HL7 and SNOMED International announce agreement for Free Set of Terms for use in the International Patient Summary Back 14 Feb 2019 Back Orlando, Fl., Feb. 14, 2019 (GLOBE NEWSWIRE) -- SNOMED International and Health Level Seven International (HL7) announce today the formalization of a license agreement in which a relevant ‘Free for Use’ Set of SNOMED CT coded concepts will be used within the HL7 International Patient Summary (IPS). Health Level Seven International (HL7) is a not-for-profit, ANSI-accredited standards developing organization dedicated to providing a comprehensive framework and related standards for the exchange, integration, sharing and retrieval of electronic health information that supports clinical practice and the management, delivery and evaluation of health services. SNOMED International is a not-for-profit, member-owned and driven international organization charged with maintaining and releasing the SNOMED CT clinical terminology product; the world’s most comprehensive clinical terminology. Presently comprised of 38 Member countries globally, SNOMED International supports the role that structured clinical terminology plays in cross border migration, and by extension the delivery of health care services. An IPS document is an electronic health record extract containing essential healthcare information for use in the unscheduled, cross-border care scenario, as well as for local, regional and other care scenarios. It is international in scope and fully aligned with CEN’s upcoming European Standard for the Patient Summary. The SNOMED CT International Patient Summary (IPS) Free set consists of more than 8,000 terms for use in implementations of the HL7 CDA R2 and FHIR IPS Implementation guides world-wide. Organizations, information systems, or mHealth apps creating or receiving an HL7 IPS may or may not have a SNOMED license. For organizations, regions or countries that have a SNOMED license, the SNOMED CT free set enables easier specification of the patient summary requirements and better interoperability, while for those organizations, regions, or countries that do not have SNOMED licenses, the free set enables them to use the data for care and to store within their EHRs. For some, it may provide a starting point for migration to the use of SNOMED CT. The duration of the agreement is set out for a period of five years over the course of which updates to SNOMED CT content will be made in line with SNOMED International’s release schedule. Click here for more information on the HL7 International Patient Summary, and here for more information about HL7’s CDA standard. Subscribe to SNOMED International news Stay up to date on SNOMED news, features, developments and newsletters by subscribing to our news service. Subscribe

The Federal Public Service Health, Food Chain Safety and Environment is responsible for distributing SNOMED CT in Belgium. Belgium The Federal Public Service Health, Food Chain Safety and Environment is responsible for distributing SNOMED CT in Belgium. Contact Details Federal Public Service Health, Food Chain Safety and Environment Galileelaan 5/2 1210 Brussel België Website: www.health.belgium.be Licensing: https://mlds.ihtsdotools.org/#/landing/BE Email: terminologie@health.belgium.be Appointed Representatives General Assembly: Tom Van Renterghem Member Forum: Katrien Scheerlinck and David Op De Beeck News articles First release of the SNOMED CT common French translation The terminology Centre of the Federal Public Service Health, Food Chain Safety and Environment manages, coordinates, facilitates and promotes the use of terminology in Belgian healthcare. The multidisciplinary team, which is part of the Standards Unit within the Data and Policy Information Department of the Directorate-General for Healthcare, consists of profiles with diverse backgrounds: medical specialists, terminologists, ICT specialists and linguists. Together, they take care of the maintenance, support and release of various terminological standards in Belgium, including SNOMED CT®. These standards are crucial for quality healthcare because they help ensure that healthcare information is recorded and shared unambiguously and only once. Acting as the SNOMED National Release Centre (NRC) of Belgium, the Terminology Centre is responsible for distributing and managing SNOMED CT in Belgium. It also produces translations of the terminology in Dutch and French. The first Belgian SNOMED CT release dates back from March 2018. In the meantime, the content of the Belgian language refset has increased to nearly 580.000 Dutch and 250.000 French validated translations. In terms of coverage, the current Belgian extension contains validated Dutch descriptions for almost 280.000 concepts and validated French descriptions for almost 170.000 concepts. Every six months, a new release is published and made available via MLDS. More information about SNOMED CT in Belgium can be accessed at: https://apps.health.belgium.be/ (Dutch and French only) Affiliated licenses can be obtained at https://mlds.ihtsdotools.org/#/landing/BE Back Learn more Global Patient Set Built from the globally recognized SNOMED CT terminology standard at no cost to users Learn more Get SNOMED CT Information about our license and fee structure Learn more Software and tools We develop and operate applications platforms to support our products and services Learn more Document library Access overviews, guides and specifications Subscribe to SNOMED International news Stay up to date on SNOMED news, features, developments and newsletters by subscribing to our news service. Subscribe

