Social network analysis in China's hospital healthcare

Xuefan Dong, Daisheng Tang, Chengxiang Tang*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

7 Citations (Scopus)

Abstract

The objective of this paper is to identify whether the interorganization of healthcare providers in China had the global property of an oligopoly market tendencies by analyzing the rich-club phenomenon of patient flow. This paper examines the properties of patient transfer networks in an entire province of China using social network analysis. Hypertension is one of the most prevalent chronic conditions in China, thus the study examined the transfer network of patients with hypertension. The rich-club coefficient, the assortativity coefficient, and community detection were used to quantitatively illustrate the rich-club tendency of the social network. The results reveal significant rich-club characteristics and regional clustering patterns in this transfer network. First, the global rich-club coefficients calculated based on both degree and weighted degree are larger than 1, suggesting a relatively noticeable rich-club effect. Second, 269 nodes (69.33%) attach only to rich nodes, suggesting that most nodes within the hypertensive-patient transfer network are highly influenced by rich-club members. Third, community detection found a significant pattern of regional clustering in the transfer network. Our findings are the first to demonstrate that the hospital system operated as an oligopoly market, in which the “elite” providers formed an “inner circle”, and other providers were only loosely interconnected. It might be valuable to patients to transfer this oligarchical system into a more pluralistic network.

Original languageEnglish
Article number125546
Pages (from-to)1-13
Number of pages13
JournalPhysica A: Statistical Mechanics and its Applications
Volume565
DOIs
Publication statusPublished - 1 Mar 2021
Externally publishedYes

Keywords

  • China
  • Healthcare delivery
  • Hospital
  • Social networks

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