Predicting Healthcare System Visitation Flow by Integrating Hospital Attributes and Population Socioeconomics with Human Mobility Data

2026-01-22

AI Summary

This study addresses the limitation of prior research that examines hospital attributes, socioeconomic factors, or mobility patterns in isolation by proposing an integrated modeling framework. It constructs a multi-source heterogeneous feature system incorporating hospital operational metrics (capacity, occupancy, reputation), population-level socioeconomic status (SES), and fine-grained human mobility data. The authors evaluate multiple predictive models—including Naive Regression, Gradient Boosting, MLP, Deep Gravity, and a Heterogeneous Graph Neural Network (HGNN)—and employ SHAP values and partial dependence plots for interpretability. Results show that Deep Gravity achieves the best performance: short-distance visits are primarily driven by convenience, whereas long-distance visits rely more heavily on hospital ratings. Sensitivity to hospital ratings is higher among Asian and highly educated populations but lower in predominantly White neighborhoods. Regions with higher proportions of racial minorities and young or elderly residents exhibit greater healthcare visit frequencies.

Technology Category

• Planning, Routing, and Scheduling: Model-Based Reasoning
• Machine Learning: Learning Preferences or Rankings
• Natural Language Processing: Interpretability, Analysis, and Evaluation of NLP Models

Application Category

• User Modeling, Personalization and Recommendation: Explainable and interpretable methods for personalization
• Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphs
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs

Abstract

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.

Problem

• healthcare visitation flow
• hospital attributes
• population socioeconomics
• human mobility
• spatial factors

Innovation

• Deep Gravity
• Human Mobility
• Healthcare Visitation Flow
• Heterogeneous Graph Neural Networks
• SHAP Analysis