Healthcare Intelligence Platform: Foundational Data Governance & Core Layers BlueprintSubtitle: Enterprise Reference Guide for Identity Resolution, Reference Data Architecture, and Semantic Standardization

This release marks the deployment of a foundational architectural framework within the comprehensive Healthcare Intelligence Reference Architecture ecosystem (aligned under global baseline registration DOI: 10.5281/zenodo.22255118). While sibling publications govern the analytical consumption and subject-oriented domain data marts (such as NCD and Oncology intelligences), this standalone volume focuses strictly on the abstract Logical Layer of the infrastructure root—systematically consolidating the enterprise core principles of master data management, multi-system identity resolution, and semantic data harmonization. Authored and architected by Chotika Ruangjun, this technical specification is structured to remain entirely vendor-neutral and production-ready. To protect absolute system security and corporate intellectual property, the document is white-labeled, containing zero live physical schemas, database tables, or backend server source codes. Key Architectural Infrastructure Contained in This Release: 1. MASTER & REFERENCE DATA ARCHITECTURE: Sets the decoupled structural parameters that separate stable master registries from fast-moving transactional rows. It provides production-level expandability vectors designed to grow alongside digital healthcare maturity and features native lookup integration frameworks for national medical terminologies such as Thailand Medicines Terminology (TMT) to eliminate conflicting business and clinical semantics. 2. IDENTITY RESOLUTION & DATA LINKAGE ARCHITECTURE: Establishes a centralized, privacy-preserving cross-system patient matching engine. It replaces fragmented, siloed hospital identifiers with a non-PII surrogate anchor—the Enterprise Patient Key (EPK). This module introduces an auditable 4-Tier Linkage Confidence Framework (Exact, Probable, Possible, and Unresolved matching logic) to prevent double-counting while ensuring strict global privacy compliance (PDPA/GDPR). 3. HARMONIZED DATASET & SEMANTIC STANDARDIZATION LAYER: Formulates the logical abstraction bridge positioned directly between ingestion endpoints and distributed data marts. It details the automated ingestion and categorization pipelines that translate discordant hospital entries into unified international standards, including structural mapping layers for HL7 FHIR, ICD-10, ICD-O, LOINC, and SNOMED CT. By moving organizations away from traditional, ad-hoc data cleaning routines, this reference guide constructs the clean, cohort-ready data repositories and precise semantic mappings required to feed modern enterprise machine learning, feature engineering stores, and hallucination-free AI reasoning systems (LLM and Retrieval-Augmented Generation / RAG frameworks).