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

Background: Since 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. Objective: This 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. Methods: Using 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. Results: 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), 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). Conclusion: This 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.