Economic and Clinical Intelligence (ECI) is a decision-oriented analytics framework that integrates clinical data, claims, and operational signals to improve how the United States finances care and allocates scarce healthcare resources. While U.S. reforms have expanded value-based payment, persistent variation, misaligned incentives, coding-driven risk distortions, and fragmented data systems limit progress. ECI addresses these barriers by combining interoperable data standards with predictive modeling, causal inference, and constrained optimization. We propose an end-to-end pipeline that (1) assembles and harmonizes claims, electronic health record, and supply-chain data, (2) defines outcomes and constraints aligned with payer and provider decisions, (3) builds calibrated risk and demand forecasts, (4) estimates causal effects of candidate interventions, and (5) allocates budgets and capacity using transparent objective functions that incorporate equity safeguards. The approach explicitly separates prediction from intervention value, reducing reliance on cost as a proxy for need and mitigating algorithmic bias. We show how ECI supports payment design, care management targeting, workforce planning, and resilient supply chains, drawing on evidence from variation research, risk adjustment literature, and applied analytics in screening disparities, drug shortages, and cybersecurity. The manuscript contributes a practical blueprint for implementing ECI in Medicare, Medicaid, and commercial markets, emphasizing governance, privacy, and auditability. By turning financing and resource allocation into measurable, testable decisions, ECI offers a pathway to lower total cost of care, improved outcomes, and more equitable distribution of services across populations and regions. We discuss deployment trade-offs, evaluation strategies, and future research priorities to strengthen validity and trust. Implementation is feasible with existing infrastructure. Its value should be monitored continuously.