Implementing Triage-Bot: Supporting the Current Practice for Triage Nurses.

IF 0.8 Q4 SURGERY
Kim Sears, Sam Belbin, Elyas Rashno, Drishti Sharma, Kevin Woo, Farhana Zulkernine, Ciprian Daniel Neagu, Bita Amani, Furkan Alaca
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Abstract

In Canada, emergency departments (ED) have 15.1 million unscheduled visits every year; this has been suggested to indicate that patients rely on ED to address the gaps experienced by 6.5 million Canadians who lack a primary care provider. When this large number of visits is coupled with a predicted shortage of 100,000 nurses in Canada by 2030, ED can be expected to face resource limitations, which highlights the importance of triage systems as a source of immediate support. Technology that incorporates innovative analytical methods, automation of routine, and efficient processing can be leveraged to enhance patient outcomes, streamline clinical processes, and improve the overall quality and efficiency of healthcare delivery. This paper aims to highlight how the Triage-Bot, a proposed AI system, can assist ED nurses when triaging patients. The Triage-Bot system is based on the Canadian Triage and Acuity Scale (CTAS), which currently serves as a standardized and highly effective tool for prioritizing patient care in emergency departments across the country. Pre-set and open-ended questions are asked using voice and video, allowing patients to describe their health concerns and conditions. Triage-Bot automatically measures the following vital signs: heart rate (HR), heart rate variability (HRV), oxygen saturation (SpO2), respiratory rate (RR), blood pressure (BP), blood glucose (BG), and stress. The system uses artificial intelligence models, particularly those with a deep learning approach that simultaneously analyzes both the user's facial expression and voice tone. Implementation: A systematic review addressed the implications of AI in nursing and concluded that it could contribute to patient care by providing personalized instructions and/or remotely monitoring patients. The Triage-Bot system can be implemented in healthcare facilities, such as emergency department waiting rooms. The information it collects can then be added to a patient's health records to support nurses in assessing the severity of each patient's condition. Limitations: If the system is accessed without a nurse's guidance, it is imperative that the user receives information regarding when to visit a healthcare provider or ED. Continuous improvements in Triage-Bot's accessibility for patients with varying abilities are required to ensure that the system remains user-friendly during times of illness. The voice and text interaction can also be influenced by a user's understanding of language, culture, and age-related factors.

实施分诊机器人:支持分诊护士的当前实践。
在加拿大,急诊科(ED)每年有 1510 万次计划外就诊;这表明,有 650 万加拿大人缺乏初级医疗服务提供者,病人依赖急诊科来弥补他们的不足。预计到 2030 年,加拿大将短缺 10 万名护士,再加上如此庞大的就诊人数,预计急诊室将面临资源限制,这就凸显了分流系统作为即时支持来源的重要性。结合创新分析方法、常规自动化和高效处理的技术可用于提高患者治疗效果、简化临床流程以及改善医疗服务的整体质量和效率。本文旨在重点介绍拟议中的人工智能系统--分诊机器人(Triage-Bot)如何协助急诊室护士分诊病人。分诊机器人系统基于加拿大分诊和急性量表(CTAS),该量表目前是全国各地急诊科确定病人护理优先次序的标准化高效工具。通过语音和视频提出预设和开放式问题,让患者描述自己的健康问题和状况。Triage-Bot 可自动测量以下生命体征:心率 (HR)、心率变异性 (HRV)、血氧饱和度 (SpO2)、呼吸频率 (RR)、血压 (BP)、血糖 (BG) 和压力。该系统使用人工智能模型,尤其是采用深度学习方法的模型,可同时分析用户的面部表情和语音语调。实施:一项系统性综述探讨了人工智能在护理中的应用,并得出结论:人工智能可以通过提供个性化指导和/或远程监控病人,为病人护理做出贡献。分诊机器人系统可在急诊科候诊室等医疗设施中使用。它收集的信息可以添加到病人的健康记录中,帮助护士评估每位病人病情的严重程度。局限性:如果在没有护士指导的情况下使用该系统,用户必须获得有关何时去医疗机构或急诊室就诊的信息。需要不断改进 Triage-Bot 对不同能力病人的易用性,以确保该系统在病人生病期间仍然方便用户使用。用户对语言、文化和年龄相关因素的理解也会影响语音和文本交互。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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CiteScore
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