To tackle the challenge of transforming free text into an OMOP-compatible format, we developed dedicated SNOMED-aware clinical language models using open-source Large Language Models (LLMs). Data privacy is safeguarded through on-premise training and deployment. Additionally, a SNOMED Query Builder (SnoQB) was built to help researchers curate and manage SNOMED CT concept sets for querying patient data. These queries are matched against a vector database of patient data built using the clinical language models. Extraction results are stored in the OMOP model's note_nlp table, thereby enabling flexible down-stream analytics. Harmonising these OMOP models across the participating hospitals facilitates data exchange, and positions them to participate in real-world evidence (RWE) studies. The entire pipeline was developed and implemented during a proof-of-concept study across a consortium of Belgian hospitals (AZ Klina, AZorg, AZ Oostende and AZ Delta), led by AZ Klina. In a follow-up validation project (PROZA), the tools developed in this project were validated with a real-world use case focused on the automated screening of osteoporosis indicators in patients‚ medical history, showcasing the successful integration of structured and unstructured data from diverse healthcare sources. This project was funded by the Belgian federal authorities‚ Data Capabilities initiative. Back View Map ibis.ai NLP>OMOP: Transforming Clinical Narratives into Actionable Data Read More Country / Region EMEA Tags Artificial intelligence, Collaboration, Data quality To tackle the challenge of transforming free text into an OMOP-compatible format, we developed dedicated SNOMED-aware clinical language models using open-source Large Language Models (LLMs). Data privacy is safeguarded through on-premise training and deployment. Additionally, a SNOMED Query Builder (SnoQB) was built to help researchers curate and manage SNOMED CT concept sets for querying patient data. These queries are matched against a vector database of patient data built using the clinical language models. Extraction results are stored in the OMOP model's note_nlp table, thereby enabling flexible down-stream analytics. Harmonising these OMOP models across the participating hospitals facilitates data exchange, and positions them to participate in real-world evidence (RWE) studies. The entire pipeline was developed and implemented during a proof-of-concept study across a consortium of Belgian hospitals (AZ Klina, AZorg, AZ Oostende and AZ Delta), led by AZ Klina. In a follow-up validation project (PROZA), the tools developed in this project were validated with a real-world use case focused on the automated screening of osteoporosis indicators in patients‚ medical history, showcasing the successful integration of structured and unstructured data from diverse healthcare sources. This project was funded by the Belgian federal authorities‚ Data Capabilities initiative. Description The scope of this project centers on integrating Natural Language Processing (NLP) with the OMOP Common Data Model (CDM) to convert unstructured clinical narratives into structured, actionable data through the use of SNOMED CT. Scope SNOMED CT was chosen for the NLP>OMOP project due to its comprehensive nature and its status as a global standard for clinical terminology. It effectively handles the wide array of concepts found in unstructured clinical narratives, enabling precise mapping and consistent representation of data. This supports interoperability between healthcare systems, facilitating data sharing across different institutions. SNOMED CT also allows for complex queries, aiding in detailed research and analysis through tools like the SNOMED Query Builder (SnoQB). How SNOMED CT will be used In the NLP>OMOP project, SNOMED CT plays a central role in standardizing and structuring clinical data extracted from unstructured text, such as patient reports. The project develops clinical language models, specifically tailored to recognize and map clinical narratives to SNOMED CT concepts. This ensures that the transformation of free-text data into structured, consistent representations aligns with a globally recognized terminology system, facilitating interoperability and data exchange. Furthermore, the project includes the development of the SNOMED Query Builder (SnoQB), which allows researchers to define and manage SNOMED CT concept sets relevant to their specific research interests. These SNOMED CT concept sets enable precise querying of patient data, effectively translating complex clinical language into a standardized format that can be systematically analyzed and stored in OMOP. Why SNOMED CT will be used Contact More information Learn more Get SNOMED CT Information about our license and fee structure Learn more Learn more Explore the wide range of resources available to our community of practice Subscribe to SNOMED International news Stay up to date on SNOMED news, features, developments and newsletters by subscribing to our news service. Subscribe

