目的  探讨通过优化病案首页质控体系提高误入DRG低权重组病例转出率的效果。方法  采用PDCA循环法，通过实施分层级编码培训、基于AI赋能的专项质控模式及智能化反馈机制构建等系统性地改进措施优化质控体系。通过对比分析质控系统优化前后（2022年1—7月和2023年1—7月）DRG低权重组病例的病案首页质控过程、“经质控低权重病例入组率”和“误入低权重组病例转出率”等指标，评估质控体系优化的实施效果。结果  质控体系优化后，低权重组病例转出率由3.27%提升至4.15%（P=0.018），经质控低权重病例入组率由16.98%降至14.96%（P＜0.001）。结论  AI赋能的专项质控、分层级编码培训与智能化反馈机制三项措施并举可以系统优化质控体系，进而提升DRG低权重组病例转出率。

       Objective  To investigate the effect of optimizing the medical  record front  page  quality control  system on improving the transfer-out rate of cases mistakenly assigned to low-weight DRG groups．Methods  The Plan-Do-Check-Act（PDCA）cycle methodology was employed．Systemic improvements were implemented to  optimize the medical  record front page quality control system，including hierarchical coding training，innovation of a  specialized quality control model based on AI empowerment，and establishment of an intelligent feedback mechanism．The implementation effectiveness was evaluated by comparative analysis of the following indicators before（January-July 2022）and after（January-July 2023）optimization：the medical record quality control process for low-weight DRG cases，the  rate of low-weight cases assigned to groups after quality control，and the transfer-out rate of cases mistakenly entering low-weight groups．Results  After optimizing the medical record front page quality control system，the transfer-out rate of cases from low-weight groups increased from 3.27% to 4.15%（P=0.018），while the rate of low-weight cases assigned to groups after quality control decreased from 16.98% to 14.96%（P＜0.001）．Conclusions  Implementing a three-pronged approach—AI-powered specialized quality control，hierarchical coding training，and an intelligent feedback mechanism—can systematically optimize the medical record front page quality control system，thereby improving the transfer-out rate of cases mistakenly assigned to low-weight DRG groups．