The impact of diagnosis-related groups on medical costs, service efficiency, and healthcare quality in Meishan, China: An interrupted time series analysis - Jinyuan Li; Meihua Peng; Shihu Deng; Jingwen Wu; Wangsu Zhou (PLoS ONE, 2025)
Background In January 2019, the Diagnosis-Related Groups (DRG) payment system was introduced in Meishan, China. Using the medical insurance records from 2017 to 2022, we evaluated the impact of the DRG system on medical costs, service efficiency and healthcare quality. Methods The sample was divided into three periods: Before DRG reform (2017–2018), the first period of DRG reform (2019–2020), and the second period of DRG reform (2021–2022). We employed an Interrupted Time Series (ITS) model to analyze the monthly changes in total hospital costs, patient cost-sharing, patient sharing ratio, length of stay, and 30-day readmission rate during both periods of DRG reform. Results In the first period of DRG reform, total hospital costs decreased by 1.23% per month (95% CI, 0.88%-1.59%), patient cost-sharing decreased by 1.46% per month (95% CI, 1.09%-1.83%), patient sharing ratio decreased by 0.23% per month (95% CI, 0.06%-0.40%), and length of stay decreased by 0.56% per month (95% CI, 0.27%-0.84%). The monthly change in 30-day readmission rate was not statistically significant (-0.11%, 95% CI, -0.73%-0.50%). In the second period of DRG reform, all monthly changes were not statistically significant. Conclusions This study assessed the impact of the DRG payment system on medical costs and service efficiency. The results showed that DRG reduced total hospital costs, patient cost-sharing, patient sharing ratio, and length of stay, but did not significantly affect the rising 30-day readmission rates. Over time, the impact of DRG on cost control and service efficiency stabilized. However, unintended hospital behaviors may have emerged, warranting further investigation. The findings suggest that policymakers should strengthen clinical practice regulation, improve the DRG payment system, and continuously monitor healthcare quality trends.