Predicting Demand for Emergency Ambulance Services: A Comparative Approach

Tanissara  Butsingkorn; Arthit  Apichottanakul; Sirawadee  Arunyanart

doi:10.6180/jase.202410_27(10).0011

Predicting Demand for Emergency Ambulance Services: A Comparative Approach

Tanissara Butsingkorn¹, Arthit Apichottanakul^1,2, and Sirawadee Arunyanart¹This email address is being protected from spambots. You need JavaScript enabled to view it.

¹Supply Chain and Logistics System Research Unit, Department of Industrial Engineering, Faculty of Engineering, Khon Kaen University, Khon Kaen 40002, Thailand

²Department of Production System Technology and Industrial Management, Faculty of Technology, Khon Kaen University

Received: October 19, 2023
Accepted: November 17, 2023
Publication Date: January 4, 2024

Copyright The Author(s). This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are cited.

Download Citation: ||https://doi.org/10.6180/jase.202410_27(10).0011

Accurate forecasting of demand for emergency medical services (EMS) is crucial for effective healthcare management, contributing to improved response times and cost control during emergencies. Additionally, it facilitates resource allocation and the implementation of knowledge-based policies, ultimately enhancing patient care and services. This study focuses on forecasting EMS demand related to patient transportation from 25 sub-hospitals in Khon Kaen, Thailand, to the central medical center hospital for the purpose of receiving necessary medical treatment. To improve the precision of demand forecasting, we evaluated various forecasting approaches. The results indicate that ANN outperforms other models. This can be attributed to the ANN’s ability to identify complex relationships and efficiently learn from observed data through nonlinear mapping. These findings underscore the potential applications of the ANN model for addressing this problem.

Keywords: Machine learning, artificial neural networks, autoregressive integrated moving averages, simple moving

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