Research on identification of key brittleness factors in emergency medical resources support system based on complex network

Benhong Peng; Jiaojiao Ge; Guo Wei; Anxia Wan

doi:10.1016/j.artmed.2022.102350

Research on identification of key brittleness factors in emergency medical resources support system based on complex network

Artif Intell Med. 2022 Sep:131:102350. doi: 10.1016/j.artmed.2022.102350. Epub 2022 Jul 11.

Authors

Benhong Peng¹, Jiaojiao Ge², Guo Wei³, Anxia Wan⁴

Affiliations

¹ School of Management Science and Engineering, Nanjing University of Information Science & Technology, Nanjing 210044, China. Electronic address: 002426@nuist.edu.cn.
² School of Management Science and Engineering, Nanjing University of Information Science & Technology, Nanjing 210044, China. Electronic address: 20201224007@nuist.edu.cn.
³ Department of Mathematics and Computer Science, University of North Carolina at Pembroke, Pembroke, NC 28372, USA. Electronic address: guo.wei@uncp.edu.
⁴ School of Management Science and Engineering, Nanjing University of Information Science & Technology, Nanjing 210044, China. Electronic address: 591567691@qq.com.

PMID: 36100341
DOI: 10.1016/j.artmed.2022.102350

Abstract

To address the problem of difficulty in identifying the key brittleness factors in the emergency medical resources support system (EMRSS), the principle of Interpretive Structural Modeling is utilized to construct a hierarchical recursive structural model for probing the influencing factors of the brittleness risk formation in the system. Further, by incorporating with the complex network theory, the network of the emergency medical resources support system has been built. The integrated value reflecting the importance of the network nodes is calculated by integrating the local nodes and the whole network to identify the critical factors that lead to the collapse of the emergency medical system. The validity of the method was verified by Advanced Research Project Agency network. The results showed that the key brittleness factors in EMRSS mainly include the professional competence of staff, the amount of government funding and the passing rate of quality sampling medical supplies in storage. It is hoped that this study provides useful information for reducing the brittleness risk and ensuring the safe operation of the system.

Keywords: Complex network; Emergency support; Factor identification; Interpretive structural modeling; Medical resources.