# About Name: Yuimedi Description: Built for clinical researchers who need an end-to-end workflow from data validation to publication-ready outputs URL: https://us.yuimedi.com/resources # Navigation Menu - Home: https://us-yuimedi-com.vercel.app - Product: # - Yuiquery Research: https://us-yuimedi-com.vercel.app/product-yuiquery-research - Yuiquery Insights: https://us-yuimedi-com.vercel.app/product-yuiquery-insights - About: # - About Us: https://us-yuimedi-com.vercel.app//about-us - Resources: # - Blog: https://us-yuimedi-com.vercel.app/resources/category/blog/ - Video: https://us-yuimedi-com.vercel.app/resources/category/video/ - News: https://us-yuimedi-com.vercel.app/resources/category/news/ - Book a Demo: https://us-yuimedi-com.vercel.app/contact-us # Blog Posts ## Yuimedi Evaluates OMOP Concept Mapping Coverage for All Entries in the MEDIS Standard Disease Name Master Author: Yuimedi US Author URL: https://us.yuimedi.com/resources/author/yuimedi-us Published: 2026-04-06 Category: news Category URL: https://us.yuimedi.com/resources/category/news Tags: news Tag URLs: news (https://us.yuimedi.com/resources/tag/news) URL: https://us.yuimedi.com/resources/yuimedi-evaluates-omop-concept-mapping-coverage-for-all-entries-in-the-medis-standard-disease-name-master ![blog_banner_omop_research.png](https://prod.superblogcdn.com/site_cuid_cmlztbkr3003v01w1bji1rodl/images/blogbanneromopresearch-1775493469604-compressed.png) Yuimedi, Inc. has published a peer-reviewed study in the Journal of Japan Association for Medical Informatics (Vol. 45, No. 6), evaluating the feasibility of mapping all entries in the MEDIS Standard Disease Name Master to OMOP standard concepts via ICD-10 codes. The MEDIS Standard Disease Name Master is one of the most widely used clinical terminologies in Japan. Applied to all 27,564 disease names using automated, vocabulary-based mapping with OHDSI-managed vocabulary data, the study achieved a mapping success rate of 99.2%. It also systematically classified the entries that could not be mapped, identifying the structural factors behind each unmappable case. **Background** As real-world data (RWD)-based drug development and clinical research continue to expand globally, the OMOP Common Data Model has become a widely used framework for enabling international interoperability of health data. Converting Japanese healthcare data to OMOP requires mapping Japanese disease names and domestic code systems to the standard concepts defined within OMOP. Yet until now, little systematic research has evaluated the practical limitations of this conversion at full-corpus scale, or the structural factors driving unmappable terms. **Key Findings** The study produced three clear findings. Automated mapping via ICD-10 codes achieved a 99.2% success rate across all 27,564 disease names in the MEDIS Standard Disease Name Master. The remaining 0.8% of unmappable entries were classified into three structural categories: - ICD-10 codes with supplementary numeric qualifiers - ICD-10 codes with supplementary alphabetic qualifiers - Entries with no ICD-10 code assigned For each category, the study outlines practical mapping strategies with concrete examples, providing actionable guidance for improving reproducibility and automation accuracy in real-world OMOP conversion workflows. **Yuimedi's Expertise** Yuimedi's core strength is the operational depth of its OMOP conversion work, particularly in navigating the complexity of medical terminologies and code systems. The approach in this study reflects that commitment: OMOP conversion that is transparent and reproducible, while accounting for the practical constraints of Japan-specific code usage and differences in coding granularity. **Outlook** Building on this research, Yuimedi will continue advancing the standardization of real-world data in Japan. Japanese healthcare data is internationally recognized for its quality and comprehensiveness. Differences in medical terminology and coding systems, however, have kept it from being fully utilized in international research. Yuimedi is directly addressing this gap through OMOP conversion technology, working toward an environment where Japanese healthcare data can be used at a global standard. **About Journal of Japan Association for Medical Informatics** Published by the Japan Association for Medical Informatics (JAMI), this peer-reviewed journal covers research at the intersection of healthcare and information technology, including health data utilization, medical information systems, and clinical informatics. It serves as a key publication for disseminating research trends and practice-relevant insights in the field of medical informatics. Online Journal: [https://www.jami.jp/document/online-journal](https://www.jami.jp/document/online-journal) **About OMOP CDM** The OMOP Common Data Model (Observational Medical Outcomes Partnership Common Data Model) is an open community data standard maintained by OHDSI. Designed for the analysis of real-world observational data, it features a standardized vocabulary system capable of integrating medical terminologies from around the world, enabling a unified representation of real-world data across countries. Its normalized relational structure also makes it well-suited for observational research. --- This blog is powered by Superblog. Visit https://superblog.ai to know more. --- ## Yuimedi Provides Technical Support for NEC's OMOP Conversion Verification of NDB and Electronic Medical Record Data Author: Yuimedi US Author URL: https://us.yuimedi.com/resources/author/yuimedi-us Published: 2026-04-01 Category: news Category URL: https://us.yuimedi.com/resources/category/news Tags: news Tag URLs: news (https://us.yuimedi.com/resources/tag/news) URL: https://us.yuimedi.com/resources/yuimedi-provides-technical-support-for-necs-omop-conversion-verification-of-ndb-and-electronic-medical-record-data ![Yuimedi Press Banner_1200x628.png](https://prod.superblogcdn.com/site_cuid_cmlztbkr3003v01w1bji1rodl/images/yuimedi-press-banner1200x628-1775147767860-compressed.png) 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](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** **Common Challenge** **Details** **How OMOP Addresses It** **Complexity of code systems** A single drug ingredient can carry multiple codes such as YJ codes, receipt calculation codes, and HOT codes, each with varying levels of granularity across ingredient, product, and formulation. Identifying patients prescribed a statin, for example, may require enumerating dozens to hundreds of codes. Multiple local codes are unified and mapped to OMOP standard vocabulary concept IDs. Statins can be aggregated under a single concept ID regardless of brand, generic, or formulation, significantly reducing the burden of managing code lists. **Complex period calculations in