Demand analysis and capacity management for hospital emergencies using advanced forecasting models and stochastic simulation

Demand forecasting and capacity management are complicated tasks for emergency healthcare services due to the uncertainty, complex relationships, and high public exposure involved. Published research does not show integrated solutions to these tasks. Thus, the objective of this paper is to present r...

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Autores principales:	Oscar Barros, Richard Weber, Carlos Reveco
Formato:	article
Lenguaje:	EN
Publicado:	Elsevier 2021
Materias:	Health care management Emergency capacity management Forecasting models Process design Simulation Mathematics QA1-939
Acceso en línea:	https://doaj.org/article/06de887f71b345f594c79576d24ab36f
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spelling	oai:doaj.org-article:06de887f71b345f594c79576d24ab36f2021-12-02T05:01:43ZDemand analysis and capacity management for hospital emergencies using advanced forecasting models and stochastic simulation2214-716010.1016/j.orp.2021.100208https://doaj.org/article/06de887f71b345f594c79576d24ab36f2021-01-01T00:00:00Zhttp://www.sciencedirect.com/science/article/pii/S2214716021000257https://doaj.org/toc/2214-7160Demand forecasting and capacity management are complicated tasks for emergency healthcare services due to the uncertainty, complex relationships, and high public exposure involved. Published research does not show integrated solutions to these tasks. Thus, the objective of this paper is to present results from three hospitals that show the feasibility of routinely applying integrated forecasting and capacity management with advanced operations research tools.After testing several forecasting methods, neural networks and support vector regression provided the best results in terms of variance and accuracy. Based on this forecasting, a logic for managing hospital capacity was designed and implemented. This logic includes the comparison between the forecasted demand and the available medical resources and a stochastic simulation model to assess the performance of different configurations of facilities and resources. The logic also provides hospital managers with a decision tool for determining the number and distribution of medical resources on emergency services based on a cost/benefit analysis of resources and service improvement. Such results support the task of assigning doctors to different kinds of boxes, defining their work schedules, and considering additional doctors. The contribution of this paper consists of an integrated solution designed to implement the abovementioned logic. This solution combines forecasting, simulation for capacity management, process design, and IT support, facilitating the practical routine use of complex models. The integration explicitly considers a solution that also has adaptation capabilities to facilitate use under changing conditions.The solution is also general and admits adaptation and extension to other services. Thus, we have already performed similar work for ambulatory and surgical services.Oscar BarrosRichard WeberCarlos RevecoElsevierarticleHealth care managementEmergency capacity managementForecasting modelsProcess designSimulationMathematicsQA1-939ENOperations Research Perspectives, Vol 8, Iss , Pp 100208- (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Health care management Emergency capacity management Forecasting models Process design Simulation Mathematics QA1-939
spellingShingle	Health care management Emergency capacity management Forecasting models Process design Simulation Mathematics QA1-939 Oscar Barros Richard Weber Carlos Reveco Demand analysis and capacity management for hospital emergencies using advanced forecasting models and stochastic simulation
description	Demand forecasting and capacity management are complicated tasks for emergency healthcare services due to the uncertainty, complex relationships, and high public exposure involved. Published research does not show integrated solutions to these tasks. Thus, the objective of this paper is to present results from three hospitals that show the feasibility of routinely applying integrated forecasting and capacity management with advanced operations research tools.After testing several forecasting methods, neural networks and support vector regression provided the best results in terms of variance and accuracy. Based on this forecasting, a logic for managing hospital capacity was designed and implemented. This logic includes the comparison between the forecasted demand and the available medical resources and a stochastic simulation model to assess the performance of different configurations of facilities and resources. The logic also provides hospital managers with a decision tool for determining the number and distribution of medical resources on emergency services based on a cost/benefit analysis of resources and service improvement. Such results support the task of assigning doctors to different kinds of boxes, defining their work schedules, and considering additional doctors. The contribution of this paper consists of an integrated solution designed to implement the abovementioned logic. This solution combines forecasting, simulation for capacity management, process design, and IT support, facilitating the practical routine use of complex models. The integration explicitly considers a solution that also has adaptation capabilities to facilitate use under changing conditions.The solution is also general and admits adaptation and extension to other services. Thus, we have already performed similar work for ambulatory and surgical services.
format	article
author	Oscar Barros Richard Weber Carlos Reveco
author_facet	Oscar Barros Richard Weber Carlos Reveco
author_sort	Oscar Barros
title	Demand analysis and capacity management for hospital emergencies using advanced forecasting models and stochastic simulation
title_short	Demand analysis and capacity management for hospital emergencies using advanced forecasting models and stochastic simulation
title_full	Demand analysis and capacity management for hospital emergencies using advanced forecasting models and stochastic simulation
title_fullStr	Demand analysis and capacity management for hospital emergencies using advanced forecasting models and stochastic simulation
title_full_unstemmed	Demand analysis and capacity management for hospital emergencies using advanced forecasting models and stochastic simulation
title_sort	demand analysis and capacity management for hospital emergencies using advanced forecasting models and stochastic simulation
publisher	Elsevier
publishDate	2021
url	https://doaj.org/article/06de887f71b345f594c79576d24ab36f
work_keys_str_mv	AT oscarbarros demandanalysisandcapacitymanagementforhospitalemergenciesusingadvancedforecastingmodelsandstochasticsimulation AT richardweber demandanalysisandcapacitymanagementforhospitalemergenciesusingadvancedforecastingmodelsandstochasticsimulation AT carlosreveco demandanalysisandcapacitymanagementforhospitalemergenciesusingadvancedforecastingmodelsandstochasticsimulation
_version_	1718400751724658688

Demand analysis and capacity management for hospital emergencies using advanced forecasting models and stochastic simulation

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