This project focuses on the principles of mapping clinical terms to standard terminology for a multi-institutional research platform. A hybrid mapping approach, combining automated and manual methods, ensures accurate and consistent terminology alignment. Strict mapping guidelines and 1:1 mapping principles are implemented to address terminology variations and data format differences. This study highlights the critical importance of precise terminology mapping for reliable multi-institutional research. Back View Map Kakao Healthcare (1 of 5) Mapping Clinical Terms to Standard Terminology for Multi-institutional Research Platform: Mapping Principles Read More Country / Region APAC Tags Implementation, Mapping, Tooling This project focuses on the principles of mapping clinical terms to standard terminology for a multi-institutional research platform. A hybrid mapping approach, combining automated and manual methods, ensures accurate and consistent terminology alignment. Strict mapping guidelines and 1:1 mapping principles are implemented to address terminology variations and data format differences. This study highlights the critical importance of precise terminology mapping for reliable multi-institutional research. Description Key aspects include: Hybrid Mapping Process: Combines automated and manual methods to ensure semantic consistency and compliance with global standards. * Validation & Expert Involvement: Cross-validation and expert consultations resolve mapping discrepancies and inactive concept issues. Mapping Strategies: Establishes mapping principles for LOINC, RxNorm, and SNOMED CT by identifying the characteristics of each terminology and analyzing the current state of local terms, in order to develop practically applicable mapping guidelines. Implementation & Outcomes: Enhances mapping accuracy, improves cohort generation, and facilitates multi-institutional research through a user-friendly platform with advanced code search capabilities. Challenges & Future Directions: Addresses inter-institutional inconsistencies and SNOMED CT limitations while highlighting the need for trained terminology specialists in Korea. Future improvements include advanced search options and attribute-based retrieval for better usability. Ultimately, this study provides a scalable strategy for clinical terminology mapping, fostering efficient multi-institutional research and supporting global healthcare innovation. Accuracy, recall, and other performance metrics are used to compare the proposed method with traditional rule-based standardization approaches. Scope SNOMED CT is utilized in this study as the primary standard terminology system for mapping clinical terms, particularly for diagnosis, chief complaints, treatment procedures, measurements, and medical history. It is employed to ensure semantic consistency and interoperability across institutions. Key Usage of SNOMED CT in the Mapping Process: 1. Standardization & Interoperability: * SNOMED CT is selected for domains where no widely accepted terminology exists due to its robust modeling capabilities. * It provides a structured approach to ensure semantic equivalence in mapped clinical terms. 2. Hybrid Mapping Approach (Automated + Manual): * Automated Mapping: * Chipmunk, Kakao Healthcare’s in-house terminology tool, uses vector similarity to map local terms to SNOMED CT. * Manual Mapping: * Clinical experts review and correct automated mappings, ensuring accuracy and consistency. * Terms without direct SNOMED CT matches undergo post-coordination, where new concepts are created. 3. New Concept authoring & Validation: * If pre-coordinated concepts are unavailable, Chipmunk supports the post-coordination process to generate new SNOMED CT concepts. * Attributes and values are refined within their domains, and validation is performed using the Machine Readable Concept Model (MRCM). * Concepts are assigned a Kakao Healthcare extension namespace (1000305) and updated biannually. 4. Mapping Guidelines & Constraints: * SNOMED CT Editorial Guidelines are followed, but internal rules are created for cases not covered. * 1:1 mapping is preferred to ensure data consistency in multi-institutional research. * Uses domain-specific semantic restrictions, attribute prioritization for post-coordination, and tailored mapping approaches. * Many-to-one mapping is allowed for refined local codes (e.g., multiple diagnosis terms mapped to a single SNOMED CT concept). By implementing SNOMED CT in this way, the study ensures semantic integrity, interoperability, and reliable data alignment across institutions, ultimately supporting multi-institutional research and healthcare innovation. How SNOMED CT will be used SNOMED CT was chosen as the primary standard terminology system in this study due to its comprehensive coverage, structured modeling capabilities, and global acceptance. The selection was driven by several key factors: * SNOMED CT provides a broad and detailed representation of clinical concepts, covering diagnoses, chief complaints, procedures, medical history, and measurements. * Multi-institutional research requires a standardized terminology that enables consistent data integration across different medical centers. * Unlike some terminologies with limited predefined terms, SNOMED CT allows post-coordination, enabling the creation of new concepts when necessary. This flexibility was crucial for ensuring the granularity needed in multi-institutional research * SNOMED CT is widely recognized and used internationally, making it an ideal choice for a research platform aiming for global compatibility. * Using SNOMED CT enhances data retrieval and searchability, facilitating efficient cohort identification for research. * The medical field evolves rapidly, and SNOMED CT provides a regular update mechanism, ensuring that new concepts can be added as medical knowledge advances. SNOMED CT was selected for its robustness, flexibility, and ability to ensure semantic consistency across institutions. Its structured approach to concept representation, interoperability, and scalability makes it an essential component of the HRS (Healthcare data Research Suite) platform and UDM model (Universal Data Model by Kakao Healthcare), supporting efficient multi-institutional research and healthcare innovation. 