In the Netherlands, SNOMED CT concepts are provided with patient-friendly terms and clarifications by the National Release Center and a medical translation agency through an extensive, mostly manual translation project. However, large language models (LLMs) offer a promising alternative for manual translation by enabling the automatic clarification of SNOMED CT terms. In this presentation we present these alternative translation approaches and share the results of a comparative analysis between manually and automatically generated clarifications. Additionally, we will explore methods to employ LLMs to automate steps in the translation process, examine the limitations of different translation pipelines, and discuss the implications for terminology developers and clinical practice. Back View Map Chipsoft Employing Large Language Models to clarify SNOMED CT concepts to patients Read More Country / Region EMEA Tags Artificial intelligence, Collaboration, Innovation, Research, Translation In the Netherlands, SNOMED CT concepts are provided with patient-friendly terms and clarifications by the National Release Center and a medical translation agency through an extensive, mostly manual translation project. However, large language models (LLMs) offer a promising alternative for manual translation by enabling the automatic clarification of SNOMED CT terms. In this presentation we present these alternative translation approaches and share the results of a comparative analysis between manually and automatically generated clarifications. Additionally, we will explore methods to employ LLMs to automate steps in the translation process, examine the limitations of different translation pipelines, and discuss the implications for terminology developers and clinical practice. Description Medical jargon is difficult to grasp for persons with low health literacy, but it is important for patients and their health outcomes to understand their condition and treatment. In the Netherlands, SNOMED CT concepts are provided with patient-friendly terms and clarifications by the National Release Center at Nictiz (The Hague, Netherlands) and a medical translation agency through an extensive, mostly manual translation project. However, large language models (LLMs) offer a promising alternative for manual translation by enabling the automatic clarification of SNOMED CT terms. We carried out a comparative analysis of the clarifications that are currently released in the SNOMED CT Netherlands Edition with clarifications generated by a state-of-the-art large language model (LLM). We limited the scope to diagnoses because this was also the current focus of the translation project from the Netherlands National Release Center. Scope Data in electronic health records (EHRs) are increasingly encoded with SNOMED CT: the comprehensive, international and multilingual healthcare terminology standard that represents health information from various healthcare domains. When clarifications are provided to SNOMED CT concepts, they can thus be used in a wide range of applications and settings. As a reference terminology SNOMED CT can also help to clarify data from other coding systems that are mapped to SNOMED CT. Additionally, tools are available to encode free-text EHR content with SNOMED CT concepts, which then can be used to provide the clarifications to lay end users. How SNOMED CT will be used We will describe the current translation method of Nictiz, that involves translation by a medical translation agency, and validation by terminologists, clinicians and patients. Additionally, we present the results from a study in which we used the large language model (LLM) Gemini 2.0 Flash (from Google, Mountain View, CA, USA), and a prompt based on the requirements from Nictiz about clarifications, to generate clarifications for diagnoses that are registered in hospitals in the Netherlands. A purposeful random sample of the clarifications was validated by two medical doctors. Next, an online survey was carried out with participants from the general population to evaluate the quality of the clarifications and to compare the quality of the clarifications from the sample and from SNOMED CT Netherlands Edition. Finally, we will discuss alternative methods to automate steps in the translation process, examine the limitations of different translation pipelines, and discuss the implications for terminology developers and clinical practice. Why SNOMED CT will be used Contact More information Learn more Get SNOMED CT Information about our license and fee structure Learn more Learn more Explore the wide range of resources available to our community of practice Subscribe to SNOMED International news Stay up to date on SNOMED news, features, developments and newsletters by subscribing to our news service. Subscribe

High-quality clinical terminologies are essential for a connected and efficient healthcare ecosystem. Previous studies have shown the impact of quality issues on downstream applications, such as reduced recall and precision in cohort queries over EHRs. The literature presents several proposals to detect and/or resolve these quality issues. While these proposals typically employ either a lexical, structural, or machine learning-based approach, some combine different techniques to address the issues. This presentation outlines a series of experiments we conducted to improve the quality and clinical accuracy of the SNOMED CT terminology by identifying key areas for enhancement and proposing AI-enabled methods to automate the detection and correction of structural anomalies, such as primitive, misaligned, missing, and redundant concepts. Back View Map IQVIA Addressing SNOMED CT Structural Anomalies Through AI -Enabled Methods Read More Country / Region EMEA Tags Artificial intelligence, Data quality, Research High-quality clinical terminologies are essential for a connected and efficient healthcare ecosystem. Previous studies have shown the impact of quality issues on downstream applications, such as reduced recall and precision in cohort queries over EHRs. The literature presents several proposals to detect and/or resolve these quality issues. While these proposals typically employ either a lexical, structural, or machine learning-based approach, some combine different techniques to address the issues. This presentation outlines a series of experiments we