ArticleBMC health services research2026
Exploring cross-city hospital visiting trips for medical service evaluation in urban agglomeration areas based on automobile navigation data.
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.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
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
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.