Predictive Modeling of Healthcare Utilization and Outcomes Using Socioeconomic and Geographic Data

Abstract

Healthcare delivery systems are complex, heterogeneous, and increasingly data rich. Socioeconomic and geographic signals often precede shifts in demand and modulate clinical risk. Against this background, predictive models that fuse administrative, clinical, and neighborhood context can improve foresight and equity. This paper develops a unified, rigorously evaluated framework for forecasting healthcare utilization and downstream outcomes by integrating patient-level covariates with socioeconomic indices and spatial graphs that encode proximity, mobility, and supply-side capacity. We formalize utilization as a coupled spatiotemporal point process with neighborhood-regularized intensities and represent outcomes with cause-specific hazards that incorporate social determinants through structured priors and graph penalties. The approach combines convex risk minimization with low-rank task couplings, graph neural operators, doubly robust causal adjustments, and distributionally robust guarantees to mitigate dataset shift. A matrix-tensor factorization links visit counts, diagnostic mixtures, and locations, while a Laplacian-constrained embedding stabilizes estimation under sparse regional data. Calibration and discrimination are assessed jointly, with uncertainty quantification derived from sandwich asymptotics and Bayesian posterior curvature. Extensive ablations isolate the contributions of spatial smoothness, transport-based domain adaptation, and fairness constraints, and we demonstrate policy counterfactuals for benefit targeting and capacity planning under demographic drift. Across multiple utilization endpoints and survival outcomes, the framework yields consistent gains in accuracy, calibrated coverage, and cross-geography transport, while maintaining parity gaps below 2\% without material loss in predictive power. The resulting methodology provides a coherent blueprint for health systems seeking anticipatory, equitable, and privacy-preserving decision support rooted in socioeconomic and geographic structure.