conducted to improve the quality and clinical accuracy of the SNOMED CT terminology by identifying key areas for enhancement and proposing AI-enabled methods to automate the detection and correction of structural anomalies, such as primitive, misaligned, missing, and redundant concepts. Description The experiments we carried out aimed to address structural anomalies, such as primitive, misaligned, missing, and redundant concepts. Primitive concepts are those not fully defined by necessary conditions, making it impossible to automatically classify them or their subtypes into the hierarchy unless a sufficient condition exists for that concept. Intermediate primitives serve as both parents and children within the hierarchy, posing a challenge due to the manual effort they require. Misaligned concepts arise when the modelling of concepts does not follow the template for that sub-hierarchy, likely indicating inconsistent modelling within a sub-hierarchy. The missing concept anomaly refers to gaps where certain concepts that should logically exist within the hierarchy are absent, hindering accurate data representation and retrieval. The redundant concept anomaly occurs when multiple concepts essentially represent the same clinical idea, causing confusion among users, increasing clinical data fragmentation, and complicating the aggregation and analysis of information, along with the extra effort needed to maintain and update the terminology. Our experiments explored AI-enabled approaches, combined with lexical and logical-based techniques, to enhance the quality and clinical accuracy of the SNOMED CT terminology by identifying key areas for improvement and methods to automate the detection and correction of structural anomalies. The presentation will detail these approaches, the methods applied, the tools used, the outcomes achieved, and the lessons learned from the experimentation. A guideline for applying the more promising methods in real-world terminology authoring will also be outlined to conclude. Scope SNOMED CT is a widely used, multilingual clinical healthcare terminology that standardizes medical terms in electronic health records (EHRs). It enables healthcare providers to accurately document patient conditions, treatments, and outcomes using standardized terms, ensuring consistent recording and understanding of clinical information across various systems and locations. This standardization supports the seamless exchange of health information between healthcare providers and systems, facilitating coordinated patient care and public health reporting. Researchers also benefit, as it allows them to query and analyse large datasets of clinical information, leading to more reliable research outcomes through precise cohort identification and data analysis. Meanwhile, previous studies have shown the impact of quality issues on downstream applications. In conclusion, SNOMED CT is the perfect candidate for evaluating new techniques to address quality issues, given its comprehensiveness and crucial role in healthcare. How SNOMED CT will be used The SNOMED CT clinical terminology was chosen as the focus of our experiments aimed at assessing AI-driven methods to enhance its quality and clinical accuracy by automating the detection and correction of structural anomalies. Why SNOMED CT will be used Contact More information Learn more Get SNOMED CT Information about our license and fee structure Learn more Learn more Explore the wide range of resources available to our community of practice Subscribe to SNOMED International news Stay up to date on SNOMED news, features, developments and newsletters by subscribing to our news service. Subscribe

Nourish Care Systems LTD Back Nourish Care Systems LTD Vendor Overview Nourish Care Ltd are an electronic care planning provider. Offering a digital platform to record notes and monitor those within a social care setting. Nourish works with a whole range of care services, including but not limited to: Residential Nursing Homes, Large Care Groups, Learning Disability Services, Mental Health Services. The Nourish platform is both web and app based, and it’s main purpose is to allow carers to record at the point of care, thus eliminating the need for lengthy paper notes and the risk of inaccuracy and inconsistency amongst patient records. One of the most important outcomes of using Nourish is to allow carers to spend less time note taking, and more time providing face-to-face, person-centred care, and allowing carers to record, monitor and analyse the health of those they support much more intricately, to be able to provide them the best care possible. What makes Nourish unique is that it is a truly flexible platform which can be configured to the needs and requirements of your care service. At Nourish, we know that there isn’t a one-size fits all approach to care, which is why the Nourish team will work with you to customise the elements of your platform in a way that works for you, and most importantly, those you support. Recording notes via the Nourish platform allows you store all information in a central location, which can be accessed anywhere at any time, by anyone with the relevant permissions. This significantly reduces the risk of lost patient data, making it a simple and safe way for care teams to work efficiently. With records that are secure and consistent, evidencing care becomes much more manageable and it provides transparency between care teams and managers on a daily basis. Subscribe to SNOMED International news Stay up to date on SNOMED news, features, developments and newsletters by subscribing to our news service. Subscribe SNOMED CT-enabled solutions Within Nourish’s electronic care planning platform you can add new patient data such as medical conditions quickly and easily, via a searchable list. You can then track and monitor medical conditions, allergies and other important information with ease either via Nourish’s analytics feature, or in conjunction with other healthcare services. Scope of services Analytics, Clinical documentation, EHR Downloadable documents Nourish Care - Brochure Office Third Floor, Dean Park House 8-10 Dean Park Crescent Bournemouth Dorset BH1 1HL UnitedKingdom https://nourishcare.co.uk Contact details Nuno Almeida caring@nourishcare.co.uk Regions where operational Europe