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In support of SNOMED International's strategy to serve as a Terminology Hub for Integration, SNOMED International has collaborated with Syadem to represent the NUVA Ontology as a SNOMED CT-compatible extension, delivered in RF2 format. The NUVA Ontology is a dedicated global vocabulary for representing on the long term administered vaccines concepts, gathering data from paper records or any digital format, regardless of when and where the vaccine was administered and how precisely it was recorded. It is intended to act as pivot format across all existing vaccine codifications, informed by a functional description of each vaccine concept through the notion of valences. Officially launched on May 9 2025 at the International Summit on Vaccine Coding and Standards in Bordeaux, this extension enables transparent semantic interoperability by supporting dual code identifiers. This approach allows NUVA concepts to leverage the advanced querying capabilities of SNOMED CT, including the Expression Constraint Language (ECL), and facilitates the integration of a broad range of internationally available branded vaccines. This presentation will outline the technical and collaborative work behind the extension, demonstrate its practical applications, and describe planned next steps for deeper integration‚such as aligning vaccine data models, enabling multilingual translations, and mapping to other vaccine code systems. Back View Map Syadem The NUVA Vaccine Ontology Extension to SNOMED CT Read More Country / Region EMEA Tags Collaboration, Implementation, Mapping In support of SNOMED International's strategy to serve as a Terminology Hub for Integration, SNOMED International has collaborated with Syadem to represent the NUVA Ontology as a SNOMED CT-compatible extension, delivered in RF2 format. The NUVA Ontology is a dedicated global vocabulary for representing on the long term administered vaccines concepts, gathering data from paper records or any digital format, regardless of when and where the vaccine was administered and how precisely it was recorded. It is intended to act as pivot format across all existing vaccine codifications, informed by a functional description of each vaccine concept through the notion of valences. Officially launched on May 9 2025 at the International Summit on Vaccine Coding and Standards in Bordeaux, this extension enables transparent semantic interoperability by supporting dual code identifiers. This approach allows NUVA concepts to leverage the advanced querying capabilities of SNOMED CT, including the Expression Constraint Language (ECL), and facilitates the integration of a broad range of internationally available branded vaccines. This presentation will outline the technical and collaborative work behind the extension, demonstrate its practical applications, and describe planned next steps for deeper integration‚such as aligning vaccine data models, enabling multilingual translations, and mapping to other vaccine code systems. Description The scope is a complete representation of the NUVA Ontology as a published SNOMED CT Extension. Scope SNOMED CT has significant market penetration and a number of major vendors already support the ingestion of SNOMED CT into their platforms. By aligning with this format and becoming a part of a SNOMED CT installation, NUVA avoids barriers to adoption of implementers having to learn a new file format, semantics and hold a separate CodeSystem in their product. How SNOMED CT will be used Although NUVA is a stand alone onology published in its own right, when packaged as an extension to the International Edition of SNOMED CT, the product becomes completely integrated with the International Edition, using SNOMED CT concepts to represent relationships between concepts, both with the NUVA vaccine hierarchy itself, and in its connections to SNOMED CT concepts. The SNOMED CT file format - RF2 - is used to deliver the ontology in a computer friendly format, and SNOMED conventions are used to convey concept terms and properties. 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

This presentation outlines our implementation of a syntactic and systematic validation process designed to significantly enhance mapping accuracy and consistency across multiple healthcare institutions. During the mapping process, several factors contributed to inconsistencies, necessitating the need for our validation procedures. For instance, human errors during the manual mapping process led to inaccuracies. Additionally, when multiple mappers work concurrently, the same local terms can inadvertently be mapped to different standard codes. Consequently, we identified conflicts and nonuniformities resulting from the mapping of identical local terms to different SNOMED CT concepts within and across institutions. By systematically addressing challenges such as duplicate local terms and mapping conflicts, our approach leverages the comprehensive framework of SNOMED CT to ensure the precise and consistent use of standard terminology in multi-institutional data standardization projects. Back View Map Kakao Healthcare (3 of 5) Systematic Validation in Multi-Institutional Clinical Data Standardization: A Syntactic and Semantic Approach Read More Country / Region APAC Tags Data quality, Implementation, Mapping, Research This presentation outlines our implementation of a syntactic and systematic validation process designed to significantly enhance mapping accuracy and consistency across multiple healthcare institutions. During the mapping process, several factors contributed to inconsistencies, necessitating the need for our validation procedures. For instance, human errors during the manual mapping process led to inaccuracies. Additionally, when multiple mappers work concurrently, the same local terms can inadvertently be mapped to different standard codes. Consequently, we identified conflicts and nonuniformities resulting from the mapping of identical local terms to different SNOMED CT concepts within and across institutions. By systematically addressing challenges such as duplicate local terms and mapping conflicts, our approach leverages the comprehensive framework of SNOMED CT to ensure the precise and consistent use of standard terminology in multi-institutional data standardization projects. Description Our project targets the challenges encountered in multi-institution clinical data standardization efforts, where discrepancies in mapping local terms to SNOMED CT concepts frequently occur. These discrepancies result from the involvement of multiple mappers who apply diverse mapping criteria, eventually leading to various conflicts such as the same local terms being mapped to different concept IDs. For example, the local term 24HRS AMBULATORY BP was mapped to two different concepts, 164783007 |Ambulatory blood pressure recording (procedure)| and 170599006 |24 hr blood pressure monitoring (regime/therapy)|. To tackle these issues, we developed an automated systematic validation process comprising both syntactic and semantic validation that performs two levels of verification: within institutions and across institutions 1. The principle of within/across institutional systematic validation : Local terms that are literally identical or equivalent in meaning are consistently mapped to the same SNOMED CT concept. 