longitudinal data** Because NDB records are generated per billing unit, understanding a patient's treatment history across institutions requires complex record linkage and matching. OMOP is designed to aggregate data at the patient level, with diagnoses, prescriptions, procedures, and tests stored chronologically under a single patient ID. This makes it straightforward to track clinical events over time and analyze treatment pathways and outcomes naturally. **Non-reusable analysis protocols** Protocols built for the NDB depend on its unique structure and cannot be reused for other databases. With an OMOP-compliant database, the same analysis protocol can be applied directly. Protocols developed internationally for OMOP can also be brought to Japan, making participation in global collaborative research a realistic possibility. **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. --- This blog is powered by Superblog. Visit https://superblog.ai to know more. --- ## YuiData Reaches 30 Partner Hospitals Across Japan Author: Yuimedi US Author URL: https://us.yuimedi.com/resources/author/yuimedi-us Published: 2026-03-27 Category: news Category URL: https://us.yuimedi.com/resources/category/news Tags: news Tag URLs: news (https://us.yuimedi.com/resources/tag/news) URL: https://us.yuimedi.com/resources/yuidata-reaches-30-partner-hospitals-across-japan ### Yuimedi's real-world data service for pharmaceutical companies has grown to approximately 30 partner hospitals, spanning national university hospitals and regional medical centers across Japan. ![blog2_cover_banner.png](https://prod.superblogcdn.com/site_cuid_cmlztbkr3003v01w1bji1rodl/images/blog2coverbanner-1774730180202-compressed.png) Yuimedi's real-world data service, YuiData, has reached a significant milestone: approximately 30 hospitals across Japan are now contributing clinical data to the network. The list includes national university hospitals, municipal institutions, and regional core hospitals, among them Chiba University Hospital, Nagoya University Hospital, Okayama University Hospital, Tokushima University Hospital, and Ryukyu University Hospital. **Building a Clinical Data Network That Matters** YuiData gives pharmaceutical companies structured access to real-world data drawn directly from electronic health records. This is a meaningful distinction in the Japanese healthcare landscape, where claims-based data has historically been the default. EHR data captures far more of the clinical picture: how diseases present in practice, how patients respond to treatment outside of trial conditions, and how outcomes vary across populations and regions. With 30 institutions now participating, pharmaceutical clients can select specific hospitals and data types tailored to their research or commercial objectives. The result is evidence that is more granular, more current, and more clinically grounded than what legacy data sources have historically offered. **Voices from the Network** Dr. Tetsuo Hirata, Vice President of Ryukyu University Hospital, described the expansion as accelerating the implementation of next-generation medicine, where individual patient data connects meaningfully with Japan's healthcare industry to deliver optimal treatment to each person. Dr. Yoshifumi Wakita of Tokushima University Hospital noted that participation creates an opportunity not just for nationwide research and development, but for exploring early diagnostic indicators, validating treatment outcomes across regional contexts, and supporting new drug discovery. Dr. Shunsuke Doi of Chiba University Hospital was direct: the era of hospitals holding data in silos is over. Contributing statistical information is now a form of social contribution that extends a hospital's impact well beyond its own walls. **What Comes Next** This expansion follows Yuimedi's 400M JPY funding round announced in February 2026, backed by DG Daiwa Ventures, Sumitomo Mitsui Trust Bank, HearstLab, and SMBC Venture Capital. The company will use that investment to grow its YuiData business development team, continue expanding the hospital network, and strengthen its ability to serve pharmaceutical clients with richer, more diverse clinical datasets. Yuimedi's mission remains constant: delivering the right care to the right patients through data. YuiData is one of the clearest expressions of that mission in practice. **About Yuimedi** Yuimedi was founded in November 2020 and is headquartered in Tokyo, Japan, with US operations based in Somerville, Massachusetts. The company builds infrastructure for medical data access spanning data standardization, real-world data services, and AI-powered research tools. --- This blog is powered by Superblog. Visit https://superblog.ai to know more. --- ## What Other Industries Can Teach Healthcare About Data Reuse and Knowledge Management Author: Yuimedi US Author URL: https://us.yuimedi.com/resources/author/yuimedi-us Published: 2026-03-04 Category: Blog Category URL: https://us.yuimedi.com/resources/category/blog Tags: blog Tag URLs: blog (https://us.yuimedi.com/resources/tag/blog) URL: https://us.yuimedi.com/resources/what-other-industries-can-teach-healthcare-about-data-reuse-and-knowledge-management ![image.png](https://prod.superblogcdn.com/site_cuid_cmlztbkr3003v01w1bji1rodl/images/image-1772590130979-compressed.png) ## **Introduction: Healthcare’s Hidden Bottleneck** Despite having some of the most data-rich environments in the world, hospitals often struggle to deliver timely, reliable insights. It’s not just the complexity of EHRs or the fragmentation across clinical, IT, and analytics teams that is causing this. It’s a deeper issue of how knowledge is managed and reused. While other industries treat queries, models, and logic as reusable assets, healthcare’s fragmented data environment and historical focus on billing and compliance have deprioritized collaboration, reuse, and standardization. Logic is often locked in individual heads or local files, and requests for the same metric, like readmission rate, yield subtly different answers depending on who’s pulling the data. This lack of data reuse and knowledge management leads to duplicated work, inconsistent reporting, longer turnaround times, and governance gaps. Industries like finance, tech, and manufacturing offer models that healthcare can adapt. By borrowing proven practices, healthcare can unlock faster insights, greater consistency, and more scalable analytics. ## **From Finance: Treat Queries Like Code** The financial sector runs on trust and traceability. Analysts operate under strict regulatory scrutiny, and every model, query, and report must be reproducible. In this world, queries are treated as code with version control, audit trails, and structured reuse. No analyst at a major bank builds a risk model or portfolio report from