1
Director de la Universidad Regional Autónoma de los Andes (UNIANDES) Sede Santo Domingo, Eccuador
(dir.santodomingo@uniandes.edu.ec)
2
Docente de la carrera de Medicina de la Universidad Regional Autónoma de los Andes (UNIANDES Ambato), Ecuador
(ua.beckerneto@uniandes.edu.ec)
3
Docente de la carrera de Medicina de la Universidad Regional Autónoma de los Andes (UNIANDES Ambato), Ecuador
(ua.miguelramos@uniandes.edu.ec)
Abstract :
Effective procurement of clinical devices in healthcare demands a sophisticated decision-making approach integrating diverse data sources from multiple devices, brands, and suppliers, particularly within the context of information fusion. This study addresses this challenge by proposing an improved best-worst method harmonized with information fusion techniques and multi-criteria decision-making methodologies. The background emphasizes the dynamic nature of healthcare procurement, necessitating systematic strategies for navigating the complexities of device selection and integration. Recognizing the intricacies inherent in this challenge, the problem statement revolves around enhancing the best-worst method to amalgamate data from clinical devices while concurrently evaluating brands and suppliers. This aims to optimize performance and minimize costs within the information fusion paradigm. Our proposed methodology introduces an augmented best-worst approach, encompassing weighted criteria assessment for clinical devices, brands, and suppliers, providing a more adaptable and nuanced decision-making framework tailored to the information fusion landscape. The results showcase a structured evaluation matrix derived from refined weighted criteria, elucidating the relative performance and strengths across various entities within the healthcare procurement ecosystem. Emphasizing reliability, compatibility, innovation, and quality assurance, this process highlights pivotal factors influencing procurement decisions within the realm of information fusion.
Keywords :
Healthcare Supply Chain; Decision Support Systems; Information Fusion; Supplier Selection; Multimodal Data Integration; Procurement Strategies; Decision-making Processes.
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| Style | # |
|---|---|
| MLA | Fredy Cañizares Galarza, Becker Neto Mullo, Miguel Ramos Argilagos. "Information Fusion from Multimodal Clinical Sensors for Effective Supplier Decision-Making in Healthcare." Fusion: Practice and Applications, Vol. 14, No. 1, 2024 ,PP. 149-157 (Doi : https://doi.org/10.54216/FPA.140113) |
| APA | Fredy Cañizares Galarza, Becker Neto Mullo, Miguel Ramos Argilagos. (2024). Information Fusion from Multimodal Clinical Sensors for Effective Supplier Decision-Making in Healthcare. Journal of Fusion: Practice and Applications, 14 ( 1 ), 149-157 (Doi : https://doi.org/10.54216/FPA.140113) |
| Chicago | Fredy Cañizares Galarza, Becker Neto Mullo, Miguel Ramos Argilagos. "Information Fusion from Multimodal Clinical Sensors for Effective Supplier Decision-Making in Healthcare." Journal of Fusion: Practice and Applications, 14 no. 1 (2024): 149-157 (Doi : https://doi.org/10.54216/FPA.140113) |
| Harvard | Fredy Cañizares Galarza, Becker Neto Mullo, Miguel Ramos Argilagos. (2024). Information Fusion from Multimodal Clinical Sensors for Effective Supplier Decision-Making in Healthcare. Journal of Fusion: Practice and Applications, 14 ( 1 ), 149-157 (Doi : https://doi.org/10.54216/FPA.140113) |
| Vancouver | Fredy Cañizares Galarza, Becker Neto Mullo, Miguel Ramos Argilagos. Information Fusion from Multimodal Clinical Sensors for Effective Supplier Decision-Making in Healthcare. Journal of Fusion: Practice and Applications, (2024); 14 ( 1 ): 149-157 (Doi : https://doi.org/10.54216/FPA.140113) |
| IEEE | Fredy Cañizares Galarza, Becker Neto Mullo, Miguel Ramos Argilagos, Information Fusion from Multimodal Clinical Sensors for Effective Supplier Decision-Making in Healthcare, Journal of Fusion: Practice and Applications, Vol. 14 , No. 1 , (2024) : 149-157 (Doi : https://doi.org/10.54216/FPA.140113) |