人工智能技术在超说明书用药循证中的应用研究

Application of artificial intelligence technology in evidence-based off-label drug use

超说明书用药在临床中广泛存在。2022年3月1日生效的《中华人民共和国医师法》要求,超说明书用药必须基于循证医学证据才能实施。循证决策需要对相关海量证据进行查找、筛选、评价,但医学证据的指数倍增长使得海量的数据信息成为循证的瓶颈和挑战。人工智能(AI)技术的应用为解决这一问题带来了新的机遇,其可实现自动化证据搜索、分析、汇总和追踪,从而帮助临床药师更快速、更准确、更全面地获取关键信息,并降低人为误差的风险。聚焦超说明书用药循证决策中的痛点,基于BioBERT、T5、UnifiedQA和GPT-2等AI技术,以满足循证流程的语义搜索、文本分类、信息抽取、决策内容生成等功能,笔者最终开发了EviMed系统,实现了全球海量医学证据的实时搜索并自动进行分析和AI辅助决策,具备快速、全面、准确、可自动更新的特点。该系统已广泛应用于全国多家三甲医院,促进了超说明书用药循证决策的高效化和规范化。

Off-label drug use is widely practiced in clinical settings.In 2022,the Law on Doctors of the People's Republic of China mandated that off-label medication must be supported by evidence-based medical evidence before implementation.However,evidence-based decision-making involves searching,screening,and evaluating vast amounts of relevant evidence.The exponential growth of medical evidence has become a bottleneck and challenge for evidence-based decision-making due to the massive amount of data involved.Fortunately,the application of artificial intelligence(AI)technology presents new opportunities to address this issue.AI technology enables automatic evidence search,analysis,summarization,and tracking,assisting clinical pharmacists in obtaining key information quickly,accurately,and comprehensively while minimizing the risk of human error.This paper focuses on addressing the challenges faced during evidence-based decision-making for off-label medication.By utilizing AI technologies such as BioBERT,T5,UnifiedQA,and GPT-2,we designed functionalities such as semantic search,text classification,information extraction,and generation of decision-making content aligned with the evidence-based process.As a result,the EviMed system has been developed to facilitate real-time search,automatic analysis,and AI-assisted decision-making based on a vast array of global medical evidence.The system is fast,comprehensive,accurate,and capable of automatic updates.Widely adopted in numerous tertiary hospitals nationwide,the system has significantly enhanced the efficiency and standardization of evidence-based decision-making for off-label medication.