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<p>Amid China’s ongoing urban transformation, the rational spatial distribution of hospitals as critical public service infrastructure is central to promoting equity and improving healthcare accessibility. However, empirical evidence on how accessibility contributes to predicting hospital choice at the individual level—particularly through nonlinear distance effects—remains limited. Adopting a behavior-oriented perspective, this study examines hospital choice within a constrained setting, specifically focusing on inpatient decisions for respiratory conditions between two tertiary (Class 3&#xa0;A) hospitals in Wuhan that are geographically proximate and comparable in institutional attributes. Using inpatient records and a behavior-calibrated interpretation of spatial accessibility, we integrate explainable machine learning with geospatial and environmental data to identify distinct distance-response regimes governing patient decision-making, rather than assuming uniform distance decay. The results indicate that spatial distance shows the strongest association with hospital selection but operates through threshold-based behavioral switching, with marked transitions occurring at specific distance intervals. Embedding these behavior-derived distance thresholds into a Two-Step Floating Catchment Area (2SFCA) framework, the study refines conventional accessibility assessment by explicitly accounting for both population demand and hospital resource availability. Spatial analysis shows that greater medical resource abundance significantly attenuates distance sensitivity, highlighting the context-dependent nature of spatial friction in healthcare utilization. By linking individual choice behavior with accessibility modeling, this study advances a behavior-calibrated framework for assessing healthcare accessibility. The findings provide applied insights for optimizing medical facility planning and resource allocation, particularly in rapidly urbanizing contexts where conventional distance-based assumptions may misrepresent actual patterns of healthcare use.</p>

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Behavior-Calibrated Spatial Accessibility and Hospital Choice: Evidence from Paired Tertiary Hospital Inpatient Data for Respiratory Conditions in Wuhan

  • 期刊论文

  • 发表日期：

    2026年8月1日

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  • Jingjing Wang,
  • Hao Wu,
  • Yang Zhao

摘要

Amid China’s ongoing urban transformation, the rational spatial distribution of hospitals as critical public service infrastructure is central to promoting equity and improving healthcare accessibility. However, empirical evidence on how accessibility contributes to predicting hospital choice at the individual level—particularly through nonlinear distance effects—remains limited. Adopting a behavior-oriented perspective, this study examines hospital choice within a constrained setting, specifically focusing on inpatient decisions for respiratory conditions between two tertiary (Class 3 A) hospitals in Wuhan that are geographically proximate and comparable in institutional attributes. Using inpatient records and a behavior-calibrated interpretation of spatial accessibility, we integrate explainable machine learning with geospatial and environmental data to identify distinct distance-response regimes governing patient decision-making, rather than assuming uniform distance decay. The results indicate that spatial distance shows the strongest association with hospital selection but operates through threshold-based behavioral switching, with marked transitions occurring at specific distance intervals. Embedding these behavior-derived distance thresholds into a Two-Step Floating Catchment Area (2SFCA) framework, the study refines conventional accessibility assessment by explicitly accounting for both population demand and hospital resource availability. Spatial analysis shows that greater medical resource abundance significantly attenuates distance sensitivity, highlighting the context-dependent nature of spatial friction in healthcare utilization. By linking individual choice behavior with accessibility modeling, this study advances a behavior-calibrated framework for assessing healthcare accessibility. The findings provide applied insights for optimizing medical facility planning and resource allocation, particularly in rapidly urbanizing contexts where conventional distance-based assumptions may misrepresent actual patterns of healthcare use.