In January 2010 Estonia became a Member, joining a global effort to develop, maintain, and enable the use of SNOMED CT in health systems around the world. Estonia In January 2010 Estonia became a Member, joining a global effort to develop, maintain, and enable the use of SNOMED CT in health systems around the world. Contact Details Health and Welfare Information Systems Centre Pärnu mnt 132 11317 Tallinn Website: www.tehik.ee Email: snomed@tehik.ee Licensing: https://mlds.ihtsdotools.org/#/landing/EE Request Management Portal: https://ee-rmp.snomedtools.org/en/ Appointed Representatives General Assembly: Kerli Linna Member Forum: Krista Kärt, Rutt Lindström News articles SNOMED CT is available throughout Estonia for use in electronic health records, health research, and other applications. Adoption of SNOMED CT has already improved data quality and semantic interoperability in pathology, laboratory and nursing domains, and it is also used in European cross-border projects. The Estonian strategy for eHealth includes even wider adoption of SNOMED CT in the Estonian National Health Information System. Health and Welfare Information Systems Centre (TEHIK) runs the SNOMED CT National Release Centre and maintains the Estonian extension of SNOMED CT. The Estonian extension contains: Value sets used in the data exchange with national health information system (distributed as SNOMED CT refsets); Nationally added content which is specific to Estonian use cases; Estonian translations to all concepts that are used in any of the national or cross-border value sets; Estonian translations to the core set of concepts (continuosly expanding). The Estonian extension is available for browsing at the international SNOMED CT Browser website. Distribution files are made available to affiliate license holders. More information about SNOMED CT in Estonia can be accessed here: www.tehik.ee Affiliated licenses can be obtained at https://mlds.ihtsdotools.org/#/landing/EE Back Learn more Global Patient Set Built from the globally recognized SNOMED CT terminology standard at no cost to users Learn more Get SNOMED CT Information about our license and fee structure Learn more Software and tools We develop and operate applications platforms to support our products and services Learn more Document library Access overviews, guides and specifications Subscribe to SNOMED International news Stay up to date on SNOMED news, features, developments and newsletters by subscribing to our news service. Subscribe

A new collaboration between SNOMED International and ICH promotes seamless data exchange in support of public health Back 29 Apr 2019 Back London, United Kingdom & Geneva, Switzerland SNOMED International and the International Council for Harmonisation of Technical Requirements for Pharmaceuticals for Human Use (ICH) are announcing the release of important new maps between global medical terminologies SNOMED CT and MedDRA. This collaborative effort is the first deliverable of a new agreement entered into between SNOMED International and ICH. ICH is an international non-profit organisation which brings together regulatory authorities and pharmaceutical industry from across the globe to discuss scientific and technical aspects of pharmaceuticals and to develop ICH guidelines. Owned by ICH, MedDRA , the Medical Dictionary for Regulatory Activities, is a rich and highly specific standardised medical terminology developed by ICH to facilitate sharing of regulatory information internationally for medical products used by humans and is used for registration, documentation and safety monitoring of medical products both before and after a product has been authorised for use. SNOMED International is the not-for-profit organization that owns and maintains SNOMED CT , the world’s most comprehensive clinical terminology with over 350,000 concepts ranging across diagnosis, signs and symptoms and tens of thousands of surgical, therapeutic and diagnostic procedures. This joint effort has produced two independent maps (MedDRA to SNOMED CT and SNOMED CT to MedDRA) which have been derived from frequently used and key pharmacovigilance MedDRA terms identified from the European Medicines Agency and the UK’s Medicines & Healthcare products Regulatory Agency. In addition, a set of COVID-19 related terms are also included in the first production release of the maps to capture important aspects of the pandemic. The maps are intended to facilitate the exchange of data between regulatory databases (which use MedDRA) and healthcare databases/electronic health records (which use SNOMED CT). In one use case, key pharmacovigilance concepts coded in SNOMED CT in an electronic health record (EHR) could be converted to MedDRA for the purpose of adverse event reporting to regulatory authorities or for the purposes of epidemiological research. In the opposite direction, these same key terms coded in MedDRA representing adverse events, warnings, and other regulatory information could be converted into SNOMED CT so that the information