H3: A Healthcare Three-Hop Index for Physician Referral Network Prediction

TL;DR

H3 is a novel, interpretable three-hop index designed to improve physician referral network prediction by modeling indirect pathways and addressing network sparsity and hub effects.

Contribution

The paper introduces H3, a new index that captures indirect referral pathways with degree normalization and redundancy penalties, outperforming existing methods.

Findings

H3 outperforms classical heuristics and deep learning baselines in prediction tasks.

H3 provides fully decomposable, transparent predictions traceable to intermediary physicians.

H3 demonstrates robustness across different temporal prediction regimes.

Abstract

Accurate prediction of physician referral links is essential for optimizing care coordination and reducing fragmentation in healthcare delivery. However, existing computational methods, ranging from triadic closure heuristics to graph neural networks, fail to capture the intrinsic properties of physician referral networks, including sparsity, disassortative degree mixing, and hub-dominated topology. Here, we propose H3, a healthcare three-hop index that addresses these limitations by modeling indirect referral pathways through intermediate physicians, with degree-based normalization and a redundancy penalty to mitigate hub-mediated noise. Using Medicare Physician Shared Patient Patterns data, we evaluate H3 under two complementary prediction regimes: within-period prediction, which assesses recovery of contemporaneous referral links under sparse conditions, and cross-period prediction, which tests robustness to temporal shift as referral windows expand. Across both regimes, H3 consistently outperforms classical heuristics and deep learning-based baselines. Unlike black-box neural network approaches, H3 produces fully decomposable predictions traceable to specific intermediary physicians, offering a transparent and deployable solution for referral network completion.