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Metaverse healthcare supply chain: Conceptual framework and barrier identification.

Authors :
Chen, Zhen-Song
Ruan, Jie-Qun
Source :
Engineering Applications of Artificial Intelligence. Jul2024:Part A, Vol. 133, pN.PAG-N.PAG. 1p.
Publication Year :
2024

Abstract

In the complex landscape of healthcare supply chains, challenges extend well beyond internal operational concerns, necessitating innovative solutions that align with the evolving technological landscape. The metaverse has emerged as a compelling solution to address these challenges by offering the potential for significant enhancements in efficiency and traceability. This paper focuses on both the theoretical underpinnings and practical applications of integrating the metaverse into healthcare supply chain management. Employing the Cyber-Physical-Social System framework, the study comprehensively explores the pivotal roles played by emerging information technologies in constructing the metaverse and overseeing various stages of the healthcare supply chain. The practical application is exemplified through the introduction of the TraceLink example, illustrating the successful healthcare supply chain digitalization. Furthermore, the paper introduces a barrier framework based on the Technology-Organization-Environment framework, addressing obstacles such as obsolete management technologies, data security, organizational structure, and societal acceptance. The examination and comparison of individual barriers using the Bayesian best-worst method further provide a nuanced understanding of the challenges. This comprehensive exploration not only equips research scholars with profound insights for addressing these challenges but also lays the foundation for future endeavors. The envisioned improvements in healthcare service delivery hold substantial benefits for patients and the industry, making this research a pioneering exploration of the metaverse's potential applications within the realm of the healthcare supply chain. [ABSTRACT FROM AUTHOR]

Details

Language :
English
ISSN :
09521976
Volume :
133
Database :
Academic Search Index
Journal :
Engineering Applications of Artificial Intelligence
Publication Type :
Academic Journal
Accession number :
177605487
Full Text :
https://doi.org/10.1016/j.engappai.2024.108113