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Seasonal influenza and respiratory viruses cause recurring surges in hospital bed demand, often resulting in overcrowding under reactive capacity management. ILI surveillance data and environmental factors such as temperature and humidity provide early signals of transmission, making them valuable for forecasting healthcare demand. Traditional forecasting methods rely on simple time series models or historical averages and fail to capture spatial disease spread or environmental influences, leading to inaccurate predictions and poor resource allocation. We propose a spatiotemporal graph neural network (ST-GNN) that integrates ILI surveillance and environmental data for regional daily hospital bed demand forecasting. The model represents regions as graph nodes connected by population flow, enabling joint spatial-temporal modeling of disease dynamics. The framework uses regional graph construction, ILI data from healthcare visits, and environmental variables such as temperature, humidity, and air quality. These inputs are processed by the ST-GNN to predict daily bed demand. The approach captures spatial disease propagation and improves early detection of demand surges, supporting proactive healthcare planning. The ST-GNN provides a scalable, data-driven framework for improving hospital bed demand forecasting and enhancing preparedness during seasonal epidemics.

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.