{ "abstract": "Background: With the rapid expansion of health care data, multiple international standardized terminology systems have been developed and refined to ensure semantic interoperability, which is a crucial factor in facilitating collaborative use.\n\nObjective: This study aimed to evaluate the real-world use and applicability of standardized terminology systems by mapping clinical terms from routine cancer care and to propose guidelines that optimize mapping.\n\nMethods: Clinical terms generated during cancer care at the National Cancer Center Korea were extracted and mapped to multiple target terminology systems: the Korean Standard Classification of Diseases; International Classification of Diseases, 9th Revision, Clinical Modification; Logical Observation Identifiers Names and Codes; RxNorm; Anatomical Therapeutic Chemical classification; and Systematized Nomenclature of Medicine Clinical Terms (SNOMED CT). A multidisciplinary working group of health care information managers, data experts, and oncologists verified the mapping outcomes, calculated the cross-verification discordance rate, and compared the coverage across systems.\n\nResults: A total of 3472 source terms were identified across 7 domains: chief complaint, diagnosis, laboratory test, imaging test, surgery/procedure, medication, and radiation therapy. With postcoordination and predefined mapping guidelines, SNOMED CT achieved high overall mapping rates (98.3%‐100%) across domains. Applying postcoordination as needed substantially improved exact semantic match rates, which exceeded 90% in most domains. However, more than 40% of source terms were broadly mapped in the imaging test and radiation therapy domains because procedural details such as imaging technique and radiation field configuration could not be represented in SNOMED CT. The cross-verification discordance rate between mappers, defined as the proportion of source terms with disagreement, remained low (0.3%‐1.3%). RxNorm and Logical Observation Identifiers Names and Codes showed complementary strengths within their specialized domains, adding detail or specificity where SNOMED CT was less complete.\n\nConclusions: Standardized terminology systems are feasible and effective in encoding clinical terms for cancer care. SNOMED CT was the most comprehensive, and predefined mapping guidelines provide a structured framework for mapping and consensus resolution. Domain-specific systems should be used alongside SNOMED CT, and all systems should be updated regularly to reflect evolving clinical practice.", "abstract_shown": true, "affiliations": [ "Cancer Data Center, National Cancer Control Institute, National Cancer Center, Goyang, South Korea", "Department of Medical Information Management, National Cancer Center, Goyang, South Korea", "Department of Public Health and Artificial Intelligence, Graduate School of Cancer Science and Policy, National Cancer Center, Goyang, South Korea", "Department of Internal Medicine, Seoul National University Bundang Hospital, 82 Gumi-ro 173beon-gil, Seongnam, 13620, South Korea, +82-31-787-7863" ], "article_type": "research article", "arxiv": null, "author_total": 11, "authors": [ { "affs": [ 0, 1 ], "name": "Boyun Jang", "orcid": "0000-0002-6997-5602" }, { "affs": [ 0, 1 ], "name": "Eunhye Park", "orcid": "0000-0003-2603-5589" }, { "affs": [ 0 ], "name": "Sangwon Lee", "orcid": "0000-0002-2587-8915" }, { "affs": [ 0, 1 ], "name": "Eunbi Ju", "orcid": "0009-0007-7723-420X" }, { "affs": [ 0, 1 ], "name": "Yewon Jang", "orcid": "0009-0008-0109-5810" }, { "affs": [ 0 ], "name": "In Hae Kwak", "orcid": "0009-0008-2913-7031" }, { "affs": [ 0, 2 ], "name": "Kui Son Choi", "orcid": "0000-0001-5336-3874" }, { "affs": [ 0 ], "name": "Hyun-Joo Kong", "orcid": "0000-0003-2319-7195" }, { "affs": [ 0, 2 ], "name": "Hyo Soung Cha", "orcid": "0000-0003-0537-8355" }, { "affs": [ 0, 2 ], "name": "Hyun-Jin Kim", "orcid": "0000-0003-4160-4815" }, { "affs": [ 0, 3 ], "name": "Heejung Chae", "orcid": "0000-0003-3547-3521" } ], "bibtex": "https://research.draconitic.com/paper/6kgvkdjrjrb0d.bib", "cited_by": 0, "doi": "10.2196/96548", "fulltext_url": "https://pmc.ncbi.nlm.nih.gov/articles/PMC13712170/", "id": "6kgvkdjrjrb0d", "issns": [ "2291-9694" ], "landing_url": "https://doi.org/10.2196/96548", "language": "en", "licence": "CC-BY-4.0", "licence_label": "CC BY 4.0", "licence_source": "article", "licence_url": "https://creativecommons.org/licenses/by/4.0/", "metadata_licence": null, "native_subjects": [ [ "heading", "Clinical Informatics" ], [ "heading", "Design and Usability of Clinical Software and EHRs" ], [ "heading", "Knowledge Translation and Implementation Science" ], [ "heading", "Ontologies, Classifications, and Coding" ], [ "heading", "Original Paper" ], [ "heading", "Standards and Interoperability" ], [ "heading", "mHealth in a Clinical Setting" ], [ "openalex", "1312|Biomedical Text Mining and Ontologies" ] ], "openalex": "W7223909671", "pdf_url": null, "pmcid": "PMC13712170", "pmid": "42944713", "published": "2026-10-27", "publisher": "JMIR Publications Inc.", "ris": "https://research.draconitic.com/paper/6kgvkdjrjrb0d.ris", "sources": [ { "first_seen": "2026-10-31T12:22:48Z", "native_id": "PMC13712170", "source": "pmc", "url": "https://doi.org/10.2196/96548" } ], "subjects": [ { "field": "biochemistry", "origin": "openalex:1312|Biomedical Text Mining and Ontologies", "subfield": "molecular-biology" } ], "title": "Real-World Applicability of Multiple International Terminology Systems in Oncology: Mapping Study of Clinical Terms for Cancer Diagnosis and Treatment", "title_text": "Real-World Applicability of Multiple International Terminology Systems in Oncology: Mapping Study of Clinical Terms for Cancer Diagnosis and Treatment", "type": "peer-reviewed", "updated_at": "2026-10-31T12:22:48Z", "url": "https://research.draconitic.com/paper/6kgvkdjrjrb0d", "venue": "JMIR Medical Informatics", "versions": [] }