Abstract

The purpose of this study is to explore the applicability of the current CHS-DRG grouping and payment scheme in Traditional Chinese Medicine (TCM) hospitals by grouping the discharged cases in TCM hospitals. It also aims to provide a method reference for discussing the related grouping of TCM disease and syndrome diagnosis. The first-page data of inpatient medical records in some TCM hospitals above the county level, from 2015 to 2020, were taken as the research object. The first-page data of inpatient medical records from 2015 to 2017 were grouped according to the CHS-DRG grouping rules, and the grouping results were obtained. Subsequently, two-stage clustering methodology was used to further group the bad groups, and the TCM disease grouping model was preliminarily formed. The model was verified by the first-page data of inpatient medical records from 2018 to 2020. The results showed that under the CHS-DRG grouping model, 494,150 cases were divided into 359 DRG groups. Only 92 cases with CV < 1 and cases > 100 were grouped poorly. After adding TCM diagnostic codes into the grouping model, the grouping effect of the disease group with CV > 1 was significantly increased, and the grouping results met the CHS-DRG grouping criteria. To fully leverage the benefits of Traditional Chinese Medicine and provide simple, convenient, empirical, and inexpensive medical services, TCM hospitals can explore TCM case grouping models that conform to the characteristics of TCM. In case grouping, TCM characteristic diagnosis and treatment elements should be included, and the study of medical insurance payment mode suitable for TCM hospitals should be conducted. Additionally, improving the hospital hardware facilities and providing an intelligent, automatic medical record management system should be prioritized. Further standardizing TCM diagnosis and code filling in the medical record information system and strengthening the study of TCM medical record management and coding will improve the quality of medical record.

Full Text
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