Abstract
PURPOSE: As one of the pioneering pilot cities in China's extensive Diagnosis Related Groups (DRG) -based prepayment reform, Beijing is leading a comprehensive overhaul of the prepayment system, encompassing hospitals of varying affiliations and tiers. This systematic transformation is rooted in extensive patient group data, with the commencement of actual payments on March 15, 2022. This study aims to evaluate the effectiveness of DRG payment reform by examining how it affects the cost, volume, and utilization of care for patients with neurological disorders. PATIENTS AND METHODS: Utilizing the exogenous shock resulting from the implementation of the DRG-based prepayment system, we adopted the Difference-in-Differences (DID) approach to discern changes in outcome variables among DRG payment cases, in comparison to control cases, both before and following the enactment of the DRG policy. The analytical dataset was derived from patients diagnosed with neurological disorders across all hospitals in Beijing that underwent the DRG-based prepayment reform. Strict data inclusion and exclusion criteria, including reasonableness tests, were applied, defining the pre-reform timeframe as March 15th through October 31st, 2021, and the post-reform timeframe as the corresponding period in 2022. The extensive dataset encompassed 53 hospitals and encompassed hundreds of thousands of cases. RESULTS: The implementation of DRG-based prepayment resulted in a substantial 12.6% decrease in total costs per case and a reduction of 0.96 days in length of stay. Additionally, the reform was correlated with significant reductions in overall in-hospital mortality and readmission rates. Surprisingly, the study unearthed unintended consequences, including a significant reduction in the proportion of inpatient cases classified as surgical patients and the Case Mix Index (CMI), indicating potential strategic adjustments by providers in response to the introduction of DRG payments. CONCLUSION: The DRG payment reform demonstrates substantial effects in restraining cost escalation and enhancing quality. Nevertheless, caution must be exercised to mitigate potential issues such as patient selection bias and upcoding.