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Forecasting hospital outpatient volume using an optimized medical two-stage hybrid grey model

Youyang Ren (Shool of Business, Jiangnan University, Wuxi, China)
Yuhong Wang (Shool of Business, Jiangnan University, Wuxi, China)
Lin Xia (Shool of Business, Jiangnan University, Wuxi, China)
Wei Liu (Henan University of Technology, Zhengzhou, China)
Ran Tao (Wuxi Hospital of Traditional Chinese Medicine, Wuxi, China)

Grey Systems: Theory and Application

ISSN: 2043-9377

Article publication date: 30 May 2024

Issue publication date: 24 September 2024

71

Abstract

Purpose

Forecasting outpatient volume during a significant security crisis can provide reasonable decision-making references for hospital managers to prevent sudden outbreaks and dispatch medical resources on time. Based on the background of standard hospital operation and Coronavirus disease (COVID-19) periods, this paper constructs a hybrid grey model to forecast the outpatient volume to provide foresight decision support for hospital decision-makers.

Design/methodology/approach

This paper proposes an improved hybrid grey model for two stages. In the non-COVID-19 stage, the Aquila Optimizer (AO) is selected to optimize the modeling parameters. Fourier correction is applied to revise the stochastic disturbance. In the COVID-19 stage, this model adds the COVID-19 impact factor to improve the grey model forecasting results based on the dummy variables. The cycle of the dummy variables modifies the COVID-19 factor.

Findings

This paper tests the hybrid grey model on a large Chinese hospital in Jiangsu. The fitting MAPE is 2.48%, and the RMSE is 16463.69 in the training group. The test MAPE is 1.91%, and the RMSE is 9354.93 in the test group. The results of both groups are better than those of the comparative models.

Originality/value

The two-stage hybrid grey model can solve traditional hospitals' seasonal outpatient volume forecasting and provide future policy formulation references for sudden large-scale epidemics.

Keywords

Acknowledgements

The research reported was partially funded by the National Natural Science Foundation of China (No: 71871106); National Social Science Fund later funded projects (No: 23FGLB051); the Fundamental Research Funds for the Central Universities (Nos: JUSRP1809ZD; 2019JDZD06; JUSRP321016); the Major Projects of Philosophy and Social Science Research of Guizhou Province (No: 21GZZB32); Project of Chinese Academic Degrees and Graduate Education (No: 2020ZDB2); Major research project of the 14th Five-Year Plan for Higher Education Scientific Research of Jiangsu Higher Education Association (No: ZDGG02); 2021 Wuxi Science and Technology Association key topics (No: KX-21-C025); Special Research Project on Education Digitalization by the Ministry of Education (No: CSDP24LF1G402); Key Project in Philosophy and Social Science of Wuxi (No: WXSK24-A-06); International Joint Research Laboratory for Artificial Forecasting and Decision Making Optimization at Jiangnan University.

Citation

Ren, Y., Wang, Y., Xia, L., Liu, W. and Tao, R. (2024), "Forecasting hospital outpatient volume using an optimized medical two-stage hybrid grey model", Grey Systems: Theory and Application, Vol. 14 No. 4, pp. 671-707. https://doi.org/10.1108/GS-01-2024-0005

Publisher

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Emerald Publishing Limited

Copyright © 2024, Emerald Publishing Limited

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