Two-stage DRG grouping of cerebral infarction based on comorbidity and complications classification

SIYU ZENG¹Xiaozhou He²Jialing Li³Lele Li^4*

• ¹Chengdu University of Information Technology, Chengdu, Sichuan, China
• ²Sichuan University, Chengdu, Sichuan Province, China
• ³Hunan University of Technology and Business, Changsha Shi, Hunan Province, China
• ⁴Renmin University of China, Beijing, China

Background: Since 2017, cerebral infarction (CI) has been a leading cause of mortality in China, with escalating 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, its implementation China faces considerable hurdles due to the country's vast geographical size, regional economic disparities, and heterogeneous disease spectrum.Objective: This study aims to propose 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.Methods: Using hospitalization data from 111,025 CI patients collected from the Healthcare Security Administration of a western Chinese city between 2016 and 2018 (pre-DRG implementation period), we developed a two-stage DRG method. In the first stage, regression analysis identified and prioritized comorbidities and complications influencing medical costs. In the second stage, a decision tree algorithm established standardized classification protocols for DRG grouping, ensuring regional adaptability.The 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), below reached 100%, satisfying the technical criteria for DRG categorization. This optimization reduced the number of DRG groups from 18 to 4 and increased the proportion of groups with CV<0.8 from 67% to 100%, signifying a substantial enhancement in group heterogeneity compared to the existing grouping method CHS-DRG.This study demonstrates the effectiveness of our propose two-stage method using real data. Implementation of this localized method in the target city could yield potential savings of USD 8.59 million, outperforming the existing CHS-DRG method. These findings suggest that this adaptive method may serve as a scalable strategy for resource-limited regions undergoing healthcare system reforms.