IDEAL Framework Surgical Research Trials with Peter McCulloch

IJGC Podcast - A podcast by BMJ Group - Mondays

In this episode of the IJGC podcast, Editor-in-Chief Dr. Pedro Ramirez, is joined by Dr. Peter McCulloch to discuss the IDEAL Framework. Dr. McCulloch is Professor of Surgical Science & Practice at Oxford University. He attended medical school in Aberdeen and underwent his surgical training in Glasgow before moving to academic posts in Liverpool and then Oxford. An Upper GI cancer surgeon by background, he is the Chair of the IDEAL Collaboration, an international network of surgeons, scientists, industrial partners and patients whose goal is to improve the methodology for evaluating surgery, therapeutic devices and other complex treatments. He is also the Co-Editor-in-Chief of BMJ Surgery, Interventions, & Health Technologies (sit.bmj.com). His other major research interest is Human Factors in healthcare. He will ski anywhere, anytime, and is passionate about countering climate change. Highlights: • The IDEAL Framework & Recommendations provide an explanation of the natural history or life cycle of new procedures and devices, and a road map for how they should be evaluated at each stage in the journey. • It explains why Randomised Trials have been so difficult to do in surgery and shows that there are at least 2 preliminary steps in research (Development and Exploration) which are necessary to ensure that an RCT has a good chance of success. • IDEAL is potentially useful in surgical research at all stages, but also in device regulatory science, decisions on approving new procedures at institutions and coverage decisions • IDEAL is increasingly recognised and endorsed by major journals (Annals of Surgery, BMJ & Lancet) and professional groups (Royal College of Surgeons) and is being used by government agencies in several countries. • The IDEAL group continues to study and produce recommendations on difficult research issues such as: When is it OK NOT to do a randomised trial? How do we interpret studies using Real World Data? How should surgical robots be evaluated? And others.