This session outlines a practical framework for integrating quantum-inspired optimization and AI-driven modeling into real-world healthcare analytics. Building on the author’s recent four-part educational series, the talk bridges foundational concepts — from stochastic processes and Markov models to energy-based architectures like Boltzmann Machines — and connects them to modern applications in machine learning and hybrid optimization. Case studies include pharmacy benefit manager (PBM) rebate negotiation modeling and the unsupervised detection of latent states in chronic disease populations, with an emphasis on interpretability and actionability. Rather than presenting speculative visions, this session focuses on how current AI tools, combined with emerging quantum-classical workflows, can enhance decision support and cost modeling in high-stakes domains. Attendees will walk away with an understanding of where and how quantum-adaptable AI approaches can be responsibly explored, especially in complex environments like healthcare where both transparency and trust are paramount.