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Heart failure affects over 6 million Americans, with 30-day readmission rates remaining 20–25% despite longstanding quality improvement efforts. These readmissions cost about $17 billion annually and are penalized under federal reimbursement programs, yet existing prediction models have not achieved clinically useful performance. Most current models treat patients independently and fail to capture meaningful relationships among patients with similar medication patterns, admission histories, and social circumstances. They also often exclude critical social determinants of health (SDOH), such as housing instability and food insecurity, despite their strong association with readmission risk. In addition, black-box models lack interpretability, limiting clinician trust and usability. I argue that explainable graph neural networks (GNNs) integrating clinical data, SDOH, and prior admissions should replace traditional logistic regression and tree-based models for readmission prediction. Patient similarity graphs can represent clinically relevant relationships that tabular models miss, while graph attention mechanisms provide interpretable, actionable explanations. GNNs enable direct integration of SDOH and prior utilization patterns and offer transparency by highlighting which similar patients most influence predictions. This makes them more suitable for clinical decision support than existing approaches. Overall, persistent readmission rates reflect limitations in current modeling strategies. Explainable GNNs provide a more clinically meaningful and policy-relevant approach to improving prediction and reducing preventable readmissions.

High-risk transitions of care remain a major source of preventable readmissions, emergency department use, medication harm, and post-discharge deterioration. Existing risk tools often simplify the transition period into structured clinical variables and may not fully represent clinical complexity, social vulnerability, medication safety, or home support. Discharge planning commonly depends on generic risk scores, clinician judgment, and incomplete structured fields. These approaches may overlook risk signals embedded in discharge summaries, medication reconciliation records, follow-up plans, case management documentation, and social risk screening. This manuscript proposes a conceptual deep learning model for predicting high-risk transitions of care. The model is designed to fuse discharge summary narratives, medication reconciliation data, follow-up appointment status, home support indicators, and social risk variables into a unified transition risk score. The proposed architecture combines a clinical language encoder for discharge summaries with a structured-data network for medication, follow-up, home support, and social risk features. These representations are integrated through a multimodal fusion layer that would generate a patient-level risk estimate at the point of discharge. Conceptually, the model could identify patients at elevated risk of readmission, emergency department use, or adverse post-discharge outcomes who might be missed by traditional scores. It would also be expected to highlight clinically interpretable risk drivers that could support targeted transitional care planning. A multimodal deep learning model could strengthen precision transitional care by integrating clinical, logistical, medication-related, and social dimensions of risk. Such a model could help discharge teams prioritize intensified interventions for patients most vulnerable to unsafe care transitions.