Managing low–acuity patients in an Emergency Department through simulation–based multiobjective optimization using a neural network metamodel

Marco Boresta (), Tommaso Giovannelli () and Massimo Roma ()
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Marco Boresta: Institute for System Analysis and Computer Science “A. Ruberti”, National Research Council of Italy
Tommaso Giovannelli: Lehigh University
Massimo Roma: SAPIENZA – University of Rome

Health Care Management Science, 2024, vol. 27, issue 3, No 6, 415-435

Abstract: Abstract This paper deals with Emergency Department (ED) fast-tracks for low-acuity patients, a strategy often adopted to reduce ED overcrowding. We focus on optimizing resource allocation in minor injuries units, which are the ED units that can treat low-acuity patients, with the aim of minimizing patient waiting times and ED operating costs. We formulate this problem as a general multiobjective simulation-based optimization problem where some of the objectives are expensive black-box functions that can only be evaluated through a time-consuming simulation. To efficiently solve this problem, we propose a metamodeling approach that uses an artificial neural network to replace a black-box objective function with a suitable model. This approach allows us to obtain a set of Pareto optimal points for the multiobjective problem we consider, from which decision-makers can select the most appropriate solutions for different situations. We present the results of computational experiments conducted on a real case study involving the ED of a large hospital in Italy. The results show the reliability and effectiveness of our proposed approach, compared to the standard approach based on derivative-free optimization.

Keywords: Emergency department fast-track; Discrete event simulation; Simulation-based optimization; Metamodel; Neural network; Multiobjective optimization; Machine learning (search for similar items in EconPapers)
Date: 2024
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DOI: 10.1007/s10729-024-09678-3

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