Medical Data Standardization Startup Yuimedi Provides Technical Support for NEC's OMOP Conversion Demonstration of NDB and EMR Information
Yuimedi provided technical support for OMOP CDM conversion in a joint research project with NEC, Ehime University, and FedAna to standardize Japanese medical data. This addresses the challenge of data preprocessing in utilizing public databases like NDB for research.
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- 📰 Published: April 1, 2026 at 19:10
- 🔍 Collected: April 1, 2026 at 16:47
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Yuimedi Inc. (Headquarters: Chuo-ku, Tokyo; CEO: Emiri Grimes; hereinafter 'Yuimedi') announces that it provided technical support for OMOP CDM (hereinafter 'OMOP') conversion in the 'OMOP CDM Conversion Technology Verification Research for Medical Information Standardization and Secondary Utilization' conducted jointly with NEC Corporation (Headquarters: Minato-ku, Tokyo; Director, Representative Executive Officer, President and CEO: Takayuki Morita; hereinafter 'NEC'), Ehime University (Matsuyama-shi, Ehime; University President: Hiroshige Nishina), and the Medical Data Linkage Analysis Infrastructure Association (hereinafter 'FedAna').
For an overview of this verification, please refer to NEC's announcement.
https://jpn.nec.com/press/202603/20260325_03.html
■Background
In order to realize the 'National Medical Information Platform Concept' and the 'Japanese version of EHDS' promoted by the government, expectations are rising for the use of public databases (hereinafter 'Public DBs'), including the National Database (NDB), for research and policy-making. On the other hand, the structure of many Public DBs is not predicated on research use. The heavy burden placed on understanding and preprocessing the data has been a major barrier to research utilization.
[Examples in NDB Analysis]
Common Issues | Details of Issues | Image of Solution through OMOP Utilization
Complexity of Code Systems | Within a single domain, two problems are intertwined: the coexistence of code systems and the hierarchy of granularity. Taking pharmaceuticals as an example, multiple systems such as YJ codes, receipt computer codes, and HOT codes coexist for the same ingredient, and even within each system, the granularity is divided into ingredient, product, and dosage form levels. Therefore, just to gather 'patients prescribed statin drugs', one must list tens to hundreds of codes. | Multiple local codes are uniformly mapped to the concept ID of the OMOP standard vocabulary. For statin drugs, they can be cross-tabulated with the same concept ID regardless of original, generic, or dosage form, significantly reducing the management cost of code lists.
Complex Period Calculation of Longitudinal Data | Since NDB generates records per billing unit, grasping the longitudinal history of 'what treatments a patient received at which medical institution' requires name identification and complex matching processing. | It is a design philosophy that aggregates data per patient, and diagnosis, prescription, treatment, and examination are...
For an overview of this verification, please refer to NEC's announcement.
https://jpn.nec.com/press/202603/20260325_03.html
■Background
In order to realize the 'National Medical Information Platform Concept' and the 'Japanese version of EHDS' promoted by the government, expectations are rising for the use of public databases (hereinafter 'Public DBs'), including the National Database (NDB), for research and policy-making. On the other hand, the structure of many Public DBs is not predicated on research use. The heavy burden placed on understanding and preprocessing the data has been a major barrier to research utilization.
[Examples in NDB Analysis]
Common Issues | Details of Issues | Image of Solution through OMOP Utilization
Complexity of Code Systems | Within a single domain, two problems are intertwined: the coexistence of code systems and the hierarchy of granularity. Taking pharmaceuticals as an example, multiple systems such as YJ codes, receipt computer codes, and HOT codes coexist for the same ingredient, and even within each system, the granularity is divided into ingredient, product, and dosage form levels. Therefore, just to gather 'patients prescribed statin drugs', one must list tens to hundreds of codes. | Multiple local codes are uniformly mapped to the concept ID of the OMOP standard vocabulary. For statin drugs, they can be cross-tabulated with the same concept ID regardless of original, generic, or dosage form, significantly reducing the management cost of code lists.
Complex Period Calculation of Longitudinal Data | Since NDB generates records per billing unit, grasping the longitudinal history of 'what treatments a patient received at which medical institution' requires name identification and complex matching processing. | It is a design philosophy that aggregates data per patient, and diagnosis, prescription, treatment, and examination are...