加强医疗保健供应链:对精益、敏捷、弹性和绿色范例的全面评估

IF 8 2区 计算机科学 Q1 AUTOMATION & CONTROL SYSTEMS
Salman Nazari-Shirkouhi , Samirasadat Samadi
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引用次数: 0

摘要

2019冠状病毒病疫情突出表明,需要高效和有弹性的医疗保健供应链,以应对需求激增和传染性浪费。许多研究已经评估了造血干细胞中单个的精益、敏捷、弹性或绿色(LARG)范式,但缺乏对所有四种范式的综合评估。我们首次尝试使用毕达哥拉斯模糊决策试验和评估实验室(PF-DEMATEL)、解释结构建模(ISM)和贝叶斯网络(BN)等综合方法来评估HSC性能中的LARG参数。我们通过文献回顾分析确定了LARG HSC最重要的影响因素,并通过专家访谈对其进行了修改和验证。采用PF-DEMATEL和ISM方法建立LARG变量的因果关系图和层次网络模型,确定各因素之间的相互依存关系。将结果映射到BN方法提供了变量之间耦合关系强度的量化。确定的关键因素包括一致的医疗服务(CMS)、灵活性(F)、痛苦情况下的紧急医疗服务(EMSDS)和医疗设备维护计划(MEMP)。该研究基于五家医院的数据得出的结果表明,实现高水平LARG绩效的可能性为62%。提出的模型为管理者提供了LARG HSC的最关键参数,并增强了他们对这些参数之间关系的理解。该模型可作为改进医疗保健服务提供和满足患者需求的实用指南。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Enhancing healthcare supply chains: A comprehensive evaluation of lean, agile, resilient and green paradigms
The COVID-19 outbreak highlighted the need for efficient and resilient healthcare supply chains (HSCs) that can handle surges in demand and infectious waste. Many studies have evaluated individual lean, agile, resilient, or green (LARG) paradigms in HSCs, but a comprehensive evaluation of all four together is lacking. We present the first attempt to evaluate LARG parameters in HSC performance, using integrated methods including Pythagorean Fuzzy Decision-making Trial and Evaluation Laboratory (PF-DEMATEL), interpretive structural modeling (ISM), and Bayesian network (BN). We identified the most important contributing factors of the LARG HSC through literature review analysis, which was modified and validated through expert interviews. Cause-and-effect diagrams and hierarchical network models of LARG variables were developed using PF-DEMATEL and ISM methods to determine the interdependent relationships between factors. Mapping the outcomes to the BN method provided a quantification of the intensity of coupling relationships between variables. The key factors identified include Consistent Medical Service (CMS), Flexibility (F), Emergency Medical Services in Distressing Situations (EMSDS), and Medical Equipment Maintenance Program (MEMP). The study’s results, based on data from five hospitals, indicate a 62% probability of achieving high-level LARG performance. The proposed model provides managers with the most critical parameters of the LARG HSC and enhances their understanding of the relationships between these parameters. The proposed model serves as a practical guide for improving healthcare service delivery and meeting patient needs.
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来源期刊
Engineering Applications of Artificial Intelligence
Engineering Applications of Artificial Intelligence 工程技术-工程:电子与电气
CiteScore
9.60
自引率
10.00%
发文量
505
审稿时长
68 days
期刊介绍: Artificial Intelligence (AI) is pivotal in driving the fourth industrial revolution, witnessing remarkable advancements across various machine learning methodologies. AI techniques have become indispensable tools for practicing engineers, enabling them to tackle previously insurmountable challenges. Engineering Applications of Artificial Intelligence serves as a global platform for the swift dissemination of research elucidating the practical application of AI methods across all engineering disciplines. Submitted papers are expected to present novel aspects of AI utilized in real-world engineering applications, validated using publicly available datasets to ensure the replicability of research outcomes. Join us in exploring the transformative potential of AI in engineering.
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