Objective: In order to solve the problems of DRG abnormal grouping such as upcoding, downcoding and ambiguous cases, a systematic review was carried out on the relevant articles on DRG abnormal grouping models at home and abroad. Methods: The PRISMA framework was used to evaluate the quality of literature and reports, and the research design, overall trends, data sources, input and output variables, evaluation indicators and other aspects of literature models were analyzed. Results: The research design types of the six categories of literature were summarized, and it was found that deep learning methods had developed rapidly in foreign countries since 2020, with significant features such as multi-center data sources, text-based input variables, output variables by type, and high model evaluation performance. Besides, scientific problems, model characteristics and limitations were extracted. Conclusion: There is a lack of research on whole-disease group and high-precision DRG payment abnormal warning models based on electronic medical record text data with deep learning methods. In the future, continuous exploration is needed to develop DRG payment abnormal warning tools based on deep learning of electronic medical record structured and unstructured data, in order to further promote the deepening of DRG payment reform.
A Systematic Review of Research on DRG Payment Abnormal Warning Model[J]. China Health Insurance. 2025, 0(3): 60-67 https://doi.org/10.19546/j.issn.1674-3830.2025.3.007
中图分类号：
F840.684
C913.7
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