2. Syntactic Validation (1) Verify that duplicate local terms are mapped to the same SNOMED CT concept. (2)Check if local terms, which exactly match to SNOMED CT descriptions, are assigned to concepts other than the corresponding SNOMED CT concepts. 3. Semantic Validation (1) Semantic tag: In addition to syntactic validation, semantic validation was performed to verify whether the meanings are identical. Any mappings that deviated from our predefined semantic tags were subsequently revised to align with the appropriate SNOMED CT concepts. (2) The results of the syntactic validation were manually reviewed one by one to ensure that they were consistently mapped based on their semantic meaning. Scope 1. Extensive Coverage We are mapping concepts across multiple fields, and SNOMED's extensive coverage, diagnoses, procedures, examinations, and medications demonstrates its versatility as a unified terminology system. This broad coverage is a significant advantage in our standardization efforts because it enables us to integrate diverse data from different institutions into a single unified system. 2. Detailed Modeling One of the primary strengths of SNOMED CT is its detailed, well-organized modeling of each concept, which incorporates both core attributes and subtle semantic distinctions. This enables users to quickly and accurately understand the exact clinical meaning of each concept‚ a critical factor when mapping diverse local terms from multiple institutions to a unified SNOMED CT concept. By leveraging these comprehensive models, our mapping process achieves enhanced semantic precision and accuracy, resulting in fewer conflicts and more consistent data integration across institutions. How SNOMED CT will be used To cover a diverse range of medical terms, we selected SNOMED CT because it supports the largest scope of local terms available. Utilization of semantic tag In order to maintain mapping consistency, we restricted semantic tags for each value set. Our automated mapping tool applied these semantic tag restrictions, and any mappings that fall outside the defined range were flagged during the manual review process. For instance, in our implementation, we constrained the semantic tags for DiagnosisCodes to include only "disorder," "finding," "situation," "event," and "person." Similarly, for ProcedureCodes as well as ExamCodes, we limited the semantic tags to "procedure" and "regime/therapy." This structured approach to semantic tags ensures that our mapping processes not only rely on literal matches but also incorporate semantic equivalence, ultimately guaranteeing semantic interoperability while upholding a high level of consistency and accuracy across various institutions and value sets. Utilization of synonyms for each description SNOMED leverages the fact that a single concept can have multiple synonyms to enhance the accuracy of mapping within institutions. Even if different institutions use different local terms to represent the same concept, as long as they share the same synonym in SNOMED, they will be mapped to the same SNOMED concept, thereby maintaining consistency. 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This presentation introduces the XiA project's innovative framework for delivering continuous professional development (CPD) in digital health interoperability, with a focus on standards used in European Electronic Health Record Exchange Format (EEHRxF), such as SNOMED CT. Central to the approach is the XiA Library, a curated repository of Microcontent Learning Blocks (MLBs) targeted, modular content designed to be combined into tailored learning pathways. These pathways support professionals across the EU in preparing for the European Health Data Space (EHDS) through flexible, engaging, and accredited training. The model promotes scalable, self-paced learning adapted to the needs of various stakeholder groups, including healthcare providers, developers, and decision-makers. Back View Map IHE Europe (2 of 2) An innovative approach to Continuous Professional Development on Digital Health Interoperability: Insights from the XiA Project Read More Country / Region EMEA Tags EHDS (European Health Data Space), Global/International, Innovation, Research This presentation introduces the XiA project's innovative framework for delivering continuous professional development (CPD) in digital health interoperability, with a focus on standards used in European Electronic Health Record Exchange Format (EEHRxF), such as SNOMED CT. Central to the approach is the XiA Library, a curated repository of Microcontent Learning Blocks (MLBs) targeted, modular content designed to be combined into tailored learning pathways. These pathways support professionals across the EU in preparing for the European Health Data Space (EHDS) through flexible, engaging, and accredited training. The model promotes scalable, self-paced learning adapted to the needs of various stakeholder groups, including healthcare providers, developers, and decision-makers. Description The Xpanding Innovative Alliance (XiA) project, launched in February 2025 and co-funded through the Erasmus+ Programme, addresses the digital skills gap in healthcare by developing a scalable, interactive education framework to support the implementation of interoperability standards in the context of the European Health Data Space (EHDS). Targeting healthcare professionals, digital health developers, and decision-makers, XiA promotes co-creation through the Quadruple Helix Model, connecting academia, industry, government, and civil society. Its methodology combines Trialogical Learning with Microlearning principles to deliver flexible and engaging continuous professional development (CPD). At the heart of the approach is the XiA Library‚ repository of Microcontent Learning Blocks (MLBs), which are structured at different levels of depth and can be assembled into tailored, accredited learning pathways. These can be system-recommended or individually composed by educators, supporting personalized, self-paced, and scalable training that aligns with academic standards and evolving