Development of a Semi-Automated Decision-Making Method for the Resilience of Urban Healthcare Systems in Crisis Situations
This study is dedicated to solving the problem of how urban healthcare systems function in crisis situations. Cases where crisis situations lead either to population migrations or to a rapid increase in demand for medical services are the focus. There are often cases of the overloading of medical st...
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| Format: | Article |
| Language: | English |
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MDPI AG
2025-01-01
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| Series: | Urban Science |
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| Online Access: | https://www.mdpi.com/2413-8851/9/1/15 |
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| author | Oksana Mulesa Vladimir Ondrejicka Oleksii Yehorchenkov Nataliia Yehorchenkova Lubomir Jamecny Marianna Marusynets |
| author_facet | Oksana Mulesa Vladimir Ondrejicka Oleksii Yehorchenkov Nataliia Yehorchenkova Lubomir Jamecny Marianna Marusynets |
| author_sort | Oksana Mulesa |
| collection | DOAJ |
| description | This study is dedicated to solving the problem of how urban healthcare systems function in crisis situations. Cases where crisis situations lead either to population migrations or to a rapid increase in demand for medical services are the focus. There are often cases of the overloading of medical staff within institutions or the entire healthcare system in the city itself during new situations for which there are no clearly developed response protocols, such as the COVID-19 epidemic or man-made disasters. These situations can lead to the uneven access of resources for the population. This study develops a semi-automated decision-making method combining Wald world analysis and fuzzy logic. The method optimizes resource allocation and determines the priority of medical care, and, as a result, reduces the burden on the healthcare system by integrating socio-demographic and medical data. The results of experimental verification confirmed the ability of the method to adapt to dynamic changes, increase the accuracy of decision-making, and reduce response time. Importantly, the proposed method allows for a more equitable and efficient distribution of resources in the context of urbanization and population density growth. |
| format | Article |
| id | doaj-art-a00bfe377e264788ad7af96facfc8321 |
| institution | Kabale University |
| issn | 2413-8851 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Urban Science |
| spelling | doaj-art-a00bfe377e264788ad7af96facfc83212025-01-24T13:51:36ZengMDPI AGUrban Science2413-88512025-01-01911510.3390/urbansci9010015Development of a Semi-Automated Decision-Making Method for the Resilience of Urban Healthcare Systems in Crisis SituationsOksana Mulesa0Vladimir Ondrejicka1Oleksii Yehorchenkov2Nataliia Yehorchenkova3Lubomir Jamecny4Marianna Marusynets5Department of Physics, Mathematics and Technologies, University of Presov, 080 01 Presov, SlovakiaSPECTRA Centre of Excellence EU, Slovak University of Technology in Bratislava, 812 43 Bratislava, SlovakiaSPECTRA Centre of Excellence EU, Slovak University of Technology in Bratislava, 812 43 Bratislava, SlovakiaSPECTRA Centre of Excellence EU, Slovak University of Technology in Bratislava, 812 43 Bratislava, SlovakiaSPECTRA Centre of Excellence EU, Slovak University of Technology in Bratislava, 812 43 Bratislava, SlovakiaTivadar Lehoczky Social Sciences Research Centre, Ferenc Rakoczi II Transcarpathian Hungarian College of Higher Education, 90201 Beregove, UkraineThis study is dedicated to solving the problem of how urban healthcare systems function in crisis situations. Cases where crisis situations lead either to population migrations or to a rapid increase in demand for medical services are the focus. There are often cases of the overloading of medical staff within institutions or the entire healthcare system in the city itself during new situations for which there are no clearly developed response protocols, such as the COVID-19 epidemic or man-made disasters. These situations can lead to the uneven access of resources for the population. This study develops a semi-automated decision-making method combining Wald world analysis and fuzzy logic. The method optimizes resource allocation and determines the priority of medical care, and, as a result, reduces the burden on the healthcare system by integrating socio-demographic and medical data. The results of experimental verification confirmed the ability of the method to adapt to dynamic changes, increase the accuracy of decision-making, and reduce response time. Importantly, the proposed method allows for a more equitable and efficient distribution of resources in the context of urbanization and population density growth.https://www.mdpi.com/2413-8851/9/1/15urbanizationfuzzy logichealthcare resiliencesemi-automated decision-makingresource optimizationcrisis management |
| spellingShingle | Oksana Mulesa Vladimir Ondrejicka Oleksii Yehorchenkov Nataliia Yehorchenkova Lubomir Jamecny Marianna Marusynets Development of a Semi-Automated Decision-Making Method for the Resilience of Urban Healthcare Systems in Crisis Situations Urban Science urbanization fuzzy logic healthcare resilience semi-automated decision-making resource optimization crisis management |
| title | Development of a Semi-Automated Decision-Making Method for the Resilience of Urban Healthcare Systems in Crisis Situations |
| title_full | Development of a Semi-Automated Decision-Making Method for the Resilience of Urban Healthcare Systems in Crisis Situations |
| title_fullStr | Development of a Semi-Automated Decision-Making Method for the Resilience of Urban Healthcare Systems in Crisis Situations |
| title_full_unstemmed | Development of a Semi-Automated Decision-Making Method for the Resilience of Urban Healthcare Systems in Crisis Situations |
| title_short | Development of a Semi-Automated Decision-Making Method for the Resilience of Urban Healthcare Systems in Crisis Situations |
| title_sort | development of a semi automated decision making method for the resilience of urban healthcare systems in crisis situations |
| topic | urbanization fuzzy logic healthcare resilience semi-automated decision-making resource optimization crisis management |
| url | https://www.mdpi.com/2413-8851/9/1/15 |
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