清晰:一个愿景,以支持临床证据生命周期与持续学习的愿景
Yilu Fang1, Gongbo Zhang1, Fangyi Chen1
1Department of Biomedical Informatics, Columbia University, New York 10032, USA.
Journal of biomedical informatics
|July 31, 2025
概括
一个新的框架使用现实世界的数据 (RWD) 来加快临床证据的生成. 这种方法增强了学习健康系统 (LHS),以获得更好的患者护理和更快的医疗进步.
科学领域:
- 医疗信息学 医疗信息学
- 临床流行病学临床流行病学
- 数据科学数据科学数据科学
背景情况:
- 临床证据生成速度缓慢且效率低下,扩大了医疗保健需求和可用数据之间的差距.
- 当前的学习健康系统 (LHS) 努力有效地整合快速生成的真实世界数据 (RWD).
- 对于决策的高质量的临床证据的需求始终超过供应.
研究的目的:
- 为在整个临床证据生命周期中利用真实世界数据 (RWD) 提出一个新的框架.
- 在学习健康系统 (LHS) 中提高证据生成和实施的效率和可扩展性.
- 建立一个不断学习的机制,以改善临床实践和患者的结果.
主要方法:
- 通过四个封闭的反循环将RWD整合到临床证据生命周期中.
- 利用现代数据科学和信息学为框架提供动力.
- 专注于研究优先级,研究设计,准则制定,评估和共享决策.
主要成果:
- 拟议的框架允许快速响应新出现的健康数据和不断变化的医疗保健需求.
- 促进临床指南的及时开发和优化.
- 通过持续学习,支持持续改善临床实践和患者结果.
结论:
- 一个新的信息技术支持的框架可以显著提高临床证据生命周期.
- 通过反循环有效集成RWD可以提高可扩展性和效率.
- 这种方法对于推进学习健康系统的愿景和优化医疗保健服务至关重要.
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