Applications of Artificial Intelligence and Machine Learning in Emergency Medicine Triage - A Systematic Review.

Qasem Ahmed Almulihi, Abdulaziz Adel Alquraini, Fatimah Ahmed Ali Almulihi, Abdullah Abdulaziz Alzahid, Saleh Saeed Al Jathnan Al Qahtani, Mohamed Almulhim, Saeed Hussain Saeed Alqhtani, Faisal Mohammed Nafea Alnafea, Saad Ali Saad Mushni, Nasser Abdullah Alaqil, Mohammad Ibrahim Faya Assiri, Nisreen H Maghraby
{"title":"Applications of Artificial Intelligence and Machine Learning in Emergency Medicine Triage - A Systematic Review.","authors":"Qasem Ahmed Almulihi, Abdulaziz Adel Alquraini, Fatimah Ahmed Ali Almulihi, Abdullah Abdulaziz Alzahid, Saleh Saeed Al Jathnan Al Qahtani, Mohamed Almulhim, Saeed Hussain Saeed Alqhtani, Faisal Mohammed Nafea Alnafea, Saad Ali Saad Mushni, Nasser Abdullah Alaqil, Mohammad Ibrahim Faya Assiri, Nisreen H Maghraby","doi":"10.5455/medarh.2024.78.198-206","DOIUrl":null,"url":null,"abstract":"<p><strong>Background: </strong>Overcrowding in Emergency departments adversely impacts efficiency, patient outcomes, and resource allocation. Accurate triage systems are essential for prioritizing care and optimizing resources. While traditional methods provide a foundation, they often lack precision in addressing modern healthcare complexities. Artificial intelligence (AI) and machine learning (ML) offer advanced capabilities to enhance triage accuracy, improve patient prioritization, and support clinical decision-making, addressing limitations of conventional approaches and paving the way for adaptive triage solutions.</p><p><strong>Objective: </strong>This systematic review aims to assess the use of artificial intelligence (AI) and machine learning (ML) in determining the outcomes of patients presenting in Emergency department (ED) triage.</p><p><strong>Methods: </strong>A systematic search was conducted on April 21, 2023, using electronic databases including PubMed/Medline, Cochrane Library, Ovid, and Google Scholar, without year restrictions. The main outcome of this review was to assess the use of AI and ML in the ED Triage. Articles that used different models of AI and ML to predict various outcomes of patients in the ED setting were included.</p><p><strong>Results: </strong>A total of 17 studies were included in this systematic review. Fifteen studies assessed the role of machine learning methods in emergency department triage, while two studies evaluated the role of AI and machine learning in prehospital triage. The results of our systematic review favor the use of machine learning methods and artificial intelligence in emergency triage. Machine learning models were found to be superior to conventional emergency severity score methods in determining triage, diagnosis, and early management of patients. Among the machine learning methods, the boosting model was slightly more effective.</p><p><strong>Conclusion: </strong>Our study supports the notion that AI and ML are the future of Emergency departments. They aid in predicting patient outcomes and determining appropriate management strategies more efficiently, thereby enhancing decision making in the ED.</p>","PeriodicalId":94135,"journal":{"name":"Medical archives (Sarajevo, Bosnia and Herzegovina)","volume":"78 3","pages":"198-206"},"PeriodicalIF":0.0000,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11813208/pdf/","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Medical archives (Sarajevo, Bosnia and Herzegovina)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.5455/medarh.2024.78.198-206","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0

Abstract

Background: Overcrowding in Emergency departments adversely impacts efficiency, patient outcomes, and resource allocation. Accurate triage systems are essential for prioritizing care and optimizing resources. While traditional methods provide a foundation, they often lack precision in addressing modern healthcare complexities. Artificial intelligence (AI) and machine learning (ML) offer advanced capabilities to enhance triage accuracy, improve patient prioritization, and support clinical decision-making, addressing limitations of conventional approaches and paving the way for adaptive triage solutions.

Objective: This systematic review aims to assess the use of artificial intelligence (AI) and machine learning (ML) in determining the outcomes of patients presenting in Emergency department (ED) triage.

Methods: A systematic search was conducted on April 21, 2023, using electronic databases including PubMed/Medline, Cochrane Library, Ovid, and Google Scholar, without year restrictions. The main outcome of this review was to assess the use of AI and ML in the ED Triage. Articles that used different models of AI and ML to predict various outcomes of patients in the ED setting were included.

Results: A total of 17 studies were included in this systematic review. Fifteen studies assessed the role of machine learning methods in emergency department triage, while two studies evaluated the role of AI and machine learning in prehospital triage. The results of our systematic review favor the use of machine learning methods and artificial intelligence in emergency triage. Machine learning models were found to be superior to conventional emergency severity score methods in determining triage, diagnosis, and early management of patients. Among the machine learning methods, the boosting model was slightly more effective.

Conclusion: Our study supports the notion that AI and ML are the future of Emergency departments. They aid in predicting patient outcomes and determining appropriate management strategies more efficiently, thereby enhancing decision making in the ED.

人工智能和机器学习在急诊医学分诊中的应用--系统性综述。
背景:急诊科过度拥挤对效率、患者预后和资源分配有不利影响。准确的分诊系统对于确定护理的优先次序和优化资源至关重要。虽然传统方法提供了基础,但它们在解决现代医疗保健复杂性方面往往缺乏精确性。人工智能(AI)和机器学习(ML)提供了先进的功能,以提高分诊准确性,改善患者优先级,并支持临床决策,解决传统方法的局限性,并为自适应分诊解决方案铺平道路。目的:本系统综述旨在评估人工智能(AI)和机器学习(ML)在确定急诊科(ED)分诊患者预后方面的应用。方法:于2023年4月21日系统检索,检索对象为PubMed/Medline、Cochrane Library、Ovid、谷歌Scholar等电子数据库,无年份限制。本综述的主要结果是评估人工智能和机器学习在急诊科分诊中的应用。文章使用不同的人工智能和机器学习模型来预测急诊科患者的各种结果。结果:本系统综述共纳入17项研究。15项研究评估了机器学习方法在急诊科分诊中的作用,而两项研究评估了人工智能和机器学习在院前分诊中的作用。我们的系统综述结果有利于在紧急分类中使用机器学习方法和人工智能。机器学习模型在确定患者分诊、诊断和早期管理方面优于传统的紧急严重程度评分方法。在机器学习方法中,助推模型的效果略好。结论:我们的研究支持人工智能和机器学习是急诊科的未来的观点。它们有助于更有效地预测患者的预后和确定适当的管理策略,从而提高急诊科的决策能力。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 求助全文
来源期刊
自引率
0.00%
发文量
0
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信