Physics-Informed Graph Learning for Spatially Contiguous and Capacity-Constrained Hospital Service Area Delineation | JoVE Visualize

Area of Science:

Health Geography
Computational Social Science
Network Science

Background:

Accurate delineation of Hospital Service Areas (HSAs) is crucial for effective healthcare resource allocation and policy development.
Existing methods for HSA delineation face challenges in simultaneously integrating patient flow, spatial contiguity, and capacity constraints.
Traditional spatial clustering and Graph Neural Networks (GNNs) often fail to adequately capture complex network dynamics or enforce strict capacity limits.

Purpose of the Study:

To develop a novel framework, SGCN-MST, that integrates physics-informed graph learning with constraint-based regionalization for improved HSA delineation.
To address the limitations of existing methods in capturing patient flow, spatial contiguity, and multiple capacity constraints simultaneously.
To provide a statistically robust and administratively practical tool for health geography research and policy.

Main Methods:

A physics-informed GNN is employed to simulate patient flow as a spatial diffusion process, capturing interaction decay.
An "interaction-aware" embedding is generated and utilized within a spatial Minimum Spanning Tree (MST) algorithm.
A Depth-First Search (DFS) strategy is incorporated to dynamically balance modularity optimization with multiple capacity constraints.

Main Results:

The SGCN-MST model reveals a nested, hierarchical spatial structure in Florida's inpatient data, reflecting functional medical hierarchies.
Large regional referral centers and compact local communities were identified, mirroring real-world healthcare delivery patterns.
Comparative analysis demonstrated that SGCN-MST offers a more balanced and policy-ready solution than baseline methods like ScLeiden and Region2Vec.

Conclusions:

SGCN-MST provides a statistically robust and administratively practical tool for delineating Hospital Service Areas.
The framework effectively balances modularity optimization with critical constraints like localization, contiguity, and capacity feasibility.
This approach offers significant advancements for healthcare resource allocation, policy-making, and health geography.