NHOS NIME™ Architecture: A Multi‑Layered Framework for Distributed Clinical Intelligence and Localized Health‑System Adaptation (v1.4.0)

The NHOS NIME™ Architecture is a multi-layered framework for distributed clinical intelligence and localized health-system adaptation developed by NHOS Labs, Inc. NIME (Natural Intelligence for Medical Environments) provides an architectural model for organizing health intelligence across local, privacy-preserving, and interoperable computing environments while supporting adaptation to diverse clinical, institutional, and geographic contexts. The framework addresses architectural challenges associated with deploying health intelligence in environments where data privacy, local autonomy, connectivity constraints, interoperability, contextual relevance, and responsible use of artificial intelligence must be considered simultaneously. It describes how distributed intelligence can be structured across multiple layers to support localized health-system capabilities without requiring centralized control of all health data or computational processes. This publication documents the NHOS NIME™ Architecture v1.4.0, including its conceptual foundations, multi-layered system model, intelligence-distribution approach, localization principles, privacy and data-governance considerations, interoperability concepts, and potential applications within health-system environments. NIME is presented as an architectural and systems-engineering framework, not as a clinical decision-making system or a claim of demonstrated clinical efficacy. The framework does not replace qualified clinical judgment, established clinical guidelines, regulatory requirements, or institutional governance. Specific implementations may require additional technical, clinical, security, privacy, interoperability, and regulatory validation depending on their intended use and deployment environment. This publication establishes the v1.4.0 architecture as a documented research and development artifact and provides a foundation for subsequent implementation, evaluation, interoperability research, and empirical validation of distributed and localized health-intelligence systems.