Operational Sciences, Air Force Institute of Technology, Wright-Patterson AFB, Ohio, USA
Operational Sciences, Air Force Institute of Technology, Wright-Patterson AFB, Ohio, USA.
BMJ Mil Health. 2023 May;169(e1):e90-e92. doi: 10.1136/bmjmilitary-2020-001631. Epub 2021 Jan 18.
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.
高级军事领导人和医务人员不断寻求新的方法来提高部署的医疗后送(MEDEVAC)系统的性能和组织,以最大限度地降低战斗伤员的死亡率。本文的目的是强调如何利用运筹学和机器学习领域的最新研究,为部署环境中的战斗行动提供更好的信息,以实施和修改当前和未来的 MEDEVAC 战术、技术和程序。更具体地说,本文讨论了在战斗行动之前和期间优化 MEDEVAC 资产管理的最新技术。这些最新的研究努力强调,军事医疗保健管理人员应该为寻求利用人工智能和运筹学设计和开发决策支持系统以提高 MEDEVAC 系统性能的不断发展的研究组合做出贡献并加以扩展。