NHOS Track™ Health Intelligence Console: A Privacy-First Architecture for Local-First Health Data Integration, Temporal Analysis, Relationship Detection, and Provenance-Aware Health Intelligence

NHOS Track™ Health Intelligence Console presents a privacy-first, local-first architecture for transforming heterogeneous personal health records into structured, traceable, and interpretable health information without requiring a centralized user account or centralized storage of personal health data.

The architecture is organized around the NIME™ Intelligence Pipeline, which provides data adaptation, canonical entity resolution, typed canonical observations, temporal analysis, relationship detection, health-intelligence interpretation, dashboard presentation, and provenance tracing.

The Relationship Matrix provides cross-entity temporal association analysis using a defined analytical window and one-to-one temporal matching. Its outputs are explicitly distinguished from statistical correlation, causal effect estimation, clinical risk assessment, diagnosis, or predictive inference.

The architecture is organized around four architectural pillars: Data Integrity, Intelligence, Transparency, and Privacy. Source records are preserved, derived observations remain traceable through a documented provenance chain, and health-intelligence outputs use qualified observational language rather than causal or diagnostic claims.

The console is designed for local-first processing using locally available health data, including browser-based storage mechanisms such as IndexedDB and localStorage. Core dashboard operations do not require centralized health-data storage, a user account, or server-side health-data processing.

The publication documents the system architecture, NIME™ Entity Registry, canonical observation model, data-adapter and entity-resolution framework, temporal analysis framework, Relationship Matrix, health-intelligence layer, provenance architecture, state architecture, user-interface architecture, privacy and security boundaries, performance considerations, extensibility model, data-management lifecycle, limitations, and future development roadmap.

Version 1.0 | August 2026 | NHOS Labs, Inc.