EU policy needs. The initiative spans 15 EU countries, building communities of practice and fostering a sustainable network of digital health change agents. Scope SNOMED CT is an integral part of the set of complementary international standards underpinning the European Electronic Health Record Exchange Format (EEHRxF), alongside terminologies such as LOINC, ICD, HL7 FHIR and other. Its inclusion in the XiA project reflects its critical role in enabling semantic interoperability, clinical decision support, and data reuse across health systems. As SNOMED CT adoption expands across EU Member States, there is a growing need for structured, accessible training to ensure professionals across clinical, technical, and policy domains are equipped to implement and use it effectively. How SNOMED CT will be used SNOMED CT is included as content and practical framework of the CPD program, grouped in family of microcontent learning blocks that can be pilled and rearranged according to the aim of the training. Participants engage with SNOMED CT through structured activities including concept navigation, mapping exercises, term modeling, and the understanding of SNOMED CT reference sets. Materials are also adapted to the stakeholder to increase engagement. 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

Belgium has designated the Systematized Nomenclature of Medicine Clinical Terms (SNOMED CT) as the recommended clinical reference terminology for clinical information systems. The use of a common, unambiguous terminology has the potential to improve data quality and patient safety by capturing clinical data in a standardized manner. In our academic hospital, we adopted a new electronic health system in 2020, and at the same time, we implemented SNOMED CT in various parts of our electronic health record. As a result, we transitioned overnight from a free-text system to one where data is 100% structured in certain sections. For the users, this represented a significant disruption to their clinical practices. Essential parts of the patient record, such as the problem list and medical history, could no longer be entered as free text but required selecting a term from a list of sometimes unfamiliar terms. How can we address this major shift in the use of the patient record? There is little documentation explaining how best to support users during this transformation. We would therefore like to share our experience of this transition, the strategies we implemented, and the challenges we faced. This presentation will be structured around four key areas: communication between end-users and the terminology team, data quality, data relevance, and the importance of structured data. Back View Map Cliniques Universitaires Saint-Luc (2 of 2) From free-text to 100% structured data : how to improve compliance of end-users and adapt SNOMED CT to clinical practice ? Experiences of an academic hospital Read More Country / Region EMEA Tags Clinical Practice, Data quality, Implementation Belgium has designated the Systematized Nomenclature of Medicine Clinical Terms (SNOMED CT) as the recommended clinical reference terminology for clinical information systems. The use of a common, unambiguous terminology has the potential to improve data quality and patient safety by capturing clinical data in a standardized manner. In our academic hospital, we adopted a new electronic health system in 2020, and at the same time, we implemented SNOMED CT in various parts of our electronic health record. As a result, we transitioned overnight from a free-text system to one where data is 100% structured in certain sections. For the users, this represented a significant disruption to their clinical practices. Essential parts of the patient record, such as the problem list and medical history, could no longer be entered as free text but required selecting a term from a list of sometimes unfamiliar terms. How can we address this major shift in the use of the patient record? There is little documentation explaining how best to support users during this transformation. We would therefore like to share our experience of this transition, the strategies we implemented, and the challenges we faced. This presentation will be structured around four key areas: communication between end-users and the terminology team, data quality, data relevance, and the importance of structured data. Description Our academic hospital transitioned from a free-text system to a system where data is 100% structured in specific sections, such as the problem list and medical history. This shift required users to select terms from a standardized list rather than entering free-text information. This represented a significant disruption to their clinical practices. The scope of this abstract covers our experience with this transition, as well as the strategies we implemented to improve compliance of end-users and adapt SNOMED CT to clinical practice. Scope Belgium has designated SNOMED CT as the recommended clinical reference terminology for clinical information systems. In our academic hospital, we adopted a new electronic health system in 2020, and at the same time, we selected and implemented SNOMED CT as the reference terminology in various parts of our electronic health record. The use of a common, unambiguous terminology has the potential to improve data quality and patient safety by capturing clinical data in a standardized manner. How SNOMED CT will be used SNOMED CT is used in the problem list, medical history, family history, visit diagnosis, surgical history, and for non-drug allergies. We would like to extend its use to other domains, such as pharmacy and laboratory. 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

