PRIME-Care: A Unified Reinforcement Learning and Mathematical Optimization Framework for Personalized Treatment Planning Under Clinical Uncertainty in Telemedicine

This study presents PRIME-Care, a unified framework combining reinforcement learning (RL) and mathematical optimization for personalized treatment planning under clinical uncertainty. The framework aims to improve the safety, personalization, and predictive accuracy of treatment strategies in dynamic, uncertain healthcare environments. PRIME-Care incorporates a hierarchical bilevel structure where the optimization layer enforces safety constraints, and the RL layer adapts treatment policies based on patient-specific dynamics. Key innovations include the propagation of uncertainty through the treatment planning process and the use of probabilistic latent-state models for more accurate disease progression forecasting. Experimental results demonstrate that PRIMECare outperforms traditional RL models and optimization-based approaches in terms of safety, personalization, and predictive accuracy. Specifically, it reduces critical constraint violations by over 70%, exhibits superior trajectory alignment to clinician-curated plans, and provides more stable, temporally consistent disease forecasts. Additionally, robustness tests show that PRIME-Care maintains near-optimal performance under perturbations, while traditional RL models experience significant degradation. These results suggest that PRIME-Care offers a promising solution for integrating AI-driven decision support into clinical workflows, providing safer, more personalized, and interpretable treatment plans. All code implementations are publicly available to ensure full reproducibility and facilitate further research in this domain.