methods to aid the workforce such as Artificial Intelligence (AI) should be investigated. This paper aims to discover how using AI in emergency triage can reduce and accelerate the triage process.

Methods: an integrative literature review was conducted via systematic research in three electronic databases, CINHAL, MEDLINE, and PubMed. The literature included studies that used artificial intelligence in triage and how AI affects emergency overcrowding. Some of these studies did not measure overcrowding directly but studied the effects of overcrowding, and how can reflect the care provided, and the time to initiate treatment. Results: AI-based triage systems, which use machine learning algorithms to forecast patient acuity, hospitalisation, and death, provide notable improvements over conventional methods. These systems reduce errors related to human judgment and cognitive biases by improving accuracy and efficiency in triage choices using clinical data and electronic health records (Lee et al., 2024; Shahbandegan et al., 2022). Conventional triage techniques, on the other hand, mostly rely on the interpretation of individual clinicians, leaving room for errors. AI-based solutions, such as those that employ extreme gradient boosting algorithms, offer real-time decision support, enhancing patient outcomes by recommending crucial treatments for prompt and suitable care in emergency rooms. When compared to conventional techniques, AI-based triage systems demonstrate higher prediction ability and the potential to improve emergency care. Conclusion: Artificial intelligence showed positive results in emergency health settings and reduced overcrowding. In addition, future research requires algorithm refinement to increase generalisability and mitigate false positive cases.