Processing of Real World Data in Traditional Chinese Medicine
DOI: 10.23977/phpm.2023.030201 | Downloads: 10 | Views: 477
Author(s)
Yujia Li 1, Jun Li 1
Affiliation(s)
1 Public Health School, Shaanxi University of Chinese Medicine, Xi'an, Shaanxi, 712046, China
Corresponding Author
Jun LiABSTRACT
Traditional Chinese medicine (TCM) is a unique traditional medicine in China and has been passed down to the present day with its unique theoretical basis and treatment model. The introduction of the concept of evidence-based medicine into traditional Chinese medicine and the scientific evaluation of its clinical efficacy are essential for the better development and transmission of TCM. Randomised controlled trials have always been the "gold standard" of clinical trial evidence due to their strict inclusion and exclusion criteria and strict control of the data collection process, but the specificity of the TCM treatment model, the holistic, ambiguous, diverse and complex nature of the data, and the concomitant events in the research process make it difficult to conduct randomized ontrolled trials in TCM. Real World Study (RWS), which is conducted in actual clinical settings with broad inclusion and exclusion criteria to obtain treatment effects and long-term clinical outcomes as endpoints, can be used for long-term evaluation of treatment measures based on patients' preference. Real world studies allow for the long-term evaluation of treatment measures based on patients' preference, and are able to evaluate the overall effects, adapting to the holistic concept of Chinese medicine and the characteristics of evidence-based treatment. This paper summaries the data characteristics and data processing methods of real world studies in TCM, with a view to providing a reference for real world studies in TCM.
KEYWORDS
Chinese medicine; real-world research; Chinese medicine data characteristics; Chinese medicine data processingCITE THIS PAPER
Yujia Li, Jun Li, Processing of Real World Data in Traditional Chinese Medicine. MEDS Public Health and Preventive Medicine (2023) Vol. 3: 1-6. DOI: http://dx.doi.org/10.23977/phpm.2023.030201.
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