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Performance evaluation for medical alliance in China based on a novel multi-attribute group decision-making technique with Archimedean copulas-based Hamy operators and extended best-worst method
BACKGROUND: Medical alliance plays an important role in promoting resource sharing, optimizing the allocation of medical resources, establishing a hierarchical diagnosis and treatment system featuring primary diagnosis at the grassroots level, a two-way referral system, separated treatment for acute...
Autores principales: | , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
SAGE Publications
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10486231/ https://www.ncbi.nlm.nih.gov/pubmed/37691765 http://dx.doi.org/10.1177/20552076231196997 |
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author | Xing, Yuping Wang, Jun |
author_facet | Xing, Yuping Wang, Jun |
author_sort | Xing, Yuping |
collection | PubMed |
description | BACKGROUND: Medical alliance plays an important role in promoting resource sharing, optimizing the allocation of medical resources, establishing a hierarchical diagnosis and treatment system featuring primary diagnosis at the grassroots level, a two-way referral system, separated treatment for acute and chronic diseases, and dynamic cooperation. Thus, comprehensive performance evaluation for medical alliance is a necessary research that involves a multi-attribute group decision-making problem. OBJECTIVE: The aim of this paper is to develop a new multi-attribute group decision-making evaluation framework and new weight method to better efficaciously resolve the issues of evaluation for the medical alliance. METHODS: Firstly, Archimedean copula and co-copula operational rules, called Archimedean co-copula, and the form of q-rung orthopair fuzzy Hamy mean aggregation operator based on Archimedean co-copula operational rules are also developed. Secondly, an extended q-rung orthopair fuzzy extended best-worst method satisfying multiplicative consistency is developed to originate the weight information of the attributes. The new weight method can integrate the membership and non-membership of assessment information, improve constancy for group decision making and get an extremely reliable weight consequence. Finally, a novel multi-attribute group decision-making framework is presented based on the proposed q-rung orthopair fuzzy Archimedean copula and co-copula Hamy mean aggregation operator and q-rung orthopair fuzzy Euclidean best-worst method. Furthermore, the new multi-attribute group decision-making method is applied to comprehensive performance evaluation for medical alliance in Shanghai, and the effectiveness of the new method is also demonstrated. RESULTS: The results show that the proposed multi-attribute group decision-making method with Archimedean copulas-based Hamy operators and extended best-worst in this paper outperforms some existing methods and provides support for policymakers seeking the use of patient- and community-centered health evaluations to improve health services. CONCLUSION: The proposed method is a theoretical guidance method and a good reference for the evaluation of medical alliances of other regions in China. |
format | Online Article Text |
id | pubmed-10486231 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | SAGE Publications |
record_format | MEDLINE/PubMed |
spelling | pubmed-104862312023-09-09 Performance evaluation for medical alliance in China based on a novel multi-attribute group decision-making technique with Archimedean copulas-based Hamy operators and extended best-worst method Xing, Yuping Wang, Jun Digit Health Original Research BACKGROUND: Medical alliance plays an important role in promoting resource sharing, optimizing the allocation of medical resources, establishing a hierarchical diagnosis and treatment system featuring primary diagnosis at the grassroots level, a two-way referral system, separated treatment for acute and chronic diseases, and dynamic cooperation. Thus, comprehensive performance evaluation for medical alliance is a necessary research that involves a multi-attribute group decision-making problem. OBJECTIVE: The aim of this paper is to develop a new multi-attribute group decision-making evaluation framework and new weight method to better efficaciously resolve the issues of evaluation for the medical alliance. METHODS: Firstly, Archimedean copula and co-copula operational rules, called Archimedean co-copula, and the form of q-rung orthopair fuzzy Hamy mean aggregation operator based on Archimedean co-copula operational rules are also developed. Secondly, an extended q-rung orthopair fuzzy extended best-worst method satisfying multiplicative consistency is developed to originate the weight information of the attributes. The new weight method can integrate the membership and non-membership of assessment information, improve constancy for group decision making and get an extremely reliable weight consequence. Finally, a novel multi-attribute group decision-making framework is presented based on the proposed q-rung orthopair fuzzy Archimedean copula and co-copula Hamy mean aggregation operator and q-rung orthopair fuzzy Euclidean best-worst method. Furthermore, the new multi-attribute group decision-making method is applied to comprehensive performance evaluation for medical alliance in Shanghai, and the effectiveness of the new method is also demonstrated. RESULTS: The results show that the proposed multi-attribute group decision-making method with Archimedean copulas-based Hamy operators and extended best-worst in this paper outperforms some existing methods and provides support for policymakers seeking the use of patient- and community-centered health evaluations to improve health services. CONCLUSION: The proposed method is a theoretical guidance method and a good reference for the evaluation of medical alliances of other regions in China. SAGE Publications 2023-09-06 /pmc/articles/PMC10486231/ /pubmed/37691765 http://dx.doi.org/10.1177/20552076231196997 Text en © The Author(s) 2023 https://creativecommons.org/licenses/by-nc/4.0/This article is distributed under the terms of the Creative Commons Attribution-NonCommercial 4.0 License (https://creativecommons.org/licenses/by-nc/4.0/) which permits non-commercial use, reproduction and distribution of the work without further permission provided the original work is attributed as specified on the SAGE and Open Access page (https://us.sagepub.com/en-us/nam/open-access-at-sage). |
spellingShingle | Original Research Xing, Yuping Wang, Jun Performance evaluation for medical alliance in China based on a novel multi-attribute group decision-making technique with Archimedean copulas-based Hamy operators and extended best-worst method |
title | Performance evaluation for medical alliance in China based on a novel multi-attribute group decision-making technique with Archimedean copulas-based Hamy operators and extended best-worst method |
title_full | Performance evaluation for medical alliance in China based on a novel multi-attribute group decision-making technique with Archimedean copulas-based Hamy operators and extended best-worst method |
title_fullStr | Performance evaluation for medical alliance in China based on a novel multi-attribute group decision-making technique with Archimedean copulas-based Hamy operators and extended best-worst method |
title_full_unstemmed | Performance evaluation for medical alliance in China based on a novel multi-attribute group decision-making technique with Archimedean copulas-based Hamy operators and extended best-worst method |
title_short | Performance evaluation for medical alliance in China based on a novel multi-attribute group decision-making technique with Archimedean copulas-based Hamy operators and extended best-worst method |
title_sort | performance evaluation for medical alliance in china based on a novel multi-attribute group decision-making technique with archimedean copulas-based hamy operators and extended best-worst method |
topic | Original Research |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10486231/ https://www.ncbi.nlm.nih.gov/pubmed/37691765 http://dx.doi.org/10.1177/20552076231196997 |
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