Data Harmonization Techniques for Multi-Source Healthcare Records

Abstract

Healthcare ecosystems generate large volumes of patient data across heterogeneous systems, including electronic health records (EHRs), laboratory information systems, payer claims platforms, and public health registries. While these datasets offer significant value for clinical decision-making, population health analytics, and research, their effective use is constrained by inconsistencies in structure, semantics, and data quality. Data harmonization addresses these challenges by transforming disparate healthcare data sources into a unified, standardized, and analyzable representation. This paper presents a comprehensive study of data harmonization techniques for multi-source healthcare records. We propose a layered harmonization framework encompassing syntactic normalization, schema alignment, semantic standardization, and data quality governance. The study examines widely adopted healthcare interoperability standards, including HL7 FHIR for data exchange and OMOP Common Data Model (CDM) for analytics, alongside terminology systems such as SNOMED CT, LOINC, and ICD. A real-world case study involving the harmonization of EHR, laboratory, and claims data across multiple providers is presented to demonstrate practical implementation, challenges, and measurable outcomes. Evaluation metrics related to mapping coverage, data quality improvement, and analytics readiness are analyzed. The results demonstrate that a metadata-driven, standards-aligned harmonization approach significantly improves data consistency, interoperability, and downstream analytical performance.