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Advancing the Population Needs-Based Health Workforce Planning Methodology: A Simulation Tool for Country Application
Although the conceptual underpinnings of needs-based health workforce planning have developed over the last two decades, lingering gaps in empirical models and lack of open access tools have partly constrained its uptake in health workforce planning processes in countries. This paper presents an adv...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7926568/ https://www.ncbi.nlm.nih.gov/pubmed/33671553 http://dx.doi.org/10.3390/ijerph18042113 |
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author | Asamani, James Avoka Christmals, Christmal Dela Reitsma, Gerda Marie |
author_facet | Asamani, James Avoka Christmals, Christmal Dela Reitsma, Gerda Marie |
author_sort | Asamani, James Avoka |
collection | PubMed |
description | Although the conceptual underpinnings of needs-based health workforce planning have developed over the last two decades, lingering gaps in empirical models and lack of open access tools have partly constrained its uptake in health workforce planning processes in countries. This paper presents an advanced empirical framework for the need-based approach to health workforce planning with an open-access simulation tool in Microsoft(®) Excel to facilitate real-life health workforce planning in countries. Two fundamental mathematical models are used to quantify the supply of, and need for, health professionals, respectively. The supply-side model is based on a stock-and-flow process, and the need-side model extents a previously published analytical frameworks using the population health needs-based approach. We integrate the supply and need analyses by comparing them to establish the gaps in both absolute and relative terms, and then explore their cost implications for health workforce policy and strategy. To illustrate its use, the model was used to simulate a real-life example using midwives and obstetricians/gynaecologists in the context of maternal and new-born care in Ghana. Sensitivity analysis showed that if a constant level of health was assumed (as in previous works), the need for health professionals could have been underestimated in the long-term. Towards universal health coverage, the findings reveal a need to adopt the need-based approach for HWF planning and to adjust HWF supply in line with population health needs. |
format | Online Article Text |
id | pubmed-7926568 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-79265682021-03-04 Advancing the Population Needs-Based Health Workforce Planning Methodology: A Simulation Tool for Country Application Asamani, James Avoka Christmals, Christmal Dela Reitsma, Gerda Marie Int J Environ Res Public Health Article Although the conceptual underpinnings of needs-based health workforce planning have developed over the last two decades, lingering gaps in empirical models and lack of open access tools have partly constrained its uptake in health workforce planning processes in countries. This paper presents an advanced empirical framework for the need-based approach to health workforce planning with an open-access simulation tool in Microsoft(®) Excel to facilitate real-life health workforce planning in countries. Two fundamental mathematical models are used to quantify the supply of, and need for, health professionals, respectively. The supply-side model is based on a stock-and-flow process, and the need-side model extents a previously published analytical frameworks using the population health needs-based approach. We integrate the supply and need analyses by comparing them to establish the gaps in both absolute and relative terms, and then explore their cost implications for health workforce policy and strategy. To illustrate its use, the model was used to simulate a real-life example using midwives and obstetricians/gynaecologists in the context of maternal and new-born care in Ghana. Sensitivity analysis showed that if a constant level of health was assumed (as in previous works), the need for health professionals could have been underestimated in the long-term. Towards universal health coverage, the findings reveal a need to adopt the need-based approach for HWF planning and to adjust HWF supply in line with population health needs. MDPI 2021-02-22 /pmc/articles/PMC7926568/ /pubmed/33671553 http://dx.doi.org/10.3390/ijerph18042113 Text en © 2021 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) ). |
spellingShingle | Article Asamani, James Avoka Christmals, Christmal Dela Reitsma, Gerda Marie Advancing the Population Needs-Based Health Workforce Planning Methodology: A Simulation Tool for Country Application |
title | Advancing the Population Needs-Based Health Workforce Planning Methodology: A Simulation Tool for Country Application |
title_full | Advancing the Population Needs-Based Health Workforce Planning Methodology: A Simulation Tool for Country Application |
title_fullStr | Advancing the Population Needs-Based Health Workforce Planning Methodology: A Simulation Tool for Country Application |
title_full_unstemmed | Advancing the Population Needs-Based Health Workforce Planning Methodology: A Simulation Tool for Country Application |
title_short | Advancing the Population Needs-Based Health Workforce Planning Methodology: A Simulation Tool for Country Application |
title_sort | advancing the population needs-based health workforce planning methodology: a simulation tool for country application |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7926568/ https://www.ncbi.nlm.nih.gov/pubmed/33671553 http://dx.doi.org/10.3390/ijerph18042113 |
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