Developing a Thai User Interface Terminology for Systematized Nomenclature of Medicine Clinical Terms Implementation in Primary Care: Cross-Sectional Content Coverage Analysis
Issued Date
2026-01-01
Resource Type
eISSN
22919694
DOI
Scopus ID
2-s2.0-105032917408
Journal Title
Jmir Medical Informatics
Volume
14
Rights Holder(s)
SCOPUS
Bibliographic Citation
Jmir Medical Informatics Vol.14 (2026)
Suggested Citation
Tangchitnob N., Ponthongmak W., Kijsanayotin B., Pattanaprateep O., Phusanti S., Atiksawedparit P., Suwanthaweemeesuk K., Siangfu J., McKay G.J., Attia J., Thakkinstian A. Developing a Thai User Interface Terminology for Systematized Nomenclature of Medicine Clinical Terms Implementation in Primary Care: Cross-Sectional Content Coverage Analysis. Jmir Medical Informatics Vol.14 (2026). doi:10.2196/80039 Retrieved from: https://repository.li.mahidol.ac.th/handle/123456789/115847
Title
Developing a Thai User Interface Terminology for Systematized Nomenclature of Medicine Clinical Terms Implementation in Primary Care: Cross-Sectional Content Coverage Analysis
Corresponding Author(s)
Other Contributor(s)
Abstract
Background: Primary care in Thailand often uses mixed Thai-English free-text documentation for diagnoses and clinical problems, limiting standardization, interoperability, and secondary data use. Clinical terminologies like Systematized Nomenclature of Medicine Clinical Terms (SNOMED CT), a comprehensive reference terminology, can bridge this gap through the use of structured clinical data. Developing and mapping a local user interface terminology (UIT) is one of the key strategies for implementing SNOMED CT in real-world clinical settings. Objective: This study aimed to develop a Thai UIT derived from frequently used terms in real-world primary care practice, map these terms to SNOMED CT concepts, and evaluate the extent of concept coverage. Methods: Frequently used clinical terms were extracted from outpatient medical records from the family, emergency, and internal medicine departments using a customized tokenization method, N-gram analysis, and expert review. This process yielded 2054 Thai-specific terms. All terms were normalized and mapped to SNOMED CT through manual expert-driven and semiautomated tools. Unmapped terms were subsequently analyzed to identify mapping barriers and solutions. Results: Of the 2054 Thai-specific terms, 2012 were successfully mapped to 2041 (97.98%) SNOMED CT concepts, including 1781 (85.50%) fully, 123 (5.90%) broader, 56 (2.69%) narrower, 81 (3.89%) inexact mappings, and 42 (2.02%) remained unmapped. Most mappings were one-to-one (1984), with 28 terms mapped to multiple concepts (57), covering 1486 unique SNOMED CT concepts. The remaining 42 unmapped terms were mostly due to culturally specific expressions or concepts not yet represented in SNOMED CT. These were categorized for potential postcoordination, exclusion, or national extension development. Conclusions: This study demonstrates the feasibility of developing a Thai UIT mapped to SNOMED CT and describes mapping challenges. The resulting UIT enhances semantic clarity in clinical documentation and supports better interoperability, clinical decision-making, and health data analytics within Thailand’s health care system.