Evidence map›Paper›PMID 41889617›Full record

ArticleFrontiers in public health2026

Supply-demand governance of hierarchical healthcare systems: mobile big data unveils non-random patient flow patterns and the bypass premium in cities.

Qing Guo, Hengna Ren, Xinmiao Shao

Abstract read
In one paragraph

Article in Frontiers in public health, 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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0citing papers in PubMed
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1 · What the graph read from it

What it found

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2 · The registry

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3 · Its place in the literature

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0 citing papers in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

3 authors.

Qing GuoBusiness School, University of Shanghai for Science and Technology, Shanghai, China.
Hengna RenBusiness School, University of Shanghai for Science and Technology, Shanghai, China.
Xinmiao ShaoBusiness School, University of Shanghai for Science and Technology, Shanghai, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: The uneven distribution of healthcare resources and jobs-housing spatial separation are reshaping the spatiotemporal patterns of urban patient flows. This structural mismatch exacerbates inequalities in service utilization and imposes hidden geographic and social costs. However, conventional static statistics and theoretical models often fail to capture authentic micro-level behavioral patterns, rendering them unable to precisely quantify or deconstruct the inequalities and burdens concealed within patient flows. Methods: Taking Shanghai as a representative megacity case study, we utilized anonymized mobile signaling data (March 2019) to construct a weighted, directed "demand-supply" patient flows network. We introduced a null model as a random benchmark and employed the channel decomposition method to deconstruct pathway structures. We developed a "bypass premium" index to quantify the specific burden of quality-driven hospital seeking. Results: (1) Resource siphoning: Patient flows are highly concentrated toward top-tier hospitals, yet their spatial footprint is widely dispersed across the city, a pattern that deviates significantly from the random benchmark. (2) Boundary filtering: Administrative boundaries act as a "value filter." Inter-district flows do not diffuse uniformly but are funneled into backbone pathways leading exclusively to tertiary hospitals. (3) Functional neutrality: Secondary hospitals fail to perform their intended hub-and-diversion function within the hierarchical healthcare system, resulting in a state of functional neutrality. (4) Cost deconstruction: The average bypass premium for reaching a tertiary hospital is 10.24 km. Crucially, 73.54% (7.53 km) of this constitutes passive structural friction required to overcome boundary barriers, while only 26.46% (2.71 km) represents the active selective premium paid for quality-driven access. Conclusion: This study confirms the non-random polarization of patient flows and the screening mechanism of administrative boundaries in Shanghai. Our findings reveal that the costs of inter-district hospital-seeking stem primarily from passive structural friction rather than active selective premiums, occurring alongside a critical functional deficit in secondary hospitals. Consequently, policy interventions must prioritize strategies of "reducing friction" and "strengthening the middle." Specifically, optimizing transportation networks, insurance integration, and medical consortiums is essential to dismantle barriers and revitalize the hub capacity of the intermediate tier.

Indexed as

Big DataDelivery of Health CareHealth Services Needs and DemandChinaCitiesHumansbypass premiumhierarchical healthcare systemmobile signaling datanull modelpatient flow networkspatial equity

Identifiers

PMID41889617
PMCPMC13013443

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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.