Fluoropyrimidines, a class of antimetabolite drugs used in chemotherapy, can cause severe cardiac complications including coronary artery spasms with potential fatal outcomes. Current risk assessment approaches are hindered by insufficient data on predisposing factors. Here, we present a retrospective audit examining cardiac risk in patients receiving fluoropyrimidine chemotherapy (5-fluorouracil and Capecitabine). Our study leverages MedCAT natural language processing technologies and SNOMED CT to analyse clinical records, identify risk factors, and evaluate outcomes in these patients. The findings aim to improve early identification of high-risk individuals, facilitate timely referrals to cardio-oncology specialists, and optimize clinical decision-making through better utilization of existing healthcare data. Back View Map Guy's & St. Thomas' NHS Foundation Trust (1 of 2) Improving Cardiac Risk Stratification in Fluoropyrimidine Chemotherapy: A Retrospective Audit for Enhanced Patient Outcomes Read More Country / Region EMEA Tags Artificial intelligence, Clinical Practice, Data analytics, Patient safety Fluoropyrimidines, a class of antimetabolite drugs used in chemotherapy, can cause severe cardiac complications including coronary artery spasms with potential fatal outcomes. Current risk assessment approaches are hindered by insufficient data on predisposing factors. Here, we present a retrospective audit examining cardiac risk in patients receiving fluoropyrimidine chemotherapy (5-fluorouracil and Capecitabine). Our study leverages MedCAT natural language processing technologies and SNOMED CT to analyse clinical records, identify risk factors, and evaluate outcomes in these patients. The findings aim to improve early identification of high-risk individuals, facilitate timely referrals to cardio-oncology specialists, and optimize clinical decision-making through better utilization of existing healthcare data. Description Hospital-wide risk factor and outcome data for a historical cohort of 3446 patients that have undergone chemotherapy with Fluoropyrimidines is surfaced using an AI model trained to identify SNOMED CT concepts. Scope SNOMED CT was selected for its level of granularity and its concept orientation. Its structure, non-ambiguity and non-redundancy facilitate good performance in named entity recognition and linking methods used by natural language processing models. How SNOMED CT will be used SNOMED CT concepts are used by a natural language processing model (developed using MedCAT) enabling it to identify comorbidity and medical history risk factors and cardiac outcomes of patients undergoing Fluoropyrimidine chemotherapy. 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

Accessing pathology cancer data within and across healthcare institutions is problematic. Manual curation of large quantities can take years. Factors including lack of normalization of grading and staging systems and non-standard narrative reporting formats hinder easy access to the data. However, these data could become readily available for research using newly published SNOMED CT content developed specifically for use in structured pathology reporting using data elements from the College of American Pathologists (CAP) Cancer Protocols and the International Collaboration on Cancer Reporting (ICCR) datasets . As a demonstration of the power of using SNOMED CT to create an interoperable data repository across multiple institutions, encoded cancer pathology reports and ancillary data for resections of invasive cancer of the breast following administration of neoadjuvant therapy were combined from two large academic medical centers[ME1] for 2021- present. Extracted data included histologic type, biomarker profile, grade, and pathologic assessment of the response to therapy for the tumors. These data were stored in a repository structured on the Observational Medical Outcomes Partnership (OMOP) model for further analysis. This presentation establishes a reproduceable methodology that can be used to extract, combine and represent pathology data from two separate EHR systems for subsequent analysis. Back View Map College of American Pathologists SNOMED CT encoded pathology breast cancer data to determine pathological response to neoadjuvant therapy Read More Country / Region Americas Tags Clinical Practice, Data analytics, Implementation, Research Accessing pathology cancer data within and across healthcare institutions is problematic. Manual curation of large quantities can take years. Factors including lack of normalization of grading and staging systems and non-standard narrative reporting formats hinder easy access to the data. However, these data could become readily available for research using newly published SNOMED CT content developed specifically for use in structured pathology reporting using data elements from the College of American Pathologists (CAP) Cancer Protocols and the International Collaboration on Cancer Reporting (ICCR) datasets . As a demonstration of the power of using SNOMED CT to create an interoperable data repository across multiple institutions, encoded cancer pathology reports and ancillary data for resections of invasive cancer of the breast following administration of neoadjuvant therapy were combined from two large academic medical centers[ME1] for 2021- present. Extracted data included histologic type, biomarker profile, grade, and pathologic assessment of the response to therapy for the tumors. These data were stored in a repository structured on the Observational Medical Outcomes Partnership (OMOP) model for further analysis. This presentation establishes a reproduceable methodology that can be used to extract, combine and represent pathology data from two separate EHR systems for subsequent analysis. Description The project addresses the value of electronic SNOMED CT encoding of histopathology in the medical record (EHR) to support cancer research within an institutional agnostic paradigm. Evaluation of response to neoadjuvant therapy for breast cancer is the exemplary objective. Scope SNOMED CT is the sole international terminology standard that can represent structured pathology cancer data. How SNOMED CT will be used SNOMED CT is explicitly used to represent and normalize discrete data from surgical pathology reports for cancer retrieved from the EHR to support clinical research. 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 Snowstorm terminology server developed by SNOMED International for the SNOMED CT browser and the authoring platform is also free and open source software that anyone can use. At Health New Zealand we promote the use of Snowstorm to health providers and the software industry as a cheap and cheerful way to add FHIR-friendly code set lookups to clinical apps. There are already some great SNOMED CT-powered apps as a result. The key is to show that terminology is fun, and this poster presentation describes our efforts to win over developers to SNOMED CT and to Snowstorm. At the extreme edge, we have a demo port of Snowstorm on a Raspberry Pi. Back View Map Health New Zealand Porting the Snowstorm terminology server to the Raspberry Pi and other ways of winning over developers and getting SNOMED CT into the field Read More Country / Region APAC Tags Implementation, Innovation, Tooling The Snowstorm terminology server developed by SNOMED International for the SNOMED CT browser and the authoring platform is also free and open