Application of a sampling and clustering-based heuristic search algorithm to find an efficient staff configuration in an emergency department
Harita Rascon, María de los Ángeles (Universitat Autònoma de Barcelona. Departament d'Arquitectura de Computadors i Sistemes Operatius)
Wong, Álvaro (Universitat Autònoma de Barcelona. Departament d'Arquitectura de Computadors i Sistemes Operatius)
Rexachs del Rosario, Dolores Isabel (Universitat Autònoma de Barcelona. Departament d'Arquitectura de Computadors i Sistemes Operatius)
Luque, Emilio (Universitat Autònoma de Barcelona. Departament d'Arquitectura de Computadors i Sistemes Operatius)
Bruballa, Eva (Escola Universitària d'Infermeria i de Fisioteràpia "Gimbernat")
Epelde, Francisco (Parc Taulí Hospital Universitari. Institut d'Investigació i Innovació Parc Taulí (I3PT))

Date:	2025
Description:	12 pàg.
Abstract:	Emergency Departments (EDs) are among the most complex areas in healthcare, requiring immediate medical attention for acute and urgent conditions. Optimizing staff configurations to reduce patient Length of Stay (LoS) and improve operational efficiency poses a significant challenge due to the combinatorial and high-dimensional nature of the problem. To identify the most effective staff configuration, we propose a heuristic optimization strategy that is based on the Montecarlo Clustering Search Algorithm (MCSA), which efficiently explores the multidimensional solution space. MCSA leverages an agent-based simulation (ABM) model that evaluates each proposed staff configuration under realistic operational conditions, providing Key Performance Indicator (KPI) feedback values related to each proposed staff configuration. Through this strategy, we explore staff configurations capable of handling patient volumes with varying acuity levels in an ED to optimize the LoS KPI. Results demonstrate that our methodology is capable to find a solution as a staff configuration that reduces LoS compared to a baseline, offering a computationally efficient and practical tool for decision-makers. We identified solutions by exploring less than 1% of the total search space, demonstrating the efficiency of the proposed approach in addressing complex optimization problems. This approach supports informed planning in healthcare environments while maintaining system feasibility and scalability.
Grants:	Agencia Estatal de Investigación PID2020-112496GB-I00 Agencia Estatal de Investigación PID2023-147955NB-I00
Note:	Altres ajuts: acords transformatius de la UAB
Rights:	Aquest document està subjecte a una llicència d'ús Creative Commons. Es permet la reproducció total o parcial, la distribució, la comunicació pública de l'obra i la creació d'obres derivades, fins i tot amb finalitats comercials, sempre i quan es reconegui l'autoria de l'obra original.
Language:	Anglès
Document:	Article ; recerca ; Versió publicada
Subject:	Agent-based simulation ; Classification algorithms ; Emergency departments ; Heuristic methods ; Optimization
Published in:	Expert Systems with Applications, Vol. 295 (2025) , p. 128803, ISSN 0957-4174

DOI: 10.1016/j.eswa.2025.128803

12 p, 3.9 MB

The record appears in these collections:
Research literature > UAB research groups literature > Research Centres and Groups (research output) > Engineering > HPC4EAS (High Performance Computing for Efficient Applications and Simulation Research Group)
Research literature > UAB research groups literature > Research Centres and Groups (research output) > Health sciences and biosciences > Parc Taulí Research and Innovation Institute (I3PT
Articles > Research articles
Articles > Published articles