ArticleHealth care science2026
Challenges and Solutions in Deploying Systematized Nomenclature of Medicine-Clinical Terms in the Chinese Healthcare Context.
Article in Health care science, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
1 citing paper in PubMed.
- Challenges and Solutions in Deploying Systematized Nomenclature of Medicine-Clinical Terms in the Chinese Healthcare Context.Health care science · 2026Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
10 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Systematized nomenclature of medicine-clinical terms (SNOMED CT), one of the most comprehensive clinical terminology systems, is pivotal in enhancing healthcare interoperability, clinical data governance, and medical artificial intelligence (AI) development globally. In China, with the rapid growth of large-scale models and an increasing emphasis on transforming the intrinsic value of healthcare data, the absence of a nationally unified clinical terminology standard poses significant challenges. This commentary provides an in-depth analysis of the benefits of SNOMED CT for global healthcare, examines the critical deficiencies in Chinese healthcare big data and AI development due to the lack of standardized terminology, and outlines the technical, administrative, and educational challenges encountered in deploying SNOMED CT within Chinese environments. Special emphasis is laid on the potential of advanced large language models in facilitating the mapping of Chinese clinical data to SNOMED CT. We further discuss the necessity of high-quality data standardization in advancing medical AI in China. Finally, key conclusions and a roadmap for overcoming these challenges are proposed.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.