source software that anyone can use. At Health New Zealand we promote the use of Snowstorm to health providers and the software industry as a cheap and cheerful way to add FHIR-friendly code set lookups to clinical apps. There are already some great SNOMED CT-powered apps as a result. The key is to show that terminology is fun, and this poster presentation describes our efforts to win over developers to SNOMED CT and to Snowstorm. At the extreme edge, we have a demo port of Snowstorm on a Raspberry Pi. Description We are developing material and education to promote the use of Snowstorm to industry, making it a fun and attractive way to build SNOMED CT-powered apps. One of the demonstrators is a port of Snowstorm to a Raspberry Pi with the full content of the SNOMED CT New Zealand Edition loaded - to what exact purpose we don't know, it's a demo! Scope As a standard for the New Zealand health sector, SNOMED CT has a government-sponsored programme for adoption by industry. How SNOMED CT will be used The SNOMED CT New Zealand is published twice a year via the SNOMED International Managed Service and is a standard product for New Zealand health providers and the health software industry. We promote Snowstorm as a cost-effective way to add terminology capability to new and existing apps, linked to the New Zealand Health Terminology Service. 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

This study investigates the comparative applicability and semantic precision of ICD-10 and SNOMED CT in the context of unstructured clinical narratives derived from abdominal region CT imaging reports. A dataset of approximately 100 cases, performed over one week at a tertiary medical center, was analyzed. The source terms extracted from the CT reports were mapped to both terminology systems (ICD-10 and SNOMED CT) in their raw form and after clinical interpretation. Results demonstrate that SNOMED CT consistently provided higher coverage and more granular, clinically meaningful representations than ICD-10, particularly when clinically interpreted terms were used. The study categorized semantic alignment into exact matches, broad matches, and no matches, and used a Venn diagram to illustrate the conceptual overlap and divergence between the two terminologies. These findings highlight the potential of SNOMED CT to facilitate more comprehensive and clinically meaningful standardization of healthcare data, extending beyond traditional diagnosis-based coding frameworks, particularly in the context of multi-center research and real-time clinical data analysis. Furthermore, the results emphasize the need for trained terminology experts in the mapping process to ensure accurate representation of clinical content. Overall, the study offers meaningful insights into the comparative advantages and limitations of ICD-10 and SNOMED CT, reinforcing the value of SNOMED CT as a more suitable framework for the standardized representation of clinical information in both research and practice. Back View Map Kakao Healthcare (4 of 5) Exploring the Mapping Coverage and Accuracy of ICD-10 and SNOMED CT in Standardizing Unstructured Clinical Data: A Focus on Abdominal region CT Imaging Read More Country / Region APAC Tags Clinical Practice, Mapping This study investigates the comparative applicability and semantic precision of ICD-10 and SNOMED CT in the context of unstructured clinical narratives derived from abdominal region CT imaging reports. A dataset of approximately 100 cases, performed over one week at a tertiary medical center, was analyzed. The source terms extracted from the CT reports were mapped to both terminology systems (ICD-10 and SNOMED CT) in their raw form and after clinical interpretation. Results demonstrate that SNOMED CT consistently provided higher coverage and more granular, clinically meaningful representations than ICD-10, particularly when clinically interpreted terms were used. The study categorized semantic alignment into exact matches, broad matches, and no matches, and used a Venn diagram to illustrate the conceptual overlap and divergence between the two terminologies. These findings highlight the potential of SNOMED CT to facilitate more comprehensive and clinically meaningful standardization of healthcare data, extending beyond traditional diagnosis-based coding frameworks, particularly in the context of multi-center research and real-time clinical data analysis. Furthermore, the results emphasize the need for trained terminology experts in the mapping process to ensure accurate representation of clinical content. Overall, the study offers meaningful insights into the comparative advantages and limitations of ICD-10 and SNOMED CT, reinforcing the value of SNOMED CT as a more suitable framework for the standardized representation of clinical information in both research and practice. Description The purpose of this study is to compare the applicability and accuracy of standardized terminology systems‚ specifically ICD-10 and SNOMED CT‚ in multi-center research and clinical data-driven studies. While ICD codes have traditionally been used for classification, SNOMED CT offers a more granular and clinically meaningful framework, enabling more precise representation and management of patient data. Distinct from prior structured code-based research, this study leverages unstructured clinical data to evaluate the coverage and applicability of the two terminology systems. In addition, mapping was performed by interpreting unstructured data to capture clinical meaning. It is anticipated that these findings will contribute to the development of more robust and clinically relevant standardization methodologies that extend beyond diagnosis-centric paradigms. Scope SNOMED CT encompasses a wide range of clinical concepts, including diseases, symptoms, treatments, test results, medical procedures, and medications. This enables healthcare professionals to handle patient data from various perspectives in clinical practice. SNOMED CT provides greater granularity and clinical relevance than ICD-10, making it more suitable for real-time clinical applications, multi-center research, and comprehensive data standardization. Granularity, in this context, refers to the capacity to express the same clinical concept with detailed distinctions such as severity, anatomical location, underlying cause, clinical context, and timing. SNOMED CT is applied in electronic health record (EHR) systems in healthcare institutions, allowing for more accurate and consistent documentation of patient information. This provides standardized data that can