Big data-enabled solutions framework to overcoming the barriers to circular economy initiatives in healthcare sector

dc.contributor.author Yigit Kazancoglu
dc.contributor.author Muhittin Saǧnak
dc.contributor.author Çisem Lafci
dc.contributor.author Sunil Luthra
dc.contributor.author Anil Kumar
dc.contributor.author Caner Taçoğlu
dc.date.accessioned 2025-10-06T17:50:25Z
dc.date.issued 2021
dc.description.abstract Ever-changing conditions and emerging new challenges affect the ability of the healthcare sector to survive with the current system and to maintain its processes effectively. In the healthcare sector the conservation of the natural resources is being obstructed by insufficient infrastructure for managing residual waste resulting from single-use medical materials increased energy use and its environmental burden. In this context circularity and sustainability concepts have become essential in healthcare to meliorate the sector’s negative impacts on the environment. The main aim of this study is to identify the barriers related to circular economy (CE) in the healthcare sector apply big data analytics in healthcare and provide solutions to these barriers. The contribution of this research is the detailed examination of the current healthcare literature about CE adaptation and a proposal for a big data-enabled solutions framework to barriers to circularity using fuzzy bestworst Method (BWM) and fuzzy VIKOR. Based on the findings managerial policy and theoretical implementations are recommended to support sustainable development initiatives in the healthcare sector. © 2021 Elsevier B.V. All rights reserved.
dc.identifier.doi 10.3390/ijerph18147513
dc.identifier.issn 16604601, 16617827
dc.identifier.issn 1660-4601
dc.identifier.uri https://www.scopus.com/inward/record.uri?eid=2-s2.0-85109961117&doi=10.3390%2Fijerph18147513&partnerID=40&md5=51cba72365ce3579f5b459f489a3db98
dc.identifier.uri https://gcris.yasar.edu.tr/handle/123456789/8948
dc.language.iso English
dc.publisher MDPI
dc.relation.ispartof International Journal of Environmental Research and Public Health
dc.source International Journal of Environmental Research and Public Health
dc.subject Barriers, Big Data Tools, Circular Economy, Fuzzy Best-worst, Fuzzy Vikor, Healthcare Sector, Sustainable Development, Waste Issues, Database, Environmental Economics, Environmental Impact, Fuzzy Mathematics, Health Care, Management Practice, Policy Approach, Sustainability, Sustainable Development, Theoretical Study, Article, Artificial Neural Network, Data Analysis, Economic Aspect, Energy, Health Care Cost, Health Care Delivery, Health Care System, Machine Learning, Mathematical Model, Medical Education, Medical Informatics, Network Analysis, Nonhuman, Quality Of Life, Solid Waste Management, Human, Big Data, Health Care Sector, Humans, Sustainable Development
dc.subject database, environmental economics, environmental impact, fuzzy mathematics, health care, management practice, policy approach, sustainability, sustainable development, theoretical study, Article, artificial neural network, data analysis, economic aspect, energy, health care cost, health care delivery, health care system, machine learning, mathematical model, medical education, medical informatics, network analysis, nonhuman, quality of life, solid waste management, human, Big Data, Health Care Sector, Humans, Sustainable Development
dc.title Big data-enabled solutions framework to overcoming the barriers to circular economy initiatives in healthcare sector
dc.type Article
dspace.entity.type Publication
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gdc.bip.influenceclass C4
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gdc.description.startpage 7513
gdc.description.volume 18
gdc.identifier.openalex W3178352173
gdc.identifier.pmid 34299964
gdc.index.type Scopus
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gdc.oaire.keywords Big Data
gdc.oaire.keywords fuzzy VIKOR
gdc.oaire.keywords sustainable development
gdc.oaire.keywords dewey650
gdc.oaire.keywords healthcare sector
gdc.oaire.keywords barriers
gdc.oaire.keywords circular economy
gdc.oaire.keywords Health Care Sector
gdc.oaire.keywords waste issues
gdc.oaire.keywords Sustainable Development
gdc.oaire.keywords Article
gdc.oaire.keywords big data tools
gdc.oaire.keywords Humans
gdc.oaire.keywords fuzzy best-worst
gdc.oaire.popularity 3.6990794E-8
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gdc.oaire.sciencefields 05 social sciences
gdc.oaire.sciencefields 03 medical and health sciences
gdc.oaire.sciencefields 0302 clinical medicine
gdc.oaire.sciencefields 0502 economics and business
gdc.openalex.collaboration International
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gdc.opencitations.count 42
gdc.plumx.crossrefcites 46
gdc.plumx.mendeley 252
gdc.plumx.pubmedcites 4
gdc.plumx.scopuscites 46
person.identifier.scopus-author-id Kazancoglu- Yigit (15848066400), Saǧnak- Muhittin (55505122500), Lafci- Çisem (57225174550), Luthra- Sunil (43361407000), Kumar- Anil (57001679600), Taçoğlu- Caner (57193696292)
publicationissue.issueNumber 14
publicationvolume.volumeNumber 18
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