DRG/DIP支付改革要求公立医院实现成本精细化、实时化管控,但传统模式面临核算粗放、管控滞后、数据割裂及临床协同不足等挑战。本研究基于认知赋能与决策优化的理论视角,探讨生成式人工智能（AI）赋能医院成本管控的创新路径。该技术可通过多模态数据处理实现病种成本动态核算;基于知识推理建立全流程动态管控闭环;并借助自然语言交互促进临床与成本管理的协同。本研究分析表明,生成式AI有潜力显著提升医院成本管控的精准性和效率,为支付改革下的运营优化提供技术支持。同时,研究识别出数据安全、“幻觉”风险、组织适配与成本效益等核心挑战,并提出了应对策略。本研究构建的“认知–决策”融合框架,旨在引导成本管控从末端控制向前端诊疗行为优化延伸,为生成式AI赋能价值医疗、助力临床路径精益化管理提供理论参考与实践指引。

The DRG/DIP payment reform requires public hospitals to achieve refined and real-time cost control. However, traditional models face challenges such as crude accounting, lagging control, data fragmentation, and insufficient clinical collaboration. From the theoretical perspectives of cognitive empowerment and decision optimization, this study explores innovative pathways for generative artificial intelligence (AI) to empower hospital cost control. This technology can enable dynamic accounting of disease-specific costs through multimodal data processing, establish a closed-loop, dynamic control system for the entire process based on knowledge reasoning, and promote collaboration between clinical and cost management through natural language interaction. Our analysis indicates that generative AI has the potential to significantly enhance the precision and efficiency of hospital cost control, providing technical support for operational optimization under the payment reform. Simultaneously, the study identifies core challenges including data security, "hallucination" risks, organizational adaptation, and cost-effectiveness, and proposes corresponding strategies. The "cognition-decision" integrated framework constructed in this study aims to guide cost control to extend from end-point control to the optimization of front-end clinical behaviors, offering theoretical reference and practical guidance for generative AI to empower value-based healthcare and facilitate the lean management of clinical pathways.