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Identifying resilience strategies for disruption management in the healthcare supply chain during COVID-19 by digital innovations: A systematic literature review.
Arji, Goli; Ahmadi, Hossein; Avazpoor, Pejman; Hemmat, Morteza.
Afiliación
  • Arji G; Health Information Management, School of Nursing and Midwifery, Saveh University of Medical Sciences, Iran.
  • Ahmadi H; Centre for Health Technology, Faculty of Health, University of Plymouth, Plymouth, PL4 8AA, UK.
  • Avazpoor P; Department of Agriculture Economics, Ferdowsi University of Mashhad, Iran.
  • Hemmat M; Health Information Management, School of Nursing and Midwifery, Saveh University of Medical Sciences, Iran.
Inform Med Unlocked ; 38: 101199, 2023.
Article en En | MEDLINE | ID: mdl-36873583
ABSTRACT
The worldwide spread of the COVID-19 disease has had a catastrophic effect on healthcare supply chains. The current manuscript systematically analyzes existing studies mitigating strategies for disruption management in the healthcare supply chain during COVID-19. Using a systematic approach, we recognized 35 related papers. Artificial intelligence (AI), block chain, big data analytics, and simulation are the most important technologies employed in supply chain management in healthcare. The findings reveal that the published research has concentrated mainly on generating resilience plans for the management of COVID-19 impacts. Furthermore, the vulnerability of healthcare supply chains and the necessity of establishing better resilience methods are emphasized in most of the research. However, the practical application of these emerging tools for managing disturbance and warranting resilience in the supply chain has been examined only rarely. This article provides directions for additional research, which can guide researchers to develop and conduct impressive studies related to the healthcare supply chain for different disasters.
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Tipo de estudio: Prognostic_studies / Systematic_reviews Idioma: En Revista: Inform Med Unlocked Año: 2023 Tipo del documento: Article País de afiliación: Irán

Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Tipo de estudio: Prognostic_studies / Systematic_reviews Idioma: En Revista: Inform Med Unlocked Año: 2023 Tipo del documento: Article País de afiliación: Irán