Yuimedi Provides Technical Support for NEC's OMOP Conversion Verification of NDB and Electronic Medical Record Data
Yuimedi, Inc. announces that it provided technical support for OMOP CDM conversion as part of a joint research project titled "Technical Verification Study on OMOP CDM Conversion for Medical Information Standardization and Secondary Use."
The project was conducted in collaboration with NEC Corporation, Ehime University, and the Federation for Analyzable Medical Data (hereinafter "FedAna").
For a full overview of the verification project, please refer to NEC's announcement:https://jpn.nec.com/press/202603/20260325_03.html
Background
As Japan advances its National Medical Information Platform initiative and works toward a Japanese equivalent of the European Health Data Space (EHDS), interest in leveraging public databases such as the NDB (National Database) for research and policymaking has grown considerably. However, most public databases were not designed with research in mind. The significant effort required to understand and preprocess the data has long stood as a barrier to their practical use in research settings.
OMOP CDM directly addresses these challenges. The table below outlines the most common pain points in NDB analysis and how OMOP resolves them.
Common Challenges in NDB Analysis and How OMOP Helps
Yuimedi's Role
In this project, Yuimedi took the technical lead in performing high-quality OMOP conversion from synthetic NDB and electronic medical record (HL7 FHIR) data prepared by NEC. This included conversion specification design, data conversion, quality checks, and the design of cohort definitions and visualization of features corresponding to research questions set for different public database user profiles. The project demonstrated that OMOP-converted public databases can be directly and practically applied to real research.
This work builds on Yuimedi's established track record in medical data standardization, including:
Development of an HL7 FHIR to OMOP CDM conversion tool in collaboration with Ehime University (May 14, 2024)
Development of a new drug terminology mapping method using LLM and RAG in collaboration with Ehime University (March 26, 2025)
Evaluation of OMOP concept conversion for all entries in the MEDIS standard disease name master (February 12, 2026)
Looking Ahead
The standardization of medical data is accelerating globally. Governments and research institutions worldwide are prioritizing international data interoperability, and OMOP has rapidly emerged as the central standard. In Japan, momentum is building for OMOP conversion of public databases and electronic medical record data. This verification is a meaningful step in that direction.
As a company with OMOP-focused standardization technology at the core of its business, Yuimedi remains at the forefront of this field. We will continue working alongside industry partners and relevant institutions, guided by our mission of delivering the right medical care to the right patients through data, to promote the effective and widespread use of medical data in Japan and beyond.
Comment from NEC
In this project, we converted synthetic claims, DPC, and HL7 FHIR prescription data into OMOP CDM, an internationally recognized common data standard, and examined the schema and data utility required for real research workflows. Yuimedi brought deep expertise in OMOP conversion and provided practical consulting throughout the process. Their involvement allowed us to progress smoothly through conversion specification development, quality assurance, cohort definition, and feature visualization, all in a form directly applicable to research. We look forward to seeing OMOP-based data integration make it easier for researchers to work with data, and to the continued development of a high-precision, reproducible research environment that advances medical research at scale.
NEC Corporation, Government and Public Integration Division Senior Professional, Michio Kaji
About OMOP CDM
OMOP CDM (Observational Medical Outcomes Partnership Common Data Model) is a common data model published by OHDSI (Observational Health Data Sciences and Informatics). Designed for real-world data analysis, it features a standardized vocabulary system capable of integrating medical terminology from around the world. It allows real-world data from different countries to be expressed in a shared format, and its structure is optimized for efficient database storage and observational research.
