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

Area of Science:

• Health Informatics
• Clinical Epidemiology
• Data Science

Background:

• Clinical evidence generation is slow and inefficient, widening the gap between healthcare needs and available data.
• Current Learning Health Systems (LHS) struggle to integrate rapidly generated Real-World Data (RWD) effectively.
• The demand for high-quality clinical evidence for decision-making consistently outpaces supply.

Purpose of the Study:

• To propose a novel framework for leveraging Real-World Data (RWD) across the entire clinical evidence lifecycle.
• To enhance the efficiency and scalability of evidence generation and implementation within a Learning Health System (LHS).
• To establish a continuous learning mechanism for improving clinical practice and patient outcomes.

Main Methods:

• Integration of RWD into the clinical evidence lifecycle through four closed feedback loops.
• Utilizing modern data science and informatics to power the framework.
• Focusing on research prioritization, study design, guideline development, evaluation, and shared decision-making.

Main Results:

• The proposed framework enables rapid responsiveness to emerging health data and evolving healthcare needs.
• Facilitates timely development and optimization of clinical guidelines.
• Supports sustained improvements in clinical practice and patient outcomes through continuous learning.

Conclusions:

• A new informatics-supported framework can significantly enhance the clinical evidence lifecycle.
• Effective integration of RWD via feedback loops improves scalability and efficiency.
• This approach is crucial for advancing the Learning Health System vision and optimizing healthcare delivery.