The EBHC Conference 2026 invites the global evidence community to reflect on how we generate, synthesise, and translate evidence in a rapidly evolving landscape. While this year’s focus embraces the transformative potential of artificial intelligence and digital innovation, the conference welcomes contributions across the full spectrum of evidence-based healthcare. Our diverse program ensures balanced insights into evidence generation, synthesis, and translation, addressing a wide spectrum of contemporary challenges and innovations.
Evidence generation forms the cornerstone of evidence-based healthcare (EBHC). At the 11th EBHC Conference, we dive deep into methods and strategies for creating robust and reliable health evidence. From innovative clinical trial designs and observational studies to leveraging real-world data, we explore how high-quality, accurate, and relevant evidence can inform better clinical and policy decisions.
Key areas include:
- Innovative study designs, including platform trials and target trial emulation methodologies
- Real-world evidence approaches utilize large datasets and advanced analytics
- Stakeholder-driven research processes engaging patients, clinicians, and policymakers
- Ethical frameworks and equity considerations guiding health research practices
- AI-driven innovations enhancing the accuracy and efficiency of research design and execution
We particularly value research that demonstrates impact with a focus on trustworthiness, integrity, and real-world applicability.
Bringing clarity to complexity, evidence synthesis is critical in distilling large amounts of data into actionable insights. Our sessions focus on cutting-edge methods and techniques for clinical guidelines, systematic reviews, and all other evidence syntheses. Discover how advances in artificial intelligence, automation, and methodological rigor contribute to faster, transparent, and more accurate synthesis processes.
Topics include:
- Advanced synthesis methods, including network meta-analyses, qualitative syntheses, and mixed-method approaches
- Rapid and living reviews designed to meet evolving stakeholder needs with timely updates
- Enhanced tools and approaches for bias and certainty assessment
- Transparency and effective patient and public involvement in reviews and guidelines, alongside robust conflict-of-interest management
- Artificial intelligence and automation techniques enhancing the speed, accuracy, and scalability of synthesis processes
We seek work that bridges methods and practice: scalable, living synthesis approaches that directly inform real-time decisions and health policy.
Evidence translation ensures that research findings effectively improve patient care and health outcomes. Explore practical approaches, tools, and frameworks that bridge the gap between research and practice. Learn from leading experts about how to implement evidence-informed guidelines, enhance healthcare delivery, and foster cultures that value evidence-based decision-making.
Core strands:
- Implementation strategies designed to maximise evidence uptake across policy, service, and clinical environments
- Digital and AI-enabled delivery methods illustrated through case studies highlighting improved patient outcomes and enhanced healthcare efficiency
- Guidelines and policy adaptation techniques tailored to specific contexts
- Shared decision-making tools including patient-focused aids, communication strategies, and clinician-patient decision frameworks
- Approaches to improving health literacy and effectively combating misinformation through evidence-informed communication strategies
- Ethical considerations and equitable implementation practices for AI-supported decision-making in diverse healthcare settings
Join us in Taormina to connect with international experts, expand your professional network, and enhance your skills in these essential pillars of evidence-based healthcare.
Let’s work together to reinforce the linkages in this ecosystem and ensure that every piece of evidence is generated, synthesised, and translated for maximum impact.
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