scratch every time. Instead, they adapt established logic stored in shared, versioned repositories. **Lesson for healthcare:** Adopt versioned query libraries and encourage refactoring workflows. Whether you’re tracking 30-day readmissions or quality scores, you need a clear, documented trail of logic and a culture where reuse is the norm. ## **From Tech: Codify Institutional Knowledge** In software, institutional knowledge is a core asset. Developers follow the “Don’t Repeat Yourself (DRY)” principle, storing shared logic in centralized codebases and wikis. Repositories like GitHub ensure knowledge is documented, discoverable, and collaborative. When someone solves a technical problem, the solution is documented and shared across the team, accelerating development, improving consistency and allowing teams to build on each other’s work in real time. **Lesson for healthcare:** Establish a central query and cohort definition repository. This becomes the backbone of your analytics knowledge, allowing teams to build on trusted work rather than recreate it. It also future-proofs your organization against turnover and knowledge loss. ## **From Manufacturing: Standardize for Quality** Manufacturers rely on standard operating procedures, process documentation, and template reuse to ensure quality and efficiency. Every production line is designed for repeatability. Errors are flagged early, and continuous improvement is built into the system. Standardization isn’t rigidity, but rather is the foundation for quality control and scalability. **Lesson for healthcare:** Create standardized definitions and queries for high-priority metrics, such as hospital-acquired conditions, length of stay, or ED throughput. When everyone uses the same foundation, you reduce variability, speed up QA, and improve trust in your reports. ## **What’s Different About Healthcare and What to Do About It** Healthcare’s context is unique: regulatory pressure, patient privacy, fragmented systems, and the inherent complexity of clinical data. Adding to the challenge is the divide between clinicians, analysts, and IT staff, each with different languages and incentives. But these barriers can be addressed with the right tools and collaboration models: - Centralization of collective intelligence: Start by establishing a single source of truth for shared logic. When validated definitions and reusable code are stored, versioned, and discoverable, teams can stop wasting time on duplicate effort and start building on each other’s work. - Human-in-the-loop systems: Use tools that help analysts safely refactor and validate queries while preserving clinical intent. Human-in-the-loop workflows help avoid blind automation and build trust in shared queries. - Governance layers: Introduce controlled access, review processes, and lineage tracking for shared queries. - Cross-functional collaboration: Build processes where clinical and analytics teams co-develop and validate definitions together to ensure that logic is both clinically relevant and computationally feasible. ## **Building a Culture of Reuse in Healthcare Analytics** Industries that master reuse don’t do it by accident. They invest in culture, tooling, and process. Healthcare can do the same. Here’s how to start: 1. Audit existing practices: How often are the same queries rewritten? Where is logic duplicated? 2. Establish a knowledge repository: Start with your most-requested metrics or service lines. 3. Define and document standards: Align on how to calculate key KPIs and cohort definitions. 4. Encourage collaboration: Embed documentation and contribution into the team’s workflow. 5. Leverage AI-powered tools: Use smart search or similarity detection to surface reusable logic. The benefits are real: - Faster turnaround on data and reporting requests, even as complexity grows - Reduced analyst fatigue - Stronger data governance and easier audit readiness - Quicker onboarding of new team members - More trust in analytics as teams converge on shared logic and consistent definitions ## **Conclusion** In finance, tech, and manufacturing, reuse is a prerequisite for speed, scale, and trust. These industries have shown that structured knowledge management pays off. Healthcare has more at stake than most. The cost of redundant work and inconsistent logic isn’t just operational—it can impact patient care, reporting accuracy, and compliance. Learn more about how you can operationalize knowledge share and reuse. --- This blog is powered by Superblog. Visit https://superblog.ai to know more. --- ## YuiQuery Research Demo Author: Yuimedi US Author URL: https://us.yuimedi.com/resources/author/yuimedi-us Published: 2026-02-24 Category: Video Category URL: https://us.yuimedi.com/resources/category/video URL: https://us.yuimedi.com/resources/yuiquery-research-demo Are you a clinical researcher or institution with limited statistical analysis resources? Book a demo to learn how Yui Query Research can help. Book a demo --- This blog is powered by Superblog. Visit https://superblog.ai to know more. --- ## Introducing YuiQuery: Precision Extraction for Insights You Can Trust Author: Yuimedi US Author URL: https://us.yuimedi.com/resources/author/yuimedi-us Published: 2026-02-24 Category: Video Category URL: https://us.yuimedi.com/resources/category/video URL: https://us.yuimedi.com/resources/introducing-yuiquery-precision-extraction-for-insights-you-can-trust Are you a clinical researcher or institution with limited statistical analysis resources? Book a demo to learn how Yui Query Research can help. Book a Demo --- This blog is powered by Superblog. Visit https://superblog.ai to know more. --- ## Yuimedi at TechCrunch Disrupt Author: Yuimedi US Author URL: https://us.yuimedi.com/resources/author/yuimedi-us Published: 2026-02-24 Category: news Category URL: https://us.yuimedi.com/resources/category/news Tags: news Tag URLs: news (https://us.yuimedi.com/resources/tag/news) URL: https://us.yuimedi.com/resources/yuimedi-at-techcrunch-disrupt ## **Yuimedi has been chosen to exhibit at TechCrunch Disrupt 2023** ![](https://prod.superblogcdn.com/site_cuid_cmlztbkr3003v01w1bji1rodl/images/image-cp-1771963207544-compressed.jpeg) Yuimedi has been chosen to exhibit at TechCrunch Disrupt 2023 as part of Startup Battlefield 200, the world’s preeminent startup competition. Yuimedi is one of 200 startups selected from a review of thousands of applicants to pitch in front of investors and TechCrunch editors. This year’s Startup Battlefield participants span artificial intelligence (AI), software as a service (SaaS), fintech, security, sustainability, space exploration and more. “We are thrilled to have been selected for this year’s Startup Battlefield 200 and hope we can leverage this opportunity to dive deeper into the healthtech ecosystem and spread the word about