Abstract

Machine learning can do more than forecast claims. It can connect clinical risk, social context, and macroeconomic signals to predict what patients, payers, and regional economies will actually face. This paper proposes and evaluates a U.S. health finance analytics blueprint that joins person-level utilization, community-level social determinants, and national indicators to predict annual healthcare costs and economic outcomes. We integrate widely used surveys and administrative datasets, harmonize coding, and align time windows so that predictions aggregate cleanly from people to places. Methodologically, we use a two-part framework for spending (any spend classifier plus conditional cost regressor), gradient boosted trees for nonlinear interactions, and explainable AI with Shapley values to expose drivers of cost and financial risk. We pair individual predictions with county level forecasts of outcomes relevant to households and governments, including out of pocket exposure, medical debt risk, and employment impacts. We also embed fairness tests, privacy safeguards, and model governance consistent with health data regulations.To illustrate feasibility, we present a reproducible pipeline with model comparison, error analysis, and interpretable feature summaries. The approach targets practical decisions: pricing and benefit design for insurers, budgeting and value-based payment for providers, and policy planning for agencies. Results from a worked example (synthetic for demonstration) show that gradient boosting improves explanatory power over linear baselines while remaining interpretable, and that adding social and economic context reduces error for high need patients. We conclude with implementation guidance, limitations, and a research agenda to validate this integrated health finance framework on public microdata and to study its equity and policy implications at scale.