Manual Therapy Techniques in Standardized Clinical Terminology System: A Coverage Analysis of SNOMED CT and OMOP CDM
Background Standardized clinical terminology is essential for semantic interoperability and for effectively utilizing clinical data in digital health, research, and artificial intelligence (AI). Although manual therapy is widely used in physical therapy practice, its techniques have not yet been systematically integrated into major standardized terminology systems such as systematized nomenclature of medicine clinical terms (SNOMED CT) and observational medical outcomes partnership common data model (OMOP CDM). Purpose The purpose of this study was to evaluate the status of terminology standardization for manual therapy techniques within SNOMED CT and OMOP CDM, including the identification of alternative or broader concepts when direct representations were unavailable. Study design Terminology evaluation study Methods Twenty-nine techniques were selected through a literature review, educational resources, and expert consensus. Each technique was evaluated for direct representation in SNOMED CT and for mapping within the OMOP CDM. Techniques not directly defined were assessed for the availability of broader, more generic concepts. Results Among the 29 techniques, 15 (51.7%) were standardized in at least one system, while 14 (48.3%) were not standardized and could only be described using higher-level categories. The standardization rates were 40.0% for orthopedic techniques, 44.4% for soft tissue techniques, 60.0% for nerve techniques, and 80.0% for other integrative approaches. Conclusions Nearly half of the manual therapy techniques commonly used in physical therapy lack independent representation in SNOMED CT and OMOP CDM. Although generic categories allow for minimal documentation, the absence of detailed, granular concepts diminishes semantic accuracy, impedes cross-institutional comparability, and limits multicenter research and AI-based applications.
Authors
- Jun-hee Kim
Institutions
- Yonsei University (KR)
Publication Details
- Journal
- Journal of Musculoskeletal Science and Technology
- Published
- 2025-12-01
- DOI
- https://doi.org/10.29273/jmst.2025.9.2.125
- Primary Topic
- Biomedical Text Mining and Ontologies
- Type
- article
- Field-Weighted Citation Impact
- 0.00