Intelligent selection of healthcare supply chain mode - an applied research based on artificial intelligence

Ping Long; Lin Lu; Qianlan Chen; Yifan Chen; Chaoling Li; Xiaochun Luo

doi:10.3389/fpubh.2023.1310016

Intelligent selection of healthcare supply chain mode - an applied research based on artificial intelligence

Front Public Health. 2023 Dec 11:11:1310016. doi: 10.3389/fpubh.2023.1310016. eCollection 2023.

Authors

Ping Long¹, Lin Lu¹, Qianlan Chen¹, Yifan Chen², Chaoling Li¹, Xiaochun Luo^{1

3}

Affiliations

¹ School of Economics and Management, Guangxi Normal University, Guilin, China.
² Adam Smith Business School, University of Glasgow, Scotland, United Kingdom.
³ School of Economics and Management, Nanjing University of Aeronautics and Astronautics, Nanjing, China.

Abstract

Introduction: Due to the inefficiency and high cost of the current healthcare supply chain mode, in order to adapt to the great changes in the global economy and public health, it is urgent to choose an effective mode for sustainable development of healthcare supply chain. The aim of this paper is to use artificial intelligence systems to make intelligent decisions for healthcare supply chain mode selection.

Methods: Firstly, according to the economic benefits, social benefits and environmental benefits of healthcare supply chain, this paper identifies different healthcare supply chain modes in combination with artificial intelligence technology. Secondly, this paper presents the intelligent choice optimization method of healthcare supply chain mode based on deep reinforcement learning algorithm. Finally, the effect of artificial intelligence in healthcare supply chain mode selection is verified by simulation experiment.

Results and discussion: The experimental results show that healthcare supply chain mode selected by artificial intelligence is basically consistent with the target mode, while healthcare supply chain mode selected by the basic selection method, BP neural network method and big data method is different from the target mode, which indicates that AI has more advantages in the selection of medical supply chain mode. Therefore, we recommend the application of artificial intelligence to healthcare supply chain management. This study not only makes up for the ineffective problems of existing methods, but also makes up for the gaps in the application of AI technology in the field of healthcare supply chain. The scientific value of this paper is that the proposed framework and the artificial intelligence algorithm enrich the relevant theories of healthcare supply chain research and provide methodological guidance for intelligent decision-making of healthcare supply chain. At the same time, for medical enterprises, this research provides a new practical guideline for the application of artificial intelligence in the sustainable development and modern management of healthcare supply chain.

Keywords: artificial intelligence; deep reinforcement learning algorithms; healthcare supply chain; intelligent selection; mode selection.

MeSH terms

Algorithms*
Artificial Intelligence*
Computer Simulation
Neural Networks, Computer
Research

Grants and funding

The author(s) declare financial support was received for the research, authorship, and/or publication of this article. This work was supported by Guangxi Key R&D Plan (Project No: 2022AB34029): The Key Technologies and Industrialization of Intelligent Traceability in the Whole Link of High-quality Seedling Seed Supply Chain in Lijiang River Basin and by the Research Fund Project of Development Institute of Zhujiang-Xijiang Economic Zone, Key Research Base of Humanities and Social Sciences in Guangxi Universities (Project No: ZX2023051) and by the Innovation Project of Guangxi Graduate Education (JG2023018).