Hutan is a senior clinician-scientist and surgeon. His career has seen him lead large research and clinical teams, working at population and precision medicine levels, to achieve the highest quality real world evidence and translational healthcare. He has >400 publications with extensive experience in strategy and driving national and international health policy with cabinet-level politicians, civil servants, and heads of state.
He qualified in medicine at University College London where he was also awarded an honours degree in Immunology and Cell Pathology. His training in surgery also led to the award of the Arris & Gale lectureship at the Royal College of Surgeons of England. He a holds a PhD in Computational Physiology and Metabolic Surgery from Imperial College London and a health economics-focused MBA with Distinction from Warwick Business School.
Prior to the Research Trials Network, he was simultaneously Chief Scientific Adviser at the Institute of Global Health Innovation, Imperial College London; an Honorary Senior Clinical Fellow in Surgery at Imperial College Healthcare NHS Trust; Academic Lead for the Big Data Analytical Unit, Imperial College London; Deputy Director (Academic Lead) for the NHS Digital Academy; Co-Founder and Chief Medical Officer at Oxford Medical Products Ltd.
Classical Trial Designs of targeted cohorts and also large real-world approaches in addition to novel Pragmatic Trial designs ranging from Cluster approaches, Adaptive trials and randomised trials with Practice Preference randomisation and Real-time Automated Enrollment and Randomisation. classical, monied and policy Delphