人工智能在病案管理中的应用现状及展望OA

Against the backdrop of escalating medical complexity and soaring patient volumes,traditional medical record management systems face significant challenges,including the intensified burden of clinical documentation,inefficiencies in quality control,and the underutilization of data value.By synthesizing global practices,this paper systematically analyzes the application value and inherent challenges of artificial intelligence(AI)across critical phases of medical record management.Core scenarios had been examined including AI-assisted documentation,intelligent quality control,the automated management of medical record face sheets,the construction of quality indicator systems,and unlocking the potential of medical record data.The integration of AI remarkably improves the quality and efficiency of medical record management,assists in establishing intelligent risk prevention systems,and advances data governance and real-world evidence(RWE)research.However,the widespread adoption of AI is still constrained by multidimensional hurdles,such as technical limitations,privacy concerns,and regulatory compliance risks.To facilitate a sustainable AI transition,this paper proposes a tiered governance framework.At the macro-level,an ecosystem integrating policies,regulations,and collaborative innovation must be established.At the meso-level,hospitals should drive institutional innovation through digital value transformation and technical integration.At the micro-level,operational workflows must be standardized,with an emphasis on physicians'ultimate accountability as final signatories and the enhancement of competency-based training.