Yuicleaner, our solution to automate the preprocessing of medical data.” – Emiri Grimes TechCrunch Disrupt is known for debuting the hottest startups, introducing game-changing technologies, and discussing what’s top-of-mind for the tech industry’s key innovators – and this year will be no different. Past companies launched at Disrupt include Dropbox, Mint, Cloudflare, Fitbit, Yammer and more. For more information on TechCrunch Disrupt 2023 and Startup Battlefield 200 visit the conference’s website [here](https://tcrn.ch/45i0973). Disrupt passes can be purchased [here](https://techcrunch.com/events/tc-disrupt-2023/tickets/?utm_source=sb200&utm_medium=social&utm_campaign=disrupt2023&utm_content=battlefield&promo=battlefield). **Event Overview** Dates September 19-21, 2023 Location San Francisco, CA Event Type In-Person (no virtual/hybrid) **About Startup Battlefield** TechCrunch’s Startup Battlefield 200 is the world’s preeminent startup competition. Startup Battlefield 200 will showcase the top 200 startups from around the globe, across multiple industries. All 200 companies will go through training, have access to masterclasses, private receptions, communities and investor meet and greets. Companies that launched on our stage include Vurb, Trello, Mint, Dropbox, Yammer, Tripit, Redbeacon, Qwiki, Getaround, and Soluto. **About TechCrunch Disrupt** TechCrunch Disrupt is the world’s leading authority in debuting revolutionary startups, introducing game-changing technologies, and discussing what’s top of mind for the tech industry’s key innovators. This year, Disrupt gathers the best and brightest entrepreneurs, investors, hackers, and tech fans virtually and in-person for interviews, demos, Startup Battlefield 200, Networking, and more. Book a Demo --- This blog is powered by Superblog. Visit https://superblog.ai to know more. --- ## Yuimedi Featured in CAHL Monthly Magazine. Author: Yuimedi US Author URL: https://us.yuimedi.com/resources/author/yuimedi-us Published: 2026-02-24 Category: news Category URL: https://us.yuimedi.com/resources/category/news Tags: news Tag URLs: news (https://us.yuimedi.com/resources/tag/news) URL: https://us.yuimedi.com/resources/yuimedi-featured-in-cahl-monthly-magazine ![Yuimedi_logo](https://prod.superblogcdn.com/site_cuid_cmlztbkr3003v01w1bji1rodl/images/yuimedilogo-1771966279734-compressed.png) Yuimedi was featured in the article “The Age of AI Has Arrived” in the monthly magazine published by CAHL (California Association of Healthcare Leaders). The article examines how health tech startups among the companies participating in TechCrunch Disrupt 2023 will play a role in the future of healthcare. Read the article [here](https://ache-cahl.org/articles/the-age-of-ai-has-arrived/) **About CAHL** CAHL is a professional association of healthcare professionals serving Northern and Central California. Its vision is to provide better healthcare services and improve the lives of communities through educational programs, networking and mentoring for healthcare professionals. Book a Demo --- This blog is powered by Superblog. Visit https://superblog.ai to know more. --- ## Yuimedi's selection for Google for Startups was featured in Forbes Asia. Author: Yuimedi US Author URL: https://us.yuimedi.com/resources/author/yuimedi-us Published: 2026-02-24 Category: news Category URL: https://us.yuimedi.com/resources/category/news Tags: news Tag URLs: news (https://us.yuimedi.com/resources/tag/news) URL: https://us.yuimedi.com/resources/yuimedis-selection-for-google-for-startups-was-featured-in-forbes-asia ![GFS_Women Founders Fund_Static Graphic_1](https://prod.superblogcdn.com/site_cuid_cmlztbkr3003v01w1bji1rodl/images/image-cp-1771962988839-compressed.png) Yuimedi was introduced as part of the first Women Founders Fund 2023 cohort along with six other companies.The article introduces the selected companies’ businesses and describes how the fund was established and the expectations for AI in various industries. Click here to read the [article](https://www.forbes.com/sites/johnkang/2023/10/10/googles-asia-pacific-women-founders-fund-picks-first-batch-of-ai-startups/?sh=48869a25a619) **About Forbes Asia** Forbes Asia is the Asian edition of the business magazine founded in the United States in 1917. In addition to the U.S., Asian, and European editions, Forbes Asia is published in 27 countries and regions worldwide. Book a Demo --- This blog is powered by Superblog. Visit https://superblog.ai to know more. --- ## Yuimedi Inc. acquired ISO/IEC 27001:2013 (JIS Q 27001:2014), an international standard for ISMS (Information Security Management System). Author: Yuimedi US Author URL: https://us.yuimedi.com/resources/author/yuimedi-us Published: 2026-02-23 Category: news Category URL: https://us.yuimedi.com/resources/category/news Tags: news Tag URLs: news (https://us.yuimedi.com/resources/tag/news) URL: https://us.yuimedi.com/resources/yuimedi-inc-acquired-isoiec-270012013-jis-q-270012014-an-international-standard-for-isms-information-security-management-system ![yuimedilogo-1771966279734.png](https://prod.superblogcdn.com/site_cuid_cmlztbkr3003v01w1bji1rodl/images/yuimedilogo-1771966279734-original.png) Yuimedi Inc. acquired ISO/IEC 27001:2013 (JIS Q 27001:2014), an international standard for ISMS (Information Security Management System), on July 10, 2023. Yuimedi operates a business that promotes the utilization of medical data under the vision of “building a data utilization infrastructure that updates healthcare”. As a company that operates a business involving medical data, which contains sensitive personal information, Yuimedi has acquired an ISMS certification to ensure that all stakeholders, including medical institutions and pharmaceutical companies, can work with us with peace of mind. We will continue to maintain a high level of information security management based on the certification standards. **Summary of ISMS Certification** Organization Name Yuimedi Inc. Scope of Registration Development and provision of medical data-specific cleansing software Medical data network business (intermediary) Initial Registration Date July 10, 2023 Certification Criteria ISO/IEC 27001:2013 / JIS Q 27001:2014 Registration number JP 23 / 00000244 Certification Body SGS Japan Inc. --- This blog is powered by Superblog. Visit https://superblog.ai to know more. --- ## Yuimedi, a medical data utilization company, raises 450 million yen and becomes the third Japanese team to be selected for the Alchemist Accelerator. Author: Yuimedi US Author URL: https://us.yuimedi.com/resources/author/yuimedi-us Published: 2026-02-23 Category: news Category URL: https://us.yuimedi.com/resources/category/news Tags: news Tag URLs: news (https://us.yuimedi.com/resources/tag/news) URL: https://us.yuimedi.com/resources/yuimedi-a-medical-data-utilization-company-raises-450-million-yen-and-becomes-the-third-japanese-team-to-be-selected-for-the-alchemist-accelerator **Sep 30 2023 – Yuimedi, the developer of a medical data utilization platform, announces that it has raised a total of 450 million yen through a pre-series A private placement of new shares. Chiba Dojo Fund, Nissay Capital, D4V, Coral Capital, DG Daiwa Ventures, and Incubate Fund (an existing shareholder) are the underwriters for this round.