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Chronic postsurgical pain (CPSP) affects 10–50% of surgical patients and is a major contributor to long-term opioid use and reduced quality of life. Current predictive models treat patients independently and fail to capture how risk evolves over time or how postoperative opioid trajectories influence divergence in outcomes. We propose a dynamic graph neural network (GNN) framework in which patients are modeled as nodes and similarity-based edges evolve over time based on opioid prescription patterns, pain scores, and preoperative psychological factors. The model includes (1) a patient graph with static preoperative features, (2) a temporal edge update mechanism, (3) a GNN message-passing layer that aggregates information from dynamically connected patients, and (4) a prediction head estimating CPSP risk at 3, 6, and 12 months. By modeling changing patient relationships after surgery, the framework captures how similar patients may diverge or converge depending on postoperative management, enabling more accurate and personalized CPSP risk prediction using longitudinal electronic health record data.

Pressure ulcers are a persistent issue in bedridden patients, especially in intensive care, rehabilitation, and long-term care, leading to pain, infection, and extended hospital stays. Current risk assessments rely on intermittent scoring and clinical judgment, failing to account for continuous changes in body posture, tissue loading, and mechanical tolerance. This conceptual framework proposes a physics-informed graph neural network to predict pressure ulcer risk by integrating data from body position sensors, local tissue loading, and skin perfusion measurements into a dynamic, personalized model. The model represents the body as a graph, with nodes representing pressure-prone areas and edges indicating anatomical and mechanical connections. Tissue stress, perfusion data, and posture features are processed through network layers constrained by soft-tissue mechanics. By encoding the relationship between external forces, internal tissue deformation, ischemia, and damage, the framework allows risk propagation across adjacent anatomical regions. This approach offers a path for continuous, personalized pressure ulcer risk monitoring, laying the foundation for clinical validation and sensor integration.

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.