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Optimization of triage time and sample delivery path in health infrastructure to combat COVID-19

Cheng Zhou (School of Civil and Hydraulic Engineering, Huazhong University of Science and Technology, Wuhan, China) (Hubei Engineering Research Center for Virtual, Safe and Automated Construction (ViSAC), Wuhan, China)
Rao Li (School of Civil and Hydraulic Engineering, Huazhong University of Science and Technology, Wuhan, China) (Hubei Engineering Research Center for Virtual, Safe and Automated Construction (ViSAC), Wuhan, China)
Xiaoju Xiong (Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China)
Jie Li (School of Nursing, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China)
Yuyue Gao (School of Civil and Hydraulic Engineering, Huazhong University of Science and Technology, Wuhan, China) (Hubei Engineering Research Center for Virtual, Safe and Automated Construction (ViSAC), Wuhan, China)

Engineering, Construction and Architectural Management

ISSN: 0969-9988

Article publication date: 1 March 2022

Issue publication date: 1 September 2023

178

Abstract

Purpose

This study presented the experience of improving the nucleic acid sample collection and transportation service in response to the epidemic. The main purpose is that through intelligent path planning, combined with the time scheduling of sample points, the process of obtaining results to determine the state of COVID-19 patients could be speeding up.

Design/methodology/approach

The research optimized the process, including finding an optimal path to traverse all sample points in the hospital area via intelligent path planning method and standardizing the operation through the time sequence scheduling of each round of support staff to collect and send samples in the hospital area, so as to ensure the shortest time in each round. And the study examines these real-time experiments through retrospective examination.

Findings

The real-time experiments' data showed that the proposed path planning and scheduling model could provide a reliable reference for improving the efficiency of hospital logistics. Testing is a very important part of diagnosis and prompt results are essential. It shows the possibility of applying the shortest-path algorithms to optimize sample collection processes in the hospital and presents the case study that gives the expected outcomes of such a process.

Originality/value

The value of the study lies in the abstraction of a very practical and urgent problem into a TSP. Combining the ant colony algorithm with the genetic algorithm (ACAGA), the performance of path planning is improved. Under the intervention and guidance, the efficiency of hospital regional logistics planning was greatly improved, which may be of greater benefit to critical patients who must go through fever clinic during the epidemic. By detailing how to more rapidly obtain results through engineering method, the paper contributes ideas and plans for practitioners to use. The experience and lessons learned from Tongji Hospital are expected to provide guidance for supporting service measures in national public health infrastructure management and valuable reference for the development of hospitals in other countries or regions.

Keywords

Acknowledgements

This research is supported in part by the National Natural Science Foundation of China (72171092, 71732001, 71821001), Natural Science Fund for Distinguished Young Scholars of Hubei Province (2021CFA091) and the Major Science and Technology Project of Hubei (No. 2020ACA006). The authors would like to thank those medical staff for providing valuable information for this study.

Citation

Zhou, C., Li, R., Xiong, X., Li, J. and Gao, Y. (2023), "Optimization of triage time and sample delivery path in health infrastructure to combat COVID-19", Engineering, Construction and Architectural Management, Vol. 30 No. 8, pp. 3620-3644. https://doi.org/10.1108/ECAM-10-2021-0877

Publisher

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Emerald Publishing Limited

Copyright © 2022, Emerald Publishing Limited

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