基于生成式大语言模型的病历内涵质控智能助手的构建与应用OA

Objective To address the issues of traditional medical record quality control tools,such as reliance on rule engines,low error detection rates,and insufficient semantic understanding,this study aims to explore the application value of large language models in the quality control of the connotation of medical records.Methods Based on the Qwen2.5-32B-Instruct generative large language model,a Medical Record Connotation Quality Control Assistant was constructed using the LoRA efficient fine-tuning technology and the Chain of Thought method.It integrates functions of intelligent perception,real-time feedback,and prompt optimization,covering core quality control tasks such as the consistency of the chief complaint and the sufficiency of diagnostic evidence;the effectiveness of the assistant was evaluated through practical application and observation of its operation effect.Results Statistics on the data of 3 months before and after the launch of the Medical Record Connotation Quality Control Assistant showed that the number of quality control items increased by 24.6%,the medical record quality control appeal rate decreased by 53.9%compared with that before the launch,the false positive rate decreased by 62.6%.Conclusion Generative large language models can significantly improve the accuracy and efficiency of the quality control of the connotation of medical records,providing intelligent support for the improvement of medical quality.