Two-stage DRG grouping of cerebral infarction based on comorbidity and complications classification
BackgroundSince 2017, cerebral infarction (CI) has become a leading cause of mortality in China, with rising treatment costs posing significant challenges to the healthcare system. The Diagnosis-Related Groups (DRG) payment system has been recognized as a potential solution to curb rising healthcare...
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Frontiers Media S.A.
2025-04-01
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| Series: | Frontiers in Public Health |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fpubh.2025.1513744/full |
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| author | Siyu Zeng Lele Li Lele Li Jialing Li Xiaozhou He |
| author_facet | Siyu Zeng Lele Li Lele Li Jialing Li Xiaozhou He |
| author_sort | Siyu Zeng |
| collection | DOAJ |
| description | BackgroundSince 2017, cerebral infarction (CI) has become a leading cause of mortality in China, with rising treatment costs posing significant challenges to the healthcare system. The Diagnosis-Related Groups (DRG) payment system has been recognized as a potential solution to curb rising healthcare expenditures. However, in its implementation, China faces considerable hurdles due to its vast geographical size, regional economic disparities, and heterogeneous disease spectrum.ObjectiveThis study proposes a novel two-stage grouping strategy with a two-stage method tailored to address the local context of western China. The method adaptively accommodates regional variations in disease burden and healthcare resource distribution.MethodsUsing hospitalization data from 111,025 CI patients collected by the Healthcare Security Administration of a western Chinese city between 2016 and 2018 (during the pre-DRG implementation period), we developed a two-stage DRG method. In the first stage, regression analysis identified and prioritized comorbidities and complications that influence medical costs. In the second stage, a decision tree algorithm established standardized classification protocols for DRG grouping, ensuring regional adaptability.ResultsThe average hospitalization cost for CI patients was USD$ 1,565, with total expenditures reaching USD$ 1.71 million in the target city. By employing this localized two-stage grouping model, the proportion of inter-group variations, as measured by the coefficient of variation (CV), is below 1, reaching 100%, satisfying the technical criteria for DRG categorization. This optimization reduced the number of DRG from 18 to 4. It increased the proportion of groups with CV to <0.8 from 67 to 100%, signifying a substantial enhancement in group heterogeneity compared to the existing grouping method, China Healthcare Security Diagnosis-Related Groups (CHS-DRG).ConclusionThis study demonstrates the effectiveness of our proposed two-stage method using real data. Implementation of this localized method in the target city could result in potential savings of USD$ 8.59 million, surpassing the existing CHS-DRG method. These findings suggest that this adaptive method may be a scalable strategy for resource-limited regions undergoing healthcare system reforms. |
| format | Article |
| id | doaj-art-69b0f631233d4fa6ba9a9ac508f2c433 |
| institution | Kabale University |
| issn | 2296-2565 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Public Health |
| spelling | doaj-art-69b0f631233d4fa6ba9a9ac508f2c4332025-08-20T03:53:43ZengFrontiers Media S.A.Frontiers in Public Health2296-25652025-04-011310.3389/fpubh.2025.15137441513744Two-stage DRG grouping of cerebral infarction based on comorbidity and complications classificationSiyu Zeng0Lele Li1Lele Li2Jialing Li3Xiaozhou He4School of Logistics, Chengdu University of Information Technology, Chengdu, Sichuan, ChinaSchool of Labor and Human Resources, Renmin University of China, Beijing, ChinaInstitute for Hospital Management of Henan Province, Zhengzhou, ChinaSchool of Management, Hunan University of Technology and Business, Changsha, Hunan, ChinaBusiness School, Sichuan University, Chengdu, Sichuan, ChinaBackgroundSince 2017, cerebral infarction (CI) has become a leading cause of mortality in China, with rising treatment costs posing significant challenges to the healthcare system. The Diagnosis-Related Groups (DRG) payment system has been recognized as a potential solution to curb rising healthcare expenditures. However, in its implementation, China faces considerable hurdles due to its vast geographical size, regional economic disparities, and heterogeneous disease spectrum.ObjectiveThis study proposes a novel two-stage grouping strategy with a two-stage method tailored to address the local context of western China. The method adaptively accommodates regional variations in disease burden and healthcare resource distribution.MethodsUsing hospitalization data from 111,025 CI patients collected by the Healthcare Security Administration of a western Chinese city between 2016 and 2018 (during the pre-DRG implementation period), we developed a two-stage DRG method. In the first stage, regression analysis identified and prioritized comorbidities and complications that influence medical costs. In the second stage, a decision tree algorithm established standardized classification protocols for DRG grouping, ensuring regional adaptability.ResultsThe average hospitalization cost for CI patients was USD$ 1,565, with total expenditures reaching USD$ 1.71 million in the target city. By employing this localized two-stage grouping model, the proportion of inter-group variations, as measured by the coefficient of variation (CV), is below 1, reaching 100%, satisfying the technical criteria for DRG categorization. This optimization reduced the number of DRG from 18 to 4. It increased the proportion of groups with CV to <0.8 from 67 to 100%, signifying a substantial enhancement in group heterogeneity compared to the existing grouping method, China Healthcare Security Diagnosis-Related Groups (CHS-DRG).ConclusionThis study demonstrates the effectiveness of our proposed two-stage method using real data. Implementation of this localized method in the target city could result in potential savings of USD$ 8.59 million, surpassing the existing CHS-DRG method. These findings suggest that this adaptive method may be a scalable strategy for resource-limited regions undergoing healthcare system reforms.https://www.frontiersin.org/articles/10.3389/fpubh.2025.1513744/fulldiagnosis-related groupsclassificationcomorbidity and complicationscerebral infarctiontwo-stage grouping method |
| spellingShingle | Siyu Zeng Lele Li Lele Li Jialing Li Xiaozhou He Two-stage DRG grouping of cerebral infarction based on comorbidity and complications classification Frontiers in Public Health diagnosis-related groups classification comorbidity and complications cerebral infarction two-stage grouping method |
| title | Two-stage DRG grouping of cerebral infarction based on comorbidity and complications classification |
| title_full | Two-stage DRG grouping of cerebral infarction based on comorbidity and complications classification |
| title_fullStr | Two-stage DRG grouping of cerebral infarction based on comorbidity and complications classification |
| title_full_unstemmed | Two-stage DRG grouping of cerebral infarction based on comorbidity and complications classification |
| title_short | Two-stage DRG grouping of cerebral infarction based on comorbidity and complications classification |
| title_sort | two stage drg grouping of cerebral infarction based on comorbidity and complications classification |
| topic | diagnosis-related groups classification comorbidity and complications cerebral infarction two-stage grouping method |
| url | https://www.frontiersin.org/articles/10.3389/fpubh.2025.1513744/full |
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