be used for multi-center research or national-level data analysis. How SNOMED CT will be used The SNOMED CT International Edition (version 2025-01-01) was used for the mapping process. Post-coordination was not applied in this study. This study utilized approximately 100 abdominal region CT records performed over the course of one week at a tertiary medical center. The dataset consisted of unstructured clinical narratives from various abdominal imaging modalities, including abdominopelvic CT, biliary-pancreatic CT, appendix CT, genitourinary/gynecologic CT, and abdominal trauma CT. Mapping to SNOMED CT and ICD-10 was conducted on both the raw (uninterpreted) and clinically interpreted data. Raw data refers to the source terms extracted directly from the CT reports, whereas clinically interpreted data refers to source terms refined based on clinical knowledge. For example, "Spleen size, 13 cm" represents raw data, while "splenomegaly" represents the clinically interpreted version of the same information. Standard terminology browsers were employed to identify relevant codes, and the degree of semantic alignment was classified into three categories: exact match, broad match, and no match. Additionally, a Venn diagram was used to visualize the conceptual overlap and divergence between the two terminology systems, highlighting shared and unique mappings. 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 this project, we aimed to enhance the structural and semantic interoperability and secondary usability of clinical data by mapping structured elements from our Electronic Health Record (KWS) to SNOMED CT and FHIR (Fast Healthcare Interoperability Resources) standards. The overarching goal was to prepare our EHR for meaningful data exchange with other systems, while unlocking the potential for reusing structured clinical data for research, quality monitoring, and organizational analytics. We focused on data elements already captured in a structured way within the EHR and aligned them with appropriate FHIR resources. These included: * Medical procedures (both surgical and non-surgical) * Imaging procedures * Laboratory * Nursing observations and procedures * Diagnoses (goal= SNOMED CT as primary language) We identified the relevant data elements within the FHIR resource that contribute to the term's semantic expression and are widely applicable. This enabled us to define FHIR-based templates tailored to our context—serving both as a mapping guide and as a foundation for generating valid FHIR resources. FHIR data elements were then semantically enriched using SNOMED CT codes. This ensured that clinical meaning was preserved, even in scenarios where precoordinated SNOMED CT concepts were unavailable. Much of the semantic mapping occurs in the background, ensuring minimal impact on the end-user workflow. This initiative demonstrated that thoughtful, context-aware mapping of EPD data to international standards is both feasible and valuable. It provides a strong foundation for improved data exchange, semantic interoperability, and meaningful reuse of clinical data across healthcare and research ecosystems. Back View Map UZ Leuven (3 of 3) Data Capabilities: SNOMED CT and FHIR as a foundation for an interoperable standardised EPD Read More Country / Region EMEA Tags Collaboration, Data quality, Global/International, Implementation, Mapping, Patient safety In this project, we aimed to enhance the structural and semantic interoperability and secondary usability of clinical data by mapping structured elements from our Electronic Health Record (KWS) to SNOMED CT and FHIR (Fast Healthcare Interoperability Resources) standards. The overarching goal was to prepare our EHR for meaningful data exchange with other systems, while unlocking the potential for reusing structured clinical data for research, quality monitoring, and organizational analytics. We focused on data elements already captured in a structured way within the EHR and aligned them with appropriate FHIR resources. These included: * Medical procedures (both surgical and non-surgical) * Imaging procedures * Laboratory * Nursing observations and procedures * Diagnoses (goal= SNOMED CT as primary language) We identified the relevant data elements within the FHIR resource that contribute to the term's semantic expression and are widely applicable. This enabled us to define FHIR-based templates tailored to our context—serving both as a mapping guide and as a foundation for generating valid FHIR resources. FHIR data elements were then semantically enriched using SNOMED CT codes. This ensured that clinical meaning was preserved, even in scenarios where precoordinated SNOMED CT concepts were unavailable. Much of the semantic mapping occurs in the background, ensuring minimal impact on the end-user workflow. This initiative demonstrated that thoughtful, context-aware mapping of EPD data to international standards is both feasible and valuable. It provides a strong foundation for improved data exchange, semantic interoperability, and meaningful reuse of clinical data across healthcare and research ecosystems. Description This project focused on enabling semantic interoperability and secondary data usability of clinical data within our hospital's Electronic Health Record (EHR) system, KWS. We mapped existing structured clinical content to SNOMED CT and aligned relevant elements with FHIR (Fast Healthcare Interoperability Resources) standards. The project covered various clinical domains, including diagnoses, medical procedures, imaging, laboratory, and nursing observations and procedures. Our aim was twofold: to prepare the EHR for interoperable, standards-based data exchange and to enable meaningful reuse of clinical data for research, quality improvement, and organizational analytics. We developed context-aware FHIR templates that served as both a technical guide and a tool for generating structured FHIR resources enriched with SNOMED CT concepts. Scope SNOMED CT was selected for its comprehensive clinical scope, standardized structure, and robust international interoperability, making it the most widely adopted clinical terminology system globally. As the mandated national standard in Belgium, its adoption aligns with strategic guidance from national healthcare bodies such as the eHealth Platform, while also ensuring seamless alignment with international standards such as HL7, ICD, and LOINC. This dual compliance guarantees consistency with both national policy and global healthcare interoperability frameworks, fostering cross-border data exchange and collaboration. SNOMED