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http://hdl.handle.net/10071/22053
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Title: Enhancing optimization planning models for health human resources management with foresight
Authors: Amorim-Lopes, M.
Oliveira, M. D.
Raposo, M.
Cardoso-Grilo, T.
Alvarenga, A.
Barbas, M.
Alves, M.
Vieira, A.
Barbosa-Póvoa, A.
Keywords: Mathematical programming
Foresight
Scenario planning
Uncertainty modelling
Health human resources
Planning
Issue Date: 2021
Publisher: Elsevier
Abstract: Achieving a balanced healthcare workforce requires health planners to adjust the supply of health human resources (HHR). Mathematical programming models have been widely used to assist such planning, but the way uncertainty is usually considered in these models entails methodological and practical issues and often disregards radical yet plausible changes to the future. This study proposes a new socio-technical methodology to factor in uncertainty over the future within mathematical programming modelling. The methodological approach makes use of foresight and scenario planning concepts to build tailor-made scenarios and scenario fit input parameters, which are then used within mathematical programming models. Health stakeholders and experts are engaged in the scenario building process. Causal map modelling and morphological analysis are adopted to digest stakeholders and experts’ information about the future and give origin to contrasting and meaningful scenarios describing plausible future. These scenarios are then adjusted and validated by stakeholders and experts, who then elicit their best quantitative estimates for coherent combinations of input parameters for the mathematical programming model under each scenario. These sets of parameters for each scenario are then fed to the mathematical programming model to obtain optimal solutions that can be interpreted in light of the meaning of the scenario. The proposed methodology has been applied to a case study involving HHR planning in Portugal, but its scope far extends HHR planning, being especially suited for addressing strategic and policy planning problems that are sensitive to input parameters.
Peer reviewed: yes
URI: http://hdl.handle.net/10071/22053
DOI: 10.1016/j.omega.2020.102384
ISSN: 0305-0483
Ciência-IUL: https://ciencia.iscte-iul.pt/id/ci-pub-77659
Appears in Collections:BRU-RI - Artigos em revistas científicas internacionais com arbitragem científica

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