Healthcare visitation patterns are influenced by a complex interplay of hospital attributes, population socioeconomics, and spatial factors. However, existing research often adopts a fragmented approach, examining these determinants in isolation. This study addresses this gap by integrating hospital capacities, occupancy rates, reputation, and popularity with population SES and spatial mobility patterns to predict visitation flows and analyze influencing factors. Utilizing four years of SafeGraph mobility data and user experience data from Google Maps Reviews, five flow prediction models, Naive Regression, Gradient Boosting, Multilayer Perceptrons (MLPs), Deep Gravity, and Heterogeneous Graph Neural Networks (HGNN),were trained and applied to simulate visitation flows in Houston, Texas, U.S. The Shapley additive explanation (SHAP) analysis and the Partial Dependence Plot (PDP) method were employed to examine the combined impacts of different factors on visitation patterns. The findings reveal that Deep Gravity outperformed other models. Hospital capacities, ICU occupancy rates, ratings, and popularity significantly influence visitation patterns, with their effects varying across different travel distances. Short-distance visits are primarily driven by convenience, whereas long-distance visits are influenced by hospital ratings. White-majority areas exhibited lower sensitivity to hospital ratings for short-distance visits, while Asian populations and those with higher education levels prioritized hospital rating in their visitation decisions. SES further influence these patterns, as areas with higher proportions of Hispanic, Black, under-18, and over-65 populations tend to have more frequent hospital visits, potentially reflecting greater healthcare needs or limited access to alternative medical services.

翻译：医疗访问模式受到医院属性、人口社会经济因素与空间因素复杂交互作用的影响。然而，现有研究常采用碎片化方法，孤立地考察这些决定因素。本研究通过整合医院容量、占用率、声誉和受欢迎程度，以及人口社会经济地位（SES）和空间移动模式，以预测访问流量并分析影响因素，从而弥补这一研究空白。利用四年期的SafeGraph移动数据和来自Google Maps Reviews的用户体验数据，本研究训练并应用了五种流量预测模型——朴素回归、梯度提升、多层感知机（MLP）、深度引力模型和异构图神经网络（HGNN），以模拟美国德克萨斯州休斯顿的医疗访问流量。研究采用沙普利加性解释（SHAP）分析和偏依赖图（PDP）方法，考察了不同因素对访问模式的综合影响。研究结果表明，深度引力模型的表现优于其他模型。医院容量、ICU占用率、评分和受欢迎程度显著影响访问模式，且其效应随出行距离不同而变化。短距离访问主要受便利性驱动，而长距离访问则受医院评分影响。在白人占多数的区域，短距离访问对医院评分的敏感性较低；而亚裔人口和受教育程度较高的人群在访问决策中更重视医院评分。社会经济地位进一步影响这些模式，西班牙裔、黑人、18岁以下及65岁以上人口比例较高的区域往往医院访问更频繁，这可能反映了更高的医疗需求或替代性医疗服务获取途径有限。