CT provides a semantically rich and precise foundation for representing clinical data, making it ideal for structured health information exchange across diverse healthcare systems worldwide. Its capacity for both pre- and post-coordination enables flexibility in mapping a wide range of clinical scenarios, supporting multilingual and multicultural healthcare environments. Furthermore, its deep integration with FHIR (Fast Healthcare Interoperability Resources) ensures future-proof, standards-based infrastructure, facilitating compliance with evolving digital health regulations. This interoperability enhances secondary uses of data, such as (international) research collaborations, global health surveillance, quality benchmarking, and advanced analytics across the healthcare ecosystem. By adopting SNOMED CT, Belgium not only strengthens its national health data infrastructure but also positions itself as an active participant in the global digital health landscape, enabling cross-border patient care, interoperable EHR systems, and worldwide clinical research initiatives. How SNOMED CT will be used SNOMED CT is used to semantically enrich the structured data elements identified within the FHIR resource that contribute to the term's semantic expression and are widely applicable. Each FHIR template was designed to incorporate relevant SNOMED CT codes at appropriate data points, ensuring that the clinical intent and meaning are preserved during data exchange. For domains where precoordinated SNOMED CT concepts were insufficient, post-coordination was explored and selectively applied. The use of SNOMED CT occurs primarily in the background (for medical procedures, imaging, laboratory results, and nursing observations and procedures), allowing clinical workflows to remain unaffected. Our hospital has adopted SNOMED CT as the default and standard terminology for diagnosis coding in the problem list. While some medical disciplines are already successfully using SNOMED CT, we are taking an incremental approach to implementation across all departments, with full hospital-wide adoption targeted for completion by the end of 2026. 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

Procedure classifications like the Swiss procedure catalogue CHOP or the German procedure catalogue OPS often contain similar content, but differ on granular level and in hierarchical structure and format. Moreover, they have to be translated to English to be processed by standard mapping tools to interoperable terminologies. With data interoperability gaining importance in the provision of health care and in international research, supporting tools are essential. The Observational Health Data Sciences and Informatics (OHDSI) team offers the Usagi tool to help in (auto-) mapping codes into standard terminologies (in this study SNOMED CT). As a preparation in this study using a transformer-based LLM, the CHOP and the OPS medical procedure codes were translated into English (accuracy of 90%), contextually augmented and loaded into Usagi. We then tested the Usagi term similarity approach as a validation for both datasets of translated and mapped procedure catalogues from the different coding systems. The agreement of the SNOMED CT identifier pairs (semantic match) was compared and rated (narrower, broader, equivalent, exact). The resulting validation by mapping to SNOMED CT concepts showed the following results: 52 of 494 SNOMED CT concept pairs (10.5%) were rated as equal (identical SNOMED CT concept identifier) and 20 of 494 (4.1%) as semantically equivalent (different SNOMED CT concept identifier). 273 (55.3%) concept pairs in total were rated as equal, equivalent, narrower or wider. In summary, the Usagi tool with SNOMED CT as standard terminology supports content and workflow for automapping and efficient validation. Back View Map Inselspital, University Hospital Bern ts for validation Read More Country / Region EMEA Tags Artificial intelligence, Innovation, Mapping, Tooling, Translation Procedure classifications like the Swiss procedure catalogue CHOP or the German procedure catalogue OPS often contain similar content, but differ on granular level and in hierarchical structure and format. Moreover, they have to be translated to English to be processed by standard mapping tools to interoperable terminologies. With data interoperability gaining importance in the provision of health care and in international research, supporting tools are essential. The Observational Health Data Sciences and Informatics (OHDSI) team offers the Usagi tool to help in (auto-) mapping codes into standard terminologies (in this study SNOMED CT). As a preparation in this study using a transformer-based LLM, the CHOP and the OPS medical procedure codes were translated into English (accuracy of 90%), contextually augmented and loaded into Usagi. We then tested the Usagi term similarity approach as a validation for both datasets of translated and mapped procedure catalogues from the different coding systems. The agreement of the SNOMED CT identifier pairs (semantic match) was compared and rated (narrower, broader, equivalent, exact). The resulting validation by mapping to SNOMED CT concepts showed the following results: 52 of 494 SNOMED CT concept pairs (10.5%) were rated as equal (identical SNOMED CT concept identifier) and 20 of 494 (4.1%) as semantically equivalent (different SNOMED CT concept identifier). 273 (55.3%) concept pairs in total were rated as equal, equivalent, narrower or wider. In summary, the Usagi tool with SNOMED CT as standard terminology supports content and workflow for automapping and efficient validation. Description The aim of the study was to test the Usagi term similarity approach as a validation for large datasets of translated and mapped procedure catalogues from different coding systems. We set up an automated process for translation, augmentation and mapping plus final validation by using SNOMED CT and the ODHSI's Usagi tool. Scope SNOMED CT was selected as a semantically potent terminology was needed to serve as a link to pairs of heterogenous terms (different in format, structure, granularity). Also, the terminology chosen had to cover the procedural content of two different health systems and thus be extensive. How SNOMED CT will be used The similarity approach maps each translated term to a SNOMED concept. The SNOMED concepts being either equal, equivalent, narrower or wider serve as an interoperable linkage between the translated terms from the different health systems' catalogues. 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