CLEAR: A vision to support clinical evidence lifecycle with continuous learning

Human knowledge of diseases, treatments, and prevention techniques is constantly evolving. The generation of clinical evidence using randomized controlled trials on human subjects occurs notably slowly and inefficiently. The Learning Health System (LHS) has been proposed to facilitate the continuous improvement of individual and population health through a cycle of knowledge, practice, and data. However, the gap between the demand for high-quality evidence to support clinical decisions and the available evidence continues to enlarge. While the current LHS vision articulates the integration of Real-World Data (RWD), the rapid generation of RWD often outpaces the rate of effective evidence synthesis and implementation. Considering this, we propose a new framework that more effectively leverages RWD to support the entire clinical evidence lifecycle through a continuous learning mechanism. This framework, powered by modern data science and informatics, offers enhanced scalability and efficiency. In this vision, specifically, RWD is integrated into the clinical evidence lifecycle via four closed feedback loops: 1) guiding research prioritization and study design, 2) facilitating clinical guideline development, 3) assisting guideline evaluation, and 4) supporting shared decision-making. Our framework enables rapid responsiveness to emerging health data and evolving healthcare needs, timely development of clinical guidelines to optimize clinical recommendations, and sustained improvements in clinical practice and patient outcomes. This vision calls for informatics support for an efficient, scalable, and stakeholder-aware clinical evidence lifecycle.