Modeling Hospital Visitation Flows Through Distance‐Attractiveness Tradeoffs: Integrating Human Mobility, Hospital Attributes, and Socioeconomic Context

Hospital visitation reflects a complex destination‐choice process shaped by travel cost, perceived hospital attractiveness, and neighborhood socioeconomic conditions. However, less is known about how these factors jointly structure realized hospital destination choice and how their relationships vary across travel distances and community contexts. This study integrates 4 years (2020–2023) of anonymized mobility data in Houston, TX, USA, with hospital attributes, Google Maps ratings and review counts, and 2023 American Community Survey socioeconomic indicators, including health‐insurance coverage. Five predictive models were evaluated across multiple test‐set proportions using 10 repeated train‐test splits. The heterogeneous graph neural network (HGNN) consistently achieved the strongest predictive performance, while interpretation of the HGNN identified drive time as the dominant predictor and showed that hospital capacity, occupancy, spatial context, online visibility, ratings, and community characteristics jointly modify visitation patterns. Controlled scenario analyses indicated that rating‐related distance‐decay crossovers vary across socioeconomic contexts rather than following a uniform threshold. These findings highlight hospital visitation as a heterogeneous travel behavior shaped by interacting spatial, institutional, and socioeconomic factors.