Demand analysis and capacity management for hospital emergencies using advanced forecasting models and stochastic simulation
Author
dc.contributor.author
Barros Vera, Oscar
Author
dc.contributor.author
Weber, Richard
Author
dc.contributor.author
Reveco, Carlos
Admission date
dc.date.accessioned
2022-05-16T16:02:46Z
Available date
dc.date.available
2022-05-16T16:02:46Z
Publication date
dc.date.issued
2021
Cita de ítem
dc.identifier.citation
Operations Research Perspectives 8 (2021) 100208
es_ES
Identifier
dc.identifier.other
/10.1016/j.orp.2021.100208
Identifier
dc.identifier.uri
https://repositorio.uchile.cl/handle/2250/185533
Abstract
dc.description.abstract
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.
es_ES
Patrocinador
dc.description.sponsorship
ANID PIA/APOYO AFB180003
es_ES
Lenguage
dc.language.iso
en
es_ES
Publisher
dc.publisher
Elsevier
es_ES
Type of license
dc.rights
Attribution-NonCommercial-NoDerivs 3.0 United States