is available in the patient’s record to aid in clinical decision-making. The two maps were created as part of a project involving SNOMED International and ICH entitled WEB-RADR 2 . Funded by the Innovative Medicines Initiative (IMI), a large-scale public-private partnership between the EU and the pharmaceutical industry association, EFPIA, IMI aims to boost biopharmaceutical innovation in Europe and to speed up the development of better and safer medicines for patients. With the creation of the maps from the WEB-RADR 2 project, both SNOMED International and ICH have committed to their ongoing use and maintenance extending past the conclusion of the project. Mick Foy, Medicines and Healthcare products Regulatory Agency UK and Chair of the ICH MedDRA Management Committee said “This is an exciting development and an important milestone. Developing interoperability between SNOMED CT and MedDRA has been a long-standing ambition and will greatly enhance data collection for regulatory purposes and for drug safety research”. SNOMED International CEO, Don Sweete, welcomes the evolution of the organization’s relationship with ICH. “It is exciting to see a long-term alliance borne from a collaborative project created to improve drug safety for patients and citizens. This agreement serves a joint commitment by two organizations dedicated to enabling health systems interoperability across regulatory and clinical continuums.” The Production version of the two maps is being made available to licensed SNOMED CT and MedDRA users on April 30, 2021 and will be based on the January 2021 version of SNOMED CT and the September 2020 version of MedDRA. It is planned that the maps will be released annually in April. To access the maps: Licensed MedDRA users, visit the Downloads page on the MedDRA website Licensed SNOMED CT users visit SNOMED International Visit SNOMED International or MedDRA’s Maintenance and Support Services Organization (MSSO) for map release documents, including: MedDRA-SNOMED CT Mapping Conventions are available here for SNOMED CT users and here for MedDRA users. Criteria for accepting requests for additions or changes to SNOMED CT to MedDRA map and MedDRA to SNOMED CT map is available here for SNOMED CT users and here for MedDRA users Map Change Request Tool (Map CR) and Map CR User Guide For more information on these maps and resources, please contact MedDRA MSSO ( mssohelp@meddra.org ) or SNOMED International ( info@snomed.org ). Subscribe to SNOMED International news Stay up to date on SNOMED news, features, developments and newsletters by subscribing to our news service. Subscribe

IQVIA Back IQVIA Vendor Overview IQVIA (NYSE:IQV) is a leading global provider of advanced analytics, technology solutions and contract research services to the life sciences industry. Formed through the merger of IMS Health and Quintiles, IQVIA applies human data science — leveraging the analytic rigor and clarity of data science to the ever-expanding scope of human science — to enable companies to reimagine and develop new approaches to clinical development and commercialization, speed innovation and accelerate improvements in healthcare outcomes. Powered by the IQVIA CORE™, IQVIA delivers unique and actionable insights at the intersection of large-scale analytics, transformative technology and extensive domain expertise, as well as execution capabilities. With more than 58,000 employees, IQVIA conducts operations in more than 100 countries. Subscribe to SNOMED International news Stay up to date on SNOMED news, features, developments and newsletters by subscribing to our news service. Subscribe SNOMED CT-enabled solutions A series of consulting and software solutions (e.g., privacy, NLP, AIML and other SaaS) that lever SNOMED CT and other reference terminologies across countries to provide insights to patient health journeys and outcomes. Scope of services Analytics Downloadable documents Office 210 Pentonville Road London N1 9JY UnitedKingdom iqvia.com Contact details Ben Hughes SVP 07500121313 ben.hughes@iqvia.com Regions where operational Global

IHTSDO Adopts Trading Name of SNOMED International Back 31 Dec 2016 Back Subscribe to SNOMED International news Stay up to date on SNOMED news, features, developments and newsletters by subscribing to our news service. Subscribe

Kakao Healthcare developed the Healthcare data Research Suite (HRS) platform to support multi-institutional research by enabling semantic interoperability through terminology standardization using SNOMED CT. However, manual mapping of local terms to SNOMED CT is labor-intensive and prone to inconsistency. To overcome these challenges, we developed and evaluated "Chipmunk‚Äù, a large language model (LLM)-based tool that automates SNOMED CT mapping and supports the authoring of new concepts through post-coordination. The automated mapping process involved preprocessing local terms, applying syntactic matching, conducting vector similarity searches with an embedding model, and continuously enriching the reference terms through iterative incorporation of verified mapping results. New concepts were authored via a structured post-coordination process. Performance was evaluated using diagnostic and surgical procedural terms from four hospitals in South Korea with usability feedback from expert clinical terminologists. After adding reference terms, accuracy in the diagnostic domain ranged from 89.69% to 98.67%, while in the surgical procedural domain, it ranged from 82.62% to 99.19%. The tool also reduced mapping time by 47% and human resource usage by 70%. Users reported reduced effort, decreased errors, and improved usability. Chipmunk demonstrated strong