Objective: The paper attempts to evaluate the grouping effectiveness of Shanghai’s Case-Based Payment Version 2.0 and to explore optimization pathways based on the DRG-DIP mapping, providing empirical evidence for advancing the DRG-DIP integration. Methods: Using inpatient discharge settlement data in Shanghai from 2021 to 2023, a mapping relationship between DRG and DIP was constructed. The coefficient of variation (CV) and reduction in variance (RIV) were applied to assess within-group homogeneity and subdivision potential. A four-quadrant analytical framework was established to classify grouping effectiveness. Typical disease groups were selected, and detailed validation was conducted based on procedure types or principal diagnosis/principal procedure. Results: The overall grouping performance was satisfactory: 73.91% of groups met the homogeneity criterion (CV ≤ 1), and 90.12% showed limited withingroup structural variation (RIV ≤ 0.4). The proportion of groups failing to meet homogeneity standards was respectively 31.11% under traditional DRG rules and 22.52% under the underlying clustering approach. A total of 31 high-heterogeneity groups with subdivision potential were identified, with heterogeneity mainly driven by surgical complexity, anatomical site, benign or malignant nature of the tumor, and the use of high-cost consumables. Case analyses indicated that optimization of surgical groups should focus on stratification by procedural complexity, while clustering-based groups require the incorporation of diagnostic dimensions (such as benign or malignant nature of the tumor). In contrast, the scope for further refinement in medical (non-surgical) groups remains limited at the coding level. Conclusion: DRG-DIP mapping provides a novel perspective for evaluating grouping effectiveness. The Shanghai Version 2.0 is generally reasonable, and classification-based optimization is key to its further improvement. This approach may serve as a reference for the nationwide exploration of DRG-DIP integration.