DRG付费与多学科协作对 ICU 抗菌药物使用强度的影响OA

Objective To investigate the dynamic impact of diagnosis-related group(DRG)payments reform and multidisciplinary team(MDT)management on the antimicrobial usage density(AUD)in intensive care unit(ICU),and construct an interrupted time series prediction model calibrated by the case-mix index(CMI),so as to break through the static limitations of traditional cross-sectional studies.Methods Data from ICU of a tertiary hospital from January 2021 to December 2024 were analyzed by the double interrupted time series(DITS)approach com-bined with an autoregressive integrated moving average(ARIMA)model.The implementation of DRG in October 2022 and the implementation of MDT management in August 2023 were identified as the key intervention points.Residual-calibrated sequences were constructed via CMI linear regression to control case complexity confounding,and model performance and predictive capability were assessed.Results The AUD exhibited a downward trend(β1=-1.70)after the implementation of DRG,while the trend reversed to an upward direction(γ1=3.38)after the implementation of MDT management,though with no statistical significance.After adjusting case complexity confounders via the CMI linear regression residual method,MDT management demonstrated a significant positive impact on the trend in antimicrobial usage.The ARIMA constructed based on the calibrated sequence demonstrated robust predictive performance.Conclusion The CMI-calibrated time-series model can effectively control confoun-ding and analyze the dynamic heterogeneity of policy interventions.The"confounding control-dynamic prediction"integrated framework constructed in this study provides a data-driven decision support tool for the refined manage-ment of antimicrobial agents.