** In addition, we have been selected for an acceleration program offered by Alchemist Accelerator, one of the leading accelerators in Silicon Valley. We are the third Japanese team to be selected for this program and will be leveraging this opportunity to enter the U.S. market. ![](https://prod.superblogcdn.com/site_cuid_cmlztbkr3003v01w1bji1rodl/images/image-cp-1771889965595-compressed.webp) In recent years, there has been a growing demand from society to better utilize medical data. It is said that medical data obtained through daily medical procedures called Real World Data (RWD), such as electronic medical records, has the potential to provide value to patients through the provision of more appropriate treatment and drug development. Increasing the utilization of RWD is also becoming a priority for national and local governments as it can contribute to the reduction of medical costs. On the other hand, as the spread of the coronavirus has shown, Japan is lagging behind the world in regards to RWD utilization. One of the blockers for better RWD utilization is that RWD such as electronic medical records are not well-formatted and in the form of “structured data,” and therefore is not designed for data analysis. Currently, the “data cleansing” process to clean up this unstructured data is performed manually, taking an enormous amount of man-hours. To solve this challenge, Yuimedi has developed “Yuicleaner,” a no-code, easy-to-use software for data cleansing that does not require programming knowledge. Yuimedi believes that the creation of cleansed RWD will lead to a more active exchange of RWD between data holders, such as hospitals, and data utilizers, such a pharmaceutical companies, ultimately providing value to patients. In addition to the development and provision of “Yuicleaner,” Yuimedi provides support to hospitals for the construction of medical databases and consultation to pharmaceutical companies on the use of medical data, aiming to build a platform that facilitates the exchange of RWD between data owners and users. To achieve this goal, the company plans to conduct four joint research projects with national hospitals and other organizations, and has about 10 business partners. We believe that there are some areas in the U.S. market related to the utilization of medical data that are more mature than in the Japan market. Therefore, with the goal of reimporting business in the future and with our inclusion into the Alchemist Accelerator in 2023, we have decided to develop our U.S. and domestic business at the same time. The funds raised will be used for platform development in Japan and to build a foundation for U.S. expansion. 【Comments from underwriters (in no particular order)】 Kotaro Chiba, General Partner, Chiba Dojo Fund Chiba Dojo is pleased to announce that we have invested in Yuimedi as lead investor for this round. Yuimedi develops a platform that provides medical data to its clients. Chiba Dojo fully supports Emiri Grimes’ desire to build a company that can compete on a global scale. We believe Yuimedi has the potential to further improve the Japanese economy in the area of medical data. Yuimedi’s team has a strong belief that better data products will lead to a better experience for patients who provide data to the company. Chiba Dojo Fund is also proud to continually support women entrepreneurs with this latest investment in Emiri Grimes and her team. Tomoyuki Kasai, Chief Capitalist, Investment Department, Nissay Capital Corporation Despite the vast amount of medical data being accumulated every day, the unfortunate reality is that it is still not being effectively utilized due to legal and cost issues. Yuimedi’s team of full-stack engineers with extensive product development experience and experts with a wealth of medical expertise can solve this challenge and realize a world where more accurate data and information reach patients and healthcare professionals. We believe that Yuimedi can achieve their mission of “bringing necessary medical care to necessary patients through data”. Fumiaki Nagase D4V（Design for Ventures) , Principal From the very first meeting, we were immediately impressed with Yuimedi: we saw an experienced and confident team, with the required technical capabilities and initial product-market fit. The healthcare industry is eagerly awaiting solutions for a more efficient utilization of clinical data, and Yuimedi is well positioned to greatly contribute to optimal patient healthcare. We believe the company’s service will be an indispensable tool for automated medical data cleansing within the industry. D4V has already started supporting Yuimedi from product design to customer interviews, and we look forward to working together to enable its global business expansion. James Riney Founding Partner & CEO While the healthcare industry has made progress on the digitalization of medical records, the databases are still disparate and lack interoperability. Many of the potential benefits of leveraging this data cannot be realized without proper cleansing to make it useful. Yuimedi is leading the way by bringing together a strong team at the intersection of healthcare and engineering to build solutions to overcome these complex challenges. We’re excited to play a small part in this ambitious and important endeavor. DG Daiwa Ventures, Senior Principal Yuta Sanada, Associate Yuma Nishikawa Yuimedi is a potential global healthcare data player who can enhance the efficiency of developing new treatments/medicines by offering data-cleansing solutions dedicated to healthcare data and insights on data usage. In the bigger picture, DGDV considers what Yuimedi offers would even tackle the global challenge, Medical cost cuts. Yuimedi’s strategy of becoming a healthcare data platformer based on data-cleansing is unique worldwide, and we believe Yuimedi can become an innovator in healthcare data utilization. Yusuke Murata / Co-Founder & General Partner at Incubate Fund Yuimedi provides data cleansing, or so-called ETL tools, specializing in medical big data and creating a system that can be used as real world data via an accumulated DWH. Yuimedi’s biggest advantage is that it has a great team.For more details, please visit our recruitment page, but we have a great team that can compete on a global scale. I have been working with Emiri on the business hypothesis and team building since before the company was founded, and now that it has finally come to fruition, we are ready for this round. We hope that medical information will be implemented in society in the way it should be. 