Dept. Medical and Surgical Sciences, Magna Graecia University of Catanzaro, Catanzaro, Italy.
DIMES Department of Informatics, Modeling, Electronics and Systems, UNICAL, Rende, Cosenza, Italy.
Sci Rep. 2024 May 31;14(1):12548. doi: 10.1038/s41598-024-63376-2.
Patient triage is crucial in emergency departments, ensuring timely and appropriate care based on correctly evaluating the emergency grade of patient conditions. Triage methods are generally performed by human operator based on her own experience and information that are gathered from the patient management process. Thus, it is a process that can generate errors in emergency-level associations. Recently, Traditional triage methods heavily rely on human decisions, which can be subjective and prone to errors. A growing interest has recently been focused on leveraging artificial intelligence (AI) to develop algorithms to maximize information gathering and minimize errors in patient triage processing. We define and implement an AI-based module to manage patients' emergency code assignments in emergency departments. It uses historical data from the emergency department to train the medical decision-making process. Data containing relevant patient information, such as vital signs, symptoms, and medical history, accurately classify patients into triage categories. Experimental results demonstrate that the proposed algorithm achieved high accuracy outperforming traditional triage methods. By using the proposed method, we claim that healthcare professionals can predict severity index to guide patient management processing and resource allocation.
患者分诊在急诊科至关重要，它可以根据患者病情的紧急程度进行正确评估，确保及时、恰当的治疗。分诊方法通常由操作人员根据自己的经验和从患者管理过程中收集的信息来进行。因此，这是一个可能会导致紧急程度关联出现错误的过程。最近，传统的分诊方法严重依赖于人工决策，而这些决策可能具有主观性且容易出错。最近，人们越来越关注利用人工智能（AI）开发算法，以最大限度地收集信息并最大限度地减少患者分诊处理中的错误。我们定义并实现了一个基于人工智能的模块，用于管理急诊科患者的紧急代码分配。它使用来自急诊科的历史数据来训练医疗决策过程。包含相关患者信息（如生命体征、症状和病史）的数据可以准确地将患者分类到分诊类别中。实验结果表明，所提出的算法在准确性方面表现出色，优于传统的分诊方法。通过使用所提出的方法，我们声称医疗保健专业人员可以预测严重程度指数，以指导患者管理流程和资源分配。
