Supplementary information files for "Academia Europaea’s guidelines for the visualization of clinical outcomes"

In the increasingly data-rich domains of healthcare and health policy, translating research findings into actionable decisions and bridging the gap between complex research findings and effective policy decisions remains crucial. While there has been a substantial rise in peer reviewed scientific publications over the past three decades, this surge in data and knowledge has not consistently been translated into corresponding reductions in avoidable mortality rates.[1]One potential reason is that policymakers, healthcare practitioners, and researchers encounter abundant clinical data, often needing specialized knowledge to interpret it fully.[2] Clinical outcomes are complex to assess, requiring consideration of efficacy, safety, and cost effectiveness to inform effective healthcare policy. Despite advances in research methodologies and data collection, the difficulty in synthesizing multifaceted information and translating it into practical, real-world applications remains a major hurdle for healthcare providers and policymakers alike.Healthcare decision-making involves a delicate balance between multiple factors: efficacy, safety, cost, and patient preferences, among others. Traditional data visualization techniques, such as forest plots, Kaplan-Meier survival curves, heat maps, decision-tree pathways, traffic-light models, and Gantt charts, have long been employed to present research findings. While these tools serve their purpose within the academic community, they often fail to provide clear, actionable insights for policymakers, hospital administrators, and frontline clinicians tasked with making real-world decisions. This gap between the production of scientific knowledge and its translation to healthcare systems creates barriers to improve patient outcomes and optimize healthcare delivery.In response to this challenge, Academia Europaea (AE) launched a project to develop an innovative and intuitive tool for visualizing clinical research implications (Figure 1A). The Hegyi et al. – Page 3 result of this initiative is the “Ring Diagram Model”, which serves as a novel approach to distilling complex, multidimensional data into a structured and easily interpretable format. The model facilitates decision-making by presenting clinical outcomes through concentric color-coded rings, clearly delineating key dimensions such as efficacy, safety, and cost. This model offers a practical solution for translating clinical research into actionable, evidence based decisions at all levels of healthcare, from policy to practice<br><br>© Springer Nature America, Inc. All rights reserved