Evidence map›Paper›PMID 42104356›Full record

ArticleBMC health services research2026

Exploring cross-city hospital visiting trips for medical service evaluation in urban agglomeration areas based on automobile navigation data.

Xi Yu, Zhengdong Huang, Renzhong Guo, Jizhe Xia, Sa Ma

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Article in BMC health services research, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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5 · Who and what money

Authors and funding

5 authors.

Xi YuResearch Institute for Smart Cities, School of Architecture and Urban Planning, Shenzhen University, Shenzhen, 518060, China.ORCID http://orcid.org/0000-0002-1962-6500
Zhengdong HuangResearch Institute for Smart Cities, School of Architecture and Urban Planning, Shenzhen University, Shenzhen, 518060, China. zdhuang@szu.edu.cn.ORCID http://orcid.org/0000-0001-7264-4567
Renzhong GuoResearch Institute for Smart Cities, School of Architecture and Urban Planning, Shenzhen University, Shenzhen, 518060, China.
Jizhe XiaResearch Institute for Smart Cities, School of Architecture and Urban Planning, Shenzhen University, Shenzhen, 518060, China.
Sa MaBusiness College, Southwest University, Chongqing, 400715, China.

Funding

National Natural Science Foundation of China 42501254Shenzhen Science and Technology Programs 20220809112159001Shenzhen Science and Technology Programs ZDSYS20210623101800001
6 · The paper itself

Abstract

backgroundUnderstanding the supply-demand relationship of medical services is essential for regional planning. Existing city-scale studies typically exclude cross-city flows, whereas national-scale studies often overlook intra-city heterogeneity. In urban agglomerations, healthcare resources and transport infrastructure are usually planned by cities, although patients may cross city boundaries to seek care. The implications of cross-city trips for regional medical services remain insufficiently understood. Taking the Pearl River Delta as a case, this study investigates cross-city hospital visiting trips and their implications for medical service evaluation.

methodsUsing 91.2 million automobile navigation records collected in 2019, 1.37 million hospital visiting trips to Grade 3 hospitals were identified through a modified spatial join method. A population-hospital bipartite network and a multi-scale analytical framework were constructed. Cross-city demand and supply indices were developed at the city, subdistrict, and hospital scales to characterize cross-city medical service patterns and influencing factors. Accessibility and Gini coefficients were computed under intra-city and regional evaluation scenarios to assess how incorporating cross-city hospital visiting trips affects medical service evaluation.

resultsBased on automobile navigation data, 9.1% of identified hospital visiting trips crossed city boundaries. Guangzhou and Shenzhen served as dominant regional suppliers, with cross-city supply indices of 55.9% and 21.8%, respectively. Cross-city demand was negatively associated with distance to boundary, GDP per capita, and hospital beds. Cross-city service share was negatively associated with distance to boundary, whereas contributions to regional cross-city service provision were positively associated with hospital size and hospital grade. Incorporating cross-city flows increased accessibility in most peripheral areas and reduced the regional population-weighted Gini coefficient from 0.596 to 0.522.

conclusionsBased on automobile navigation data, cross-city hospital visiting trips constitute an important component of medical service utilization in urban agglomerations. At the subdistrict scale, cross-city demand was jointly associated with boundary proximity and local economic and medical conditions. At the hospital scale, the cross-city service share was higher among hospitals closer to city boundaries, whereas contributions to regional cross-city medical service provision were greater among larger and higher-grade hospitals. Evaluation frameworks relying solely on intra-city data tend to underestimate accessibility in boundary areas and, in most cases, overestimate the Gini coefficient.

Indexed as

Health Services AccessibilityTravelChinaCitiesHumansCross-city hospital visiting tripsMedical service evaluationPearl River DeltaPopulation–hospital networkSpatial accessibilityUrban agglomeration

Identifiers

PMID42104356
PMCPMC13326144

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