accuracy and efficiency in real-world SNOMED CT mapping by integrating pre- and post-coordination into a unified workflow. However, challenges remain with complex surgical procedures, ambiguous terms, and abbreviations, highlighting the need for ongoing expert review. Future directions include integrating knowledge graph-based retrieval and enhanced validation features. Despite advances in automation, human expertise remains essential for ensuring precise and contextually accurate terminology mapping. Back View Map Kakao Healthcare (2 of 5) Improving Clinical Data Interoperability: An LLM-Based Approach to Mapping Local Terms to SNOMED CT Read More Country / Region APAC Tags Artificial intelligence, Implementation, Innovation, Mapping, Pre/postcoordination, Tooling Kakao Healthcare developed the Healthcare data Research Suite (HRS) platform to support multi-institutional research by enabling semantic interoperability through terminology standardization using SNOMED CT. However, manual mapping of local terms to SNOMED CT is labor-intensive and prone to inconsistency. To overcome these challenges, we developed and evaluated "Chipmunk‚Äù, a large language model (LLM)-based tool that automates SNOMED CT mapping and supports the authoring of new concepts through post-coordination. The automated mapping process involved preprocessing local terms, applying syntactic matching, conducting vector similarity searches with an embedding model, and continuously enriching the reference terms through iterative incorporation of verified mapping results. New concepts were authored via a structured post-coordination process. Performance was evaluated using diagnostic and surgical procedural terms from four hospitals in South Korea with usability feedback from expert clinical terminologists. After adding reference terms, accuracy in the diagnostic domain ranged from 89.69% to 98.67%, while in the surgical procedural domain, it ranged from 82.62% to 99.19%. The tool also reduced mapping time by 47% and human resource usage by 70%. Users reported reduced effort, decreased errors, and improved usability. Chipmunk demonstrated strong accuracy and efficiency in real-world SNOMED CT mapping by integrating pre- and post-coordination into a unified workflow. However, challenges remain with complex surgical procedures, ambiguous terms, and abbreviations, highlighting the need for ongoing expert review. Future directions include integrating knowledge graph-based retrieval and enhanced validation features. Despite advances in automation, human expertise remains essential for ensuring precise and contextually accurate terminology mapping. Description Research Aim and Scope This research aims to develop and evaluate Chipmunk, a LLM-based SNOMED CT automatic mapping tool designed for multi-institutional research. The scope encompasses the methodology for developing the tool, its implementation in a real-world environment, and a comprehensive evaluation‚Äîincluding both performance (accuracy) and usability assessments. The evaluation was conducted using diagnostic and surgical procedural terms collected from four South Korean hospitals. In addition, special attention is paid to the challenges of automatic mapping when standardizing clinical terminologies in multi-center settings. Anticipated Impact The system offers several key improvements: (1) it enhances resource and time efficiency through advanced preprocessing‚Äîsuch as Korean-to-English translation of local terms, abbreviation handling‚Äîand by applying curation rules, thereby significantly reducing repetitive manual tasks; (2) it improves the quality and accuracy of mapping by leveraging automated mapping results, reducing human errors and increasing precision; (3) it increases consistency by minimizing variability between different mappers that often occurs with manual mapping; (4) it supports an efficient workflow by enabling both pre-coordinated and post-coordinated mapping within a single process; and (5) it further advances usability with an intuitive, user-friendly interface‚Äîsimilar to Excel‚Äîcompared to traditional methods. Ultimately, by using Chipmunk, large-scale mapping projects such as multi-institution terminology standardization can be performed more easily, quickly, accurately, and efficiently, thereby ensuring semantic interoperability and facilitating the meaningful use of clinical data. Scope 1. Global Standard Terminology System: SNOMED CT is globally adopted, ensuring international standardization and interoperability. 2. Comprehensive Reference Terminology: It covers various medical domains, ensuring broad consistency in healthcare data. 3. Support for Post-Coordination: It enables creating new concepts by combining existing terms, providing flexibility. 4. Rich Attribute Relationships and Hierarchical Structure: Its detailed structure allows for sophisticated and precise data querying. 