RUAHFORGE v2: AI-Compatible Institutional Decision Architecture for Complex Clinical Cases

Short Description RUAHFORGE v2 is a theoretical institutional decision architecture designed to support structured reasoning in complex clinical environments.The framework integrates Bayesian updating, Monte-Carlo uncertainty modeling, and utility-based path comparison to enable transparent evaluation of alternative decision pathways under uncertainty. Rather than providing a software product, the system is intended as a prompt-native, machine-readable capability architecture that can be used by clinicians, medical controlling, legal departments, and institutional AI systems to structure decision processes, identify documentation gaps, and compare clinical-economic-governance trade-offs. The package includes a formal conceptual model, machine-readable schemas (YAML/JSON), a prompt operating system for LLM-based interaction, and a reference notebook illustrating the evaluation logic.RUAHFORGE v2 is designed to remain implementation-agnostic and may be integrated into existing institutional workflows or internal software systems.