【Comments from CEO and Representative Director, Eimiri Grimes】 When I was a graduate student, I was also engaged in epidemiological research dealing with medical data. One of the issues I felt at the time was that if the necessary data could be easily obtained from the real world in a clean format, research activities could be conducted more quickly. Yuimedi was launched in the hope of overcoming these challenges and creating a world in which medical research using data can flourish. This world needs to be created together with all stakeholders, both owners and users of medical data. Through this fund raising, Yuimedi will unite more stakeholders and strive to realize its mission of “delivering necessary medical care to necessary patients through data”. ＜Profile of Eimiri Grimes＞ Emiri graduated from Kyoto University, Faculty of Pharmaceutical Sciences. After obtaining her pharmacist license, she worked in clinical development at Takeda Pharmaceutical Company. Interested in improving the Japanese healthcare system through the combination of industry, government, and academia, she obtained a Masters in Medical and Industrial Pharmaceutical Sciences from ETH Zurich, Switzerland. Afterwards, she studied managerial perspectives as a consultant at McKinsey & Company. founded Yuimedi Inc. in November 2020 to develop digital solutions related to the intersection of healthcare and data. 【About Yuicleaner】 Yuicleaner is an AI-based, no-code data cleansing software specialized for medical data. It can operate offline and is suitable for cleansing sensitive medical data. It also enables users to trace how data was transformed, enabling data quality control. HP： [https://us.yuimedi.com/product/](https://us.yuimedi.com/product/) 【About Alchemist Accelerator】 A top-tier accelerator headquartered in the U.S. that specializes in seed-stage B2B startups. As of September 2020, over 300 companies have participated in the program with more than 150 companies successfully receiving over $500,000 in investments and more than 35 companies having achieved M&A Exits. HP： [https://www.alchemistaccelerator.com/](https://www.alchemistaccelerator.com/) --- This blog is powered by Superblog. Visit https://superblog.ai to know more. --- ## Yuimedi has been selected for the Google for Startups Women Founders Fund Author: Yuimedi US Author URL: https://us.yuimedi.com/resources/author/yuimedi-us Published: 2026-02-23 Category: news Category URL: https://us.yuimedi.com/resources/category/news Tags: news Tag URLs: news (https://us.yuimedi.com/resources/tag/news) URL: https://us.yuimedi.com/resources/yuimedi-has-been-selected-for-the-google-for-startups-women-founders-fund **We’ve been selected! We’re proud to be selected to join the inaugural cohort of the Google for Startups #WomenFoundersFund, receiving $100,000 in non-dilutive funding and hands-on support from Google experts. We’re excited to embark on this transformative journey with Google’s support for women founders in AI innovation.** Yuimedi continues to make progress toward their mission to automate the preprocessing of medical-grade research data in Japan and around the world through their medical cleansing product, Yuicleaner. With the selection of Google for Startups Women Founders Fund, Yuimedi will accelerate its business promotion in both Japan and the U.S.by ramping up its sales force and executing its product roadmap to introduce generative AI features into its product. ![](https://prod.superblogcdn.com/site_cuid_cmlztbkr3003v01w1bji1rodl/images/image-cp-1771889835726-compressed.jpeg) Google for Startups [Women Founders Fund](https://startup.google.com/programs/women-founders-fund/APAC/) provides non-dilutive cash awards and hands-on support to help selected founders build and grow their businesses. In addition, recipients receive ongoing Google mentorship, Google Cloud credits, and product support to help them navigate every stage of their startup journey. --- This blog is powered by Superblog. Visit https://superblog.ai to know more. --- ## Data Governance is Not Enough — Why Hospitals Also Need Code Governance Author: Yuimedi US Author URL: https://us.yuimedi.com/resources/author/yuimedi-us Published: 2025-06-14 Category: Blog Category URL: https://us.yuimedi.com/resources/category/blog Tags: blog Tag URLs: blog (https://us.yuimedi.com/resources/tag/blog) URL: https://us.yuimedi.com/resources/data-governance-is-not-enough-why-hospitals-also-need-code-governance ![image.png](https://prod.superblogcdn.com/site_cuid_cmlztbkr3003v01w1bji1rodl/images/image-1772590240605-compressed.png) In healthcare, data is both a powerful tool and a significant responsibility. Hospitals rely on accurate, accessible data for patient care, research, and operations. Naturally, robust data governance frameworks are crucial for compliance, data quality, and informed decision-making. Many IT leaders often operate under the assumption that if they have data governance in place, all their data control needs are met. But there’s a blind spot: **how data is actually retrieved and manipulated in code**. Today’s hospital data workflows rely not just on structured databases, but also on SQL queries, Python scripts, Jupyter notebooks, ETL pipelines, and APIs. These tools are used daily by analysts, data scientists, researchers, and even clinicians. Yet most data governance policies stop short of governing the code layer. This post will explain why data governance alone is insufficient, define code governance and its vital role for hospitals, highlight the risks of neglecting it, and show how proper code governance enhances data security, compliance, and operational efficiency. ## **The Gaps in Data Governance** In healthcare, data governance typically refers to the frameworks, roles, and policies that ensure health data is accurate, consistent, secure, and compliant. Organizations often follow well-established models such as: - AHIMA’s Data Governance Framework, which emphasizes data stewardship, quality, and metadata management - HIMSS guidelines, which highlight lifecycle management and clinical alignment - HITRUST CSF, which integrates security and risk management into governance structures These approaches are foundational, and often essential for regulatory compliance and quality reporting. However, they frequently stop short of governing how data is accessed and used in practice. This is the “last mile” problem. Data governance defines _what_ data can be used and _by whom,_ but _how_ that data is extracted, modified, or reported is often left to individual developers or analysts. And in healthcare, _how_ matters deeply. A lack of standardized, audited code practices across an organization creates inconsistencies and blind spots. Without oversight at the code level, even routine queries can introduce silent risks. Consider a research analyst who runs a familiar query to extract lab data for a sepsis study, unaware that it references a table deprecated after the hospital’s migration to LOINC-coded labs. The query still runs, but it silently omits recent data. The resulting report shows a drop in positive blood cultures, which is interpreted as a clinical improvement and shared with leadership. In reality, the data is incomplete. Without code-level governance to flag deprecated tables, outdated logic can quietly mislead even well-intentioned teams. ## **What is Code-Level Governance?