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How to do (or not to do)… health resource allocations using constrained mathematical optimization
Despite the push towards evidence-based health policy, decisions about how to allocate health resources are all too often made on the basis of political forces or a continuation of the status quo. This results in wastage in health systems and loss of potential population health. However, if health s...
Autores principales: | , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Oxford University Press
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9825717/ https://www.ncbi.nlm.nih.gov/pubmed/36398991 http://dx.doi.org/10.1093/heapol/czac096 |
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author | Stuart, Robyn M Fraser-Hurt, Nicole Shubber, Zara Vu, Lung Cheik, Nejma Kerr, Cliff C Wilson, David P |
author_facet | Stuart, Robyn M Fraser-Hurt, Nicole Shubber, Zara Vu, Lung Cheik, Nejma Kerr, Cliff C Wilson, David P |
author_sort | Stuart, Robyn M |
collection | PubMed |
description | Despite the push towards evidence-based health policy, decisions about how to allocate health resources are all too often made on the basis of political forces or a continuation of the status quo. This results in wastage in health systems and loss of potential population health. However, if health systems are to serve people best, then they must operate efficiently and equitably, and appropriate valuation methods are needed to determine how to do this. With the advances in computing power over the past few decades, advanced mathematical optimization algorithms can now be run on personal computers and can be used to provide comprehensive, evidence-based recommendations for policymakers on how to prioritize health spending considering policy objectives, interactions of interventions, real-world system constraints and budget envelopes. Such methods provide an invaluable complement to traditional or extended cost-effectiveness analyses or league tables. In this paper, we describe how such methods work, how policymakers and programme managers can access them and implement their recommendations and how they have changed health spending in the world to date. |
format | Online Article Text |
id | pubmed-9825717 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Oxford University Press |
record_format | MEDLINE/PubMed |
spelling | pubmed-98257172023-01-10 How to do (or not to do)… health resource allocations using constrained mathematical optimization Stuart, Robyn M Fraser-Hurt, Nicole Shubber, Zara Vu, Lung Cheik, Nejma Kerr, Cliff C Wilson, David P Health Policy Plan How to Do (Or Not to Do) Despite the push towards evidence-based health policy, decisions about how to allocate health resources are all too often made on the basis of political forces or a continuation of the status quo. This results in wastage in health systems and loss of potential population health. However, if health systems are to serve people best, then they must operate efficiently and equitably, and appropriate valuation methods are needed to determine how to do this. With the advances in computing power over the past few decades, advanced mathematical optimization algorithms can now be run on personal computers and can be used to provide comprehensive, evidence-based recommendations for policymakers on how to prioritize health spending considering policy objectives, interactions of interventions, real-world system constraints and budget envelopes. Such methods provide an invaluable complement to traditional or extended cost-effectiveness analyses or league tables. In this paper, we describe how such methods work, how policymakers and programme managers can access them and implement their recommendations and how they have changed health spending in the world to date. Oxford University Press 2022-11-18 /pmc/articles/PMC9825717/ /pubmed/36398991 http://dx.doi.org/10.1093/heapol/czac096 Text en © The Author(s) 2022. Published by Oxford University Press in association with The London School of Hygiene and Tropical Medicine. https://creativecommons.org/licenses/by/4.0/This is an Open Access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | How to Do (Or Not to Do) Stuart, Robyn M Fraser-Hurt, Nicole Shubber, Zara Vu, Lung Cheik, Nejma Kerr, Cliff C Wilson, David P How to do (or not to do)… health resource allocations using constrained mathematical optimization |
title | How to do (or not to do)… health resource allocations using constrained mathematical optimization |
title_full | How to do (or not to do)… health resource allocations using constrained mathematical optimization |
title_fullStr | How to do (or not to do)… health resource allocations using constrained mathematical optimization |
title_full_unstemmed | How to do (or not to do)… health resource allocations using constrained mathematical optimization |
title_short | How to do (or not to do)… health resource allocations using constrained mathematical optimization |
title_sort | how to do (or not to do)… health resource allocations using constrained mathematical optimization |
topic | How to Do (Or Not to Do) |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9825717/ https://www.ncbi.nlm.nih.gov/pubmed/36398991 http://dx.doi.org/10.1093/heapol/czac096 |
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