This paper analyzed clinical studies, systematic reviews and clinical practice guidelines on the treatment of COVID-19 with antiviral drugs. It elaborated on the evidence ecosystems in these studies following the "efficacy, evidence-based practice, effectiveness" (3E) model that considered five elements: research hypothesis, research evidence, big data from healthcare facilities, real-world data, and real-world evidence. Finally, this paper summarized the experience in the production, transformation and application of evidence. This paper could help clinicians conduct high-quality clinical research and provide good clinical practice based on the best currently available evidence.
The first Global Evidence Summit (GES) was held in Cape Town in South Africa from September 13th to November 16th, 2017. This paper interprets the construction of digital and trustworthy evidence ecosystem which was proposed to be established by the GES.
The contradiction between the exponential growth of medical knowledge and the delay in traditional evidence synthesis has necessitated the living evidence (LE) synthesis model, which focuses on continuous updates. The concept of LE synthesis has evolved into a dynamic full-lifecycle spectrum ranging from evidence maps and systematic reviews to clinical guidelines. Although the framework by Bendersky et al. established a methodological baseline for regulating this complex system, its reliance on human labor and a "process-driven" logic creates significant efficiency bottlenecks when dealing with massive heterogeneous data. In the context of the technological revolution driven by AI and large language models (LLMs), this paper analyzes the limitations of current frameworks and demonstrates the necessity of evolving from "mechanical process execution" to a paradigm of "human-machine collaborative cognitive augmentation". This shift demands a restructuring of how evidence is discovered and integrated. Furthermore, by combining China's advantages in clinical data resources with current standardization efforts, we explore localized pathways that integrate technological foresight with evidence-based rigor. This aims to build a smart, dynamic evidence ecosystem dedicated to high-quality health decision-making.