** Code-level governance refers to the oversight, standardization, and auditing of how data is accessed and manipulated through code. Unlike traditional data governance, which focuses on metadata, access rights, and source system control, code-level governance addresses the layer where decisions are actually implemented. It’s at this layer that inconsistencies, inefficiencies, or silent failures often emerge, affecting everything from patient cohort definitions to reporting accuracy and system performance. In practice, this includes: - Access Control & Permissions: Restricting who can run, modify, or deploy certain types of code, especially in production environments. - Audit Trails & Versioning: Tracking what code was run, by whom, on what data, and when, supporting compliance and root cause analysis. - Code Analysis & Optimization: Proactively identifying inefficient, risky, or malicious code _before_ it impacts production, preventing performance issues or data integrity problems. - Standardization of Logic & Best Practices: Enforcing consistent practices for filters, joins, naming conventions, and calculations to reduce variation and confusion. - Detection of Risky or Deprecated Code: Identifying queries or scripts that reference outdated tables, inefficient operations, or sensitive fields used inappropriately. ## **Why Hospitals Cannot Afford to Neglect Code-Level Governance** Neglecting code governance in a hospital setting is like leaving the back door open while meticulously securing the front. The risks are substantial: - Uncontrolled Data Access & Security Gaps: increases the risk of PHI exposure, insider threats, and SQL injection vulnerabilities. Code governance enforces access controls and flags risky behavior before it becomes a breach. - Regulatory Noncompliance & Audit Failures: Need to demonstrate who accessed or modified what data. A lack of traceability jeopardizes compliance with HIPAA, GDPR, and internal policies. Code governance provides the visibility needed for due diligence and defensible reporting. - Inaccurate Reporting & Clinical Risk: A single incorrect filter or outdated field can distort a report, leading to the wrong patient cohort, misaligned quality scores, or flawed clinical insights. Governance ensures consistency and alerts teams when risky or deprecated logic is used. - Performance Bottlenecks & System Slowdowns: Inefficient queries can overload production systems and delay time-sensitive reporting. Governance tools proactively identify and block performance-draining logic before it disrupts operations. - Slow Incident Response & Forensics: When a metric suddenly breaks or a report shows unusual results, the lack of version history or query lineage slows down the investigation. With code-level logging, teams can trace issues back to their source quickly, minimizing disruption. These risks don’t always announce themselves with system errors or failed jobs. Often, they take the form of subtle inconsistencies, silent data omissions, or small logic changes that ripple through dashboards and decisions. Over time, these vulnerabilities erode trust, distort insights, and increase the risk of compliance breaches. ## **Implementing Code-Level Governance in a Hospital Setting** Implementing code-level governance is complex, especially with legacy systems and diverse data sources. Key steps include: 1. Assessment: Understand current code practices, risks, and existing controls. 2. Policy Definition: Develop clear policies for code access, development, and auditing. 3. Centralized Query Storage: Establish a central repository where all validated queries and scripts are stored, versioned, and tagged, thus enabling governance at scale. 4. Intelligent Tools: This is where purpose-built tools become invaluable. Solutions like YuiQuery streamline code-level governance by enabling you to define granular metrics, identify deprecated objects, and promote standardization. 5. Training & Education: Educate your developers, analysts, and DBAs on best practices and the new framework. 6. Continuous Monitoring & Improvement: Code-level governance is an ongoing process requiring regular review and adaptation. Integration with Existing Data Governance: Code-level governance complements and strengthens your existing data governance framework. It provides the essential technical enforcement layer, transforming high-level policies into practical, auditable controls at the point of data interaction. ## **Conclusion** While data governance is foundational, code-level governance is the indispensable next step for hospitals striving for true data mastery. The cost of inaction – security breaches, compliance failures, and operational inefficiencies – far outweighs the investment in proactive control. It’s time for healthcare organizations to recognize that true data mastery extends beyond high-level policies and into the very code that interacts with patient data. By embracing code-level governance, hospitals can achieve robust compliance, reliable data, and greater operational efficiency. Assess your current code governance posture today. Learn more \[link to product page\] about how you can promote code-level governance. --- This blog is powered by Superblog. Visit https://superblog.ai to know more. --- ## Why Inconsistent SQL Logic Is Costing You Time and Trust Author: Yuimedi US Author URL: https://us.yuimedi.com/resources/author/yuimedi-us Published: 2025-06-14 Category: Blog Category URL: https://us.yuimedi.com/resources/category/blog Tags: blog Tag URLs: blog (https://us.yuimedi.com/resources/tag/blog) URL: https://us.yuimedi.com/resources/why-inconsistent-sql-logic-is-costing-you-time-and-trust ![image.png](https://prod.superblogcdn.com/site_cuid_cmlztbkr3003v01w1bji1rodl/images/image-1772590178510-compressed.png) ## **Same Logic, Different Results** In today’s competitive landscape, data holds transformational value, and hospitals are no exception. Data-driven decisions can improve patient outcomes, optimize operations, and ensure regulatory compliance. Companies pour resources into data