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A DEA-Based Complexity of Needs Approach for Hospital Beds Evacuation during the COVID-19 Outbreak
Data envelopment analysis (DEA) is a powerful nonparametric engineering tool for estimating technical efficiency and production capacity of service units. Assuming an equally proportional change in the output/input ratio, we can estimate how many additional medical resource health service units woul...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Hindawi
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7528060/ https://www.ncbi.nlm.nih.gov/pubmed/33029339 http://dx.doi.org/10.1155/2020/8857553 |
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author | Nepomuceno, Thyago C. C. Silva, Wilka M. N. Nepomuceno, Késsia T. C. Barros, Isloana K. F. |
author_facet | Nepomuceno, Thyago C. C. Silva, Wilka M. N. Nepomuceno, Késsia T. C. Barros, Isloana K. F. |
author_sort | Nepomuceno, Thyago C. C. |
collection | PubMed |
description | Data envelopment analysis (DEA) is a powerful nonparametric engineering tool for estimating technical efficiency and production capacity of service units. Assuming an equally proportional change in the output/input ratio, we can estimate how many additional medical resource health service units would be required if the number of hospitalizations was expected to increase during an epidemic outbreak. This assessment proposes a two-step methodology for hospital beds vacancy and reallocation during the COVID-19 pandemic. The framework determines the production capacity of hospitals through data envelopment analysis and incorporates the complexity of needs in two categories for the reallocation of beds throughout the medical specialties. As a result, we have a set of inefficient healthcare units presenting less complex bed slacks to be reduced, that is, to be allocated for patients presenting with more severe conditions. The first results in this work, in collaboration with state and municipal administrations in Brazil, report 3772 beds feasible to be evacuated by 64% of the analyzed health units, of which more than 82% are moderate complexity evacuations. The proposed assessment and methodology can provide a direction for governments and policymakers to develop strategies based on a robust quantitative production capacity measure. |
format | Online Article Text |
id | pubmed-7528060 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | Hindawi |
record_format | MEDLINE/PubMed |
spelling | pubmed-75280602020-10-06 A DEA-Based Complexity of Needs Approach for Hospital Beds Evacuation during the COVID-19 Outbreak Nepomuceno, Thyago C. C. Silva, Wilka M. N. Nepomuceno, Késsia T. C. Barros, Isloana K. F. J Healthc Eng Research Article Data envelopment analysis (DEA) is a powerful nonparametric engineering tool for estimating technical efficiency and production capacity of service units. Assuming an equally proportional change in the output/input ratio, we can estimate how many additional medical resource health service units would be required if the number of hospitalizations was expected to increase during an epidemic outbreak. This assessment proposes a two-step methodology for hospital beds vacancy and reallocation during the COVID-19 pandemic. The framework determines the production capacity of hospitals through data envelopment analysis and incorporates the complexity of needs in two categories for the reallocation of beds throughout the medical specialties. As a result, we have a set of inefficient healthcare units presenting less complex bed slacks to be reduced, that is, to be allocated for patients presenting with more severe conditions. The first results in this work, in collaboration with state and municipal administrations in Brazil, report 3772 beds feasible to be evacuated by 64% of the analyzed health units, of which more than 82% are moderate complexity evacuations. The proposed assessment and methodology can provide a direction for governments and policymakers to develop strategies based on a robust quantitative production capacity measure. Hindawi 2020-09-30 /pmc/articles/PMC7528060/ /pubmed/33029339 http://dx.doi.org/10.1155/2020/8857553 Text en Copyright © 2020 Thyago C. C. Nepomuceno et al. https://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Research Article Nepomuceno, Thyago C. C. Silva, Wilka M. N. Nepomuceno, Késsia T. C. Barros, Isloana K. F. A DEA-Based Complexity of Needs Approach for Hospital Beds Evacuation during the COVID-19 Outbreak |
title | A DEA-Based Complexity of Needs Approach for Hospital Beds Evacuation during the COVID-19 Outbreak |
title_full | A DEA-Based Complexity of Needs Approach for Hospital Beds Evacuation during the COVID-19 Outbreak |
title_fullStr | A DEA-Based Complexity of Needs Approach for Hospital Beds Evacuation during the COVID-19 Outbreak |
title_full_unstemmed | A DEA-Based Complexity of Needs Approach for Hospital Beds Evacuation during the COVID-19 Outbreak |
title_short | A DEA-Based Complexity of Needs Approach for Hospital Beds Evacuation during the COVID-19 Outbreak |
title_sort | dea-based complexity of needs approach for hospital beds evacuation during the covid-19 outbreak |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7528060/ https://www.ncbi.nlm.nih.gov/pubmed/33029339 http://dx.doi.org/10.1155/2020/8857553 |
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