文件夹
医疗后送
计算机科学
运筹学
业务
过程管理
风险分析(工程)
工程管理
管理科学
工程类
运营管理
医疗急救
医学
财务
作者
Phillip R. Jenkins,Matthew J. Robbins,Brian J. Lunday
出处
期刊:BMJ military health
[BMJ]
日期:2021-01-18
卷期号:169 (e1): e90-e92
被引量:4
标识
DOI:10.1136/bmjmilitary-2020-001631
摘要
Senior military leaders and medical practitioners continuously seek new ways to improve the performance and organisation of deployed medical evacuation (MEDEVAC) systems to minimise mortality rates of combat casualties. The objective of this paper is to highlight how recent research in the fields of operations research and machine learning can be leveraged to better inform the implementation and modification of current and future MEDEVAC tactics, techniques and procedures for combat operations in a deployed environment. More specifically, this paper discusses state-of-the-art techniques that optimise the management of MEDEVAC assets prior to and during combat operations. These recent research efforts emphasise that military healthcare administrators should contribute to and extend the evolving portfolio of research that seeks to design and develop decision support systems leveraging artificial intelligence and operations research to improve MEDEVAC system performance.
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