5. Standard Terminology System for HRS: It is the chosen standard for multi-institutional data standardization within Kakao Healthcare's HRS platform. How SNOMED CT will be used SNOMED CT serves as the standard terminology system for multi-institutional data standardization in the Healthcare data Research Suite (HRS) developed by Kakao Healthcare. Here are the key ways SNOMED CT is utilized: 1. System Architecture and Implementation The automatic mapping process begins with setting up a mapping project and includes two main steps: auto-mapping and new concept authoring. Final mappings are integrated into a database as the local term-SNOMED CT mapping table. Both mapping results and new SNOMED CT concepts are added to the reference term database, which holds previous mapping data and extensions. A terminology review committee, along with the mapping team, reviews and cross-validates these results 1.1 Auto-mapping Method The overall automated mapping process, as illustrated in Figure 2. Key steps include: * Preprocessing: Refine local terms by removing unnecessary symbols, expanding abbreviations, and translating non-English terms into English using tools like Google Cloud and OpenAI API. * Syntactic Mapping: Local terms are mapped to exact text matches in SNOMED CT and reference databases, with search results restricted by semantic tags to ensure precision and efficiency. Consistency is maintained by applying mappings across duplicate terms while taking into consideration any domain differences. * Vector Similarity Mapping: Vector similarity search is used with reference terms generated from SNOMED CT descriptions and mapped terms, embedding these terms with the text-embedding-3-small model and storing the results in the FAISS Vector DB for fast retrieval. * Reference Term Integration and Improvement: Figure 3 illustrates the process of generating reference terms. Finalized mappings are added to the reference term set for ongoing refinement, with duplicate terms removed and unique ones undergoing vector embedding. This process uses an iterative approach, continuously enhancing tool performance across multiple institutions. 1.2 Authoring New Concepts Chipmunk facilitates the authoring of new SNOMED CT concepts through a structured post-coordination process * Focus Concept Selection: Begin with choosing a focus concept, aided by proximal primitive concept retrieval to ensure accurate supertype inference and minimize errors. * Concept Modeling with MRCM: Model the concept following MRCM rules, utilizing guided naming to prevent errors. Chipmunk restricts attribute selection based on domain, ensuring only relevant ones are available, and narrows permissible values using ECL queries. * Classification and Validation: These are performed to maintain precision and consistency, using tools like the SNOMED Release Validation Framework and the OWL Toolkit. * Integration: Newly authored concepts are integrated into a mapping table, structured to include the source terminology and corresponding SNOMED CT identifiers. 2. System Evaluation * Dataset: Data from four South Korean medical centers provided a diverse set of diagnostic and surgical procedural codes, ensuring comprehensive evaluation aligned with SNOMED CT semantic tags. * Accuracy Evaluation: Accuracy was tested by comparing automated results to verified manual mappings, using top-1‚ and top-5 metrics. Performance was assessed both within individual hospitals and with added reference terms from others. * Efficiency Evaluation: Baseline manual processes were timed and then compared to the tool-enabled processes to measure time savings and resource optimization. Why SNOMED CT will be used Contact More information Learn more Get SNOMED CT Information about our license and fee structure Learn more Learn more Explore the wide range of resources available to our community of practice Subscribe to SNOMED International news Stay up to date on SNOMED news, features, developments and newsletters by subscribing to our news service. Subscribe

The SNOMED CT Web Series is a collection of scheduled webinars showcasing the achievements of the SNOMED CT community of practice across clinical, research and implementation domains. Registration is open to all and there is no cost to attend. SNOMED CT Web Series The SNOMED CT Web Series is a collection of scheduled webinars showcasing the achievements of the SNOMED CT community of practice. Registration is open to all and there is no cost to attend. Implementation Web Series Clinical Web Series Research Web Series Implementation Web Series The Implementation Web Series addresses pertinent topics related to SNOMED CT implementation. Determined by the topic, presentations will be held by representatives from the SNOMED CT community, staff members, or relevant external parties. Upcoming implementation webinars Topic: Building Questionnaires with SNOMED CT and FHIR Date: March 09, 2026 (13:00-14:00 UTC) More information: https://tockify.com/snomed/detail/581/1773061200000?startms=1772323200000 ------------------------------- Topic: Implementing the LOINC Ontology Date: Apr 20, 2026 (13:00-14:00 UTC) More information: https://tockify.com/snomed/detail/587/1776690000000?startms=1775001600000 ------------------------------- SCROLL TO SEE MORE Watch past webinars Clinical Web Series The Clinical Web Series showcases clinical real-world experiences working with SNOMED CT. Upcoming clinical webinars There are currently no scheduled Clinical Web Series sessions. SCROLL TO SEE MORE Watch past webinars Research Web Series The Research Web Series showcases distinguished researchers from the SNOMED CT community and their current research leveraging SNOMED CT. Upcoming research webinars Topic: A Calibrated Class-Weighted Ensemble Framework for Relationship Classification in SNOMED CT with Domain-Specific BERT Models Date: March 25, 2026 (17:00-18:00 UTC) More information: https://tockify.com/snomed/detail/588/1774458000000?startms=1772323200000 ------------------------------- SCROLL TO SEE MORE Watch past webinars Future presenters Interested in participating as a Web Series presenter? Contact us. SNOMED CT Expo 2026 The SNOMED CT Expo unites clinical terminology SMEs from around the world Learn more Get involved Find out how you can get involved Learn more Subscribe to SNOMED International news Stay up to date on SNOMED news, features, developments and newsletters by subscribing to our news service. Subscribe