{"title":"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","authors":"Salman Nazari-Shirkouhi , Samirasadat Samadi","doi":"10.1016/j.engappai.2025.110204","DOIUrl":null,"url":null,"abstract":"<div><div>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.</div></div>","PeriodicalId":50523,"journal":{"name":"Engineering Applications of Artificial Intelligence","volume":"145 ","pages":"Article 110204"},"PeriodicalIF":8.0000,"publicationDate":"2025-02-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Enhancing healthcare supply chains: A comprehensive evaluation of lean, agile, resilient and green paradigms\",\"authors\":\"Salman Nazari-Shirkouhi , Samirasadat Samadi\",\"doi\":\"10.1016/j.engappai.2025.110204\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><div>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.</div></div>\",\"PeriodicalId\":50523,\"journal\":{\"name\":\"Engineering Applications of Artificial Intelligence\",\"volume\":\"145 \",\"pages\":\"Article 110204\"},\"PeriodicalIF\":8.0000,\"publicationDate\":\"2025-02-09\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Engineering Applications of Artificial Intelligence\",\"FirstCategoryId\":\"94\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S0952197625002040\",\"RegionNum\":2,\"RegionCategory\":\"计算机科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"AUTOMATION & CONTROL SYSTEMS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Engineering Applications of Artificial Intelligence","FirstCategoryId":"94","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0952197625002040","RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"AUTOMATION & CONTROL SYSTEMS","Score":null,"Total":0}
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.
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.