Phase-Coupled Stochastic Cost Process: A Self-Modifying Point Process Framework for Individualized Health Insurance Cost Projection

This paper introduces the Phase-Coupled Stochastic Cost Process (PCSCP), a computational framework for individualized health insurance cost projection that models the dynamic interaction between insurance benefit design, healthcare utilization behavior, and stochastic healthcare events. Unlike conventional actuarial or deterministic healthcare cost calculators, the PCSCP framework incorporates state-dependent utilization feedback, behavioral elasticity, Monte Carlo simulation, and insurance phase transitions to model how deductible structures, coinsurance rates, and out-of-pocket maximums influence healthcare utilization over time. The framework integrates published findings from the RAND Health Insurance Experiment, healthcare utilization literature, and stochastic process modeling to simulate individualized healthcare cost trajectories under different insurance plan structures. The paper further introduces concepts including endogenous utilization feedback, phase-coupled behavioral dynamics, and plan-dependent tail risk modification. Validation is conducted using published benchmarks from RAND HIE, MEPS, KFF, and CMS datasets. The paper positions PCSCP as an exploratory healthcare informatics and computational health economics framework for insurance cost modeling, population health analytics, and healthcare decision-support research.