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Specialty referral pathways are a critical point at which healthcare inequities can emerge. Referral decisions may be shaped by insurance status, race, language, documentation practices, diagnosis severity, and the structure of available provider networks. Health systems often lack scalable and explainable tools for detecting inequitable referral patterns as they occur. As a result, discriminatory or structurally biased patterns may remain hidden within routine clinical operations. This article develops a conceptual explainable artificial intelligence model for identifying whether demographic or insurance factors unduly influence specialty referral decisions after accounting for clinical severity. The model is designed to support transparent, fairness-oriented referral analytics rather than replace clinical judgment. The proposed model uses a gradient-boosted classification framework trained on referral-eligible primary care encounters. Input features include patient demographics, insurance type, diagnosis severity, primary care note-derived complexity and completeness features, and provider network metrics, with SHAP-based explanation layers used for fairness auditing. Conceptually, the model could flag encounters in which predicted referral likelihood diverges from clinically expected patterns. These flags would be interpreted through explanation methods that attribute potential inequitable influence to insurance, demographic, documentation, or network-related factors. The model could help health systems audit, explain, and intervene on systemic specialty referral bias. Its central contribution is a transparent framework for moving referral equity work from retrospective description toward proactive, data-driven fairness review.

Care fragmentation is a silent driver of adverse outcomes because it distributes responsibility for a patient’s care across multiple providers, settings, and transitions. It is often recognized only after duplication, missed follow-up, or contradictory management has already occurred. Fragmentation unfolds through networks of providers, referrals, encounters, and unresolved care tasks. Conventional prediction models often treat these events as independent patient-level variables rather than as relational patterns embedded in a care system. This article develops a conceptual graph neural network model for predicting the likelihood of fragmented care episodes. The proposed model uses provider networks, encounter sequences, referral patterns, cross-setting utilization, and unresolved care gap indicators as an integrated graph representation. A heterogeneous graph is constructed with patient and provider nodes, while edges represent encounters, referrals, shared patients, cross-setting transitions, and unresolved care gaps. A graph neural network learns patient embeddings from this relational structure and produces a fragmentation risk score for each patient-period. Conceptually, the model would identify patients at elevated risk of fragmentation, such as those moving across many disconnected providers, receiving repeated referrals, or accumulating open follow-up tasks. These signals could support earlier care coordination before avoidable gaps widen into adverse outcomes. The relational lens of graph neural networks could transform fragmentation prediction from a static risk scoring task into a network-aware early-warning system. Such a model would be expected to help health systems detect fragmented care episodes before they become operationally and clinically consequential.