infrastructure, sophisticated analytics tools, and talented data teams, all with the goal of transforming raw information into actionable intelligence. However, there’s an often-unseen risk undermining these advances. Imagine a scenario in which two data analysts calculate hospital readmission rates using slightly different logic: one includes observation stays and planned readmissions, while the other excludes them per CMS guidelines. The result? One team reports a 22% readmission rate, the other 16%. If the higher figure is used in reporting, the hospital could face reimbursement penalties. Worse, internal quality initiatives might focus on the wrong root causes. Without a shared framework for logic and definitions, even well-intentioned work can lead to costly consequences. Discrepancies aren’t always about bad data, a lack of skill, or a flawed tool. More often than not, it’s a hidden inconsistency in the underlying logic used to extract and transform that data, specifically, inconsistent SQL logic. In healthcare, these discrepancies can skew performance metrics, jeopardize reimbursement, and misinform patient care decisions. ## **How Minor SQL Differences Lead to Major Discrepancies** Small differences in how SQL queries are written can lead to significant variations in results, especially in complex environments like healthcare. - **Metric Definitions:** Consider a seemingly straightforward metric like “Length of Stay.” Is it calculated from admission to discharge, or does it exclude transfer days? Does it count partial days? Different interpretations lead to different numbers. - **Join Conditions:** Subtle differences in how tables are joined can drastically alter results. A `LEFT JOIN` versus an `INNER JOIN`, or even using slightly different join keys, can include or exclude entire sets of data, leading to discrepancies that are hard to spot without deep inspection. - **Aggregation Logic:** Variations in how data is aggregated are another common culprit. Is `COUNT(DISTINCT column)` used consistently? How are `NULL` values handled in calculations? Does your “week” start on Sunday or Monday when grouping dates? These seemingly minor choices can lead to wildly different summary statistics. Why does this happen? Often, it’s a byproduct of analysts working in silos, a proliferation of ad-hoc queries, and the absence of a shared, enforced “source of truth” for key definitions and calculations. Without a single, trusted blueprint, every analyst effectively creates their own. These minor differences don’t stay minor for long. They propagate, snowballing into conflicting reports, contradictory dashboards, and ultimately, misinformed business decisions. ## **The Ripple Effect: Rework, Missed Deadlines, and Eroding Stakeholder Confidence** The consequences of inconsistent SQL logic extend far beyond mere numerical differences. **Rework and Wasted Effort:** - **Reconciliation Challenges:** Data analysts spend countless hours painstakingly reconciling conflicting reports instead of generating new insights or tackling high-value analytical problems. - **Duplication of Effort:** Multiple team members independently develop similar SQL queries, unaware that a colleague has already done the same, leading to wasted time and redundant code. - **Chasing Down Differences:** Debugging these discrepancies consumes valuable time and mental energy, pulling skilled professionals away from their core responsibilities. **The Hidden Cost:** - **Project Delays:** Project timelines are delayed as data validation becomes an unexpected and significant bottleneck. - **Decision Paralysis:** Important business decisions are delayed or put on hold until data discrepancies can be resolved, hindering agility and responsiveness. - **Suffering Agility:** When the foundational data is unstable, the entire organization’s ability to react quickly and strategically is compromised. **The Most Damaging Consequence: Eroding Stakeholder Confidence:** “Why are these numbers different?” This frequent, exasperated question is perhaps the most damaging consequence of inconsistent SQL. It directly undermines trust in the data team. - **Skepticism and Reluctance:** Stakeholders begin to doubt the reliability of data outputs, leading to skepticism and a reluctance to act on any insights provided. - **Confusion, Not Clarity:** The data team, instead of being seen as a source of clarity and strategic advantage, risks being perceived as a source of confusion and unreliability. - **Reputational Damage:** This directly impacts the data team’s reputation and its perceived value within the organization, making it harder to secure resources, gain buy-in, and drive impactful initiatives. ## **Quantifying the Cost of Inconsistency** While it’s difficult to put an exact dollar figure on every discrepancy, the costs of inconsistent SQL logic are real and substantial. **Time-Based Costs:** - **Data Analyst Hours:** Data analysts and managers spend valuable hours reconciling discrepancies, re-running queries, and reviewing conflicting reports—time that could be spent on higher-value work. - **Leadership Review Time:** Managers and directors also spend valuable time trying to understand conflicting reports, engaging in debates, and seeking clarification that should be unnecessary. **Resource-Based Costs:** - **Unnecessary Compute Cycles:** Duplicated or inefficient queries often consume redundant compute cycles, leading to higher cloud costs or strained on-premise resources. - **Storage Costs:** Multiple versions of “the same” dataset, each derived from slightly different logic, can lead to unnecessary storage expenditure. **Decision-Making Costs:** - **Clinical and Operational Impact:** Flawed data can lead to misguided initiatives—such as targeting the wrong patient populations or deploying resources inefficiently. - **Financial Consequences:** Inaccurate quality metrics may result in reimbursement penalties or missed incentive payments. - **Intangible Costs:** Over time, poor data reliability erodes trust, hinders strategic planning, and damages the organization’s reputation. ## **Conclusion** Inconsistent SQL logic is not a minor technical nuisance; it is a silent, yet powerful, force undermining your data teams and, more broadly, organizational trust in data itself. It leads to wasted time, missed opportunities, and a sense of doubt that can cripple even the most ambitious data-driven initiatives. The good news is that this problem is solvable. Proactive measures can transform a chaotic data environment, plagued by inconsistencies, into a trusted, single source of truth. Is your data team grappling with these silent inconsistencies? Learn how you can standardize metrics and enforce consistency to build reliable data environments. --- This blog is powered by Superblog. Visit https://superblog.ai to know more. ---