基于深度循证医学理念构建新一代循证决策生态系统
Construction of a new generation of evidence-based decision-making ecosystem based on the concept of deep evidence-based medicine
孙凤 1章萌 2詹思延3
作者信息
- 1. 北京大学公共卫生学院流行病与卫生统计学系,北京 100191;重大疾病流行病学教育部重点实验室(北京大学),北京 100191;北京大学循证医学中心,北京 100191;新疆医科大学,乌鲁木齐 830017
- 2. 北京大学公共卫生学院流行病与卫生统计学系,北京 100191;重大疾病流行病学教育部重点实验室(北京大学),北京 100191;北京大学循证医学中心,北京 100191
- 3. 北京大学公共卫生学院流行病与卫生统计学系,北京 100191;重大疾病流行病学教育部重点实验室(北京大学),北京 100191;北京大学循证医学中心,北京 100191;北京大学第三医院临床流行病学研究中心,北京 100191
- 折叠
摘要
传统循证医学在医疗实践和卫生决策中发挥了重要作用,与此同时也不断暴露出证据生成效率低、覆盖范围窄、整合策略不完善等不足,难以及时服务临床诊疗及监管决策,因此亟须根据前沿技术的发展,对循证医学理念进行深入拓展和完善.2023年提出的深度循证医学倡导运用最新人工智能和自然语言处理技术,全面扩大证据的广度、深度和可整合性,提高证据生产和整合效率.基于深度循证医学理念建立新一代循证决策生态系统具有广阔的实际应用前景,可推动循证医学证据检索、生产、整合、传播、转化和应用等各环节的发展,深入挖掘影像学、多组学和真实世界数据,增大真实世界证据的利用潜力,建立动态文献管理平台和决策辅助工具,减少资源浪费,促进证据流动.借助此系统有助于获取以个体为中心的临床综合证据进行精准决策,并在人才培养、循证教学改革、科普宣传等方面发挥重大作用,最终推动同一健康的实现.
Abstract
Traditional evidence-based medicine has been essential in medical practice and health decision-making.However,it has also continuously exposed shortcomings such as low efficiency in evidence generation,narrow scope of coverage,and imperfect integration strategies,making it challenging to serve clinical diagnosis and treatment and regulatory decision-making.Therefore,it is urgent to adapt to the development of cutting-edge technology and to expand and improve the concept of evidence-based medicine.Deep evidence-based medicine proposed in 2023 aims to advocate the innovative use of the latest artificial intelligence and natural language processing technologies,comprehensively expanding the breadth,depth,and integrability of evidence and improving the efficiency of evidence generation and integration.Building a new generation of evidence-based decision-making ecosystems based on deep evidence-based medicine has broad prospects for practical application.It can promote the development of evidence retrieval,generation,integration,dissemination,transformation,and application,deeply explore imaging,multi-omics,and real-world data to increase the utilization potential of real-world evidence,establish dynamic literature management platforms and decision support tools,reduce resource waste,and promote evidence flow.Utilizing this system can help obtain individual-centered comprehensive clinical evidence and play a significant role in talent training,reforming evidence-based teaching,popularizing science,and ultimately promoting the goal of"One Health".
关键词
深度循证医学/循证决策生态系统/证据整合/随机对照试验Key words
Deep evidence-based medicine/Evidence-based decision ecosystem/Evidence integration/Randomized controlled trials引用本文复制引用
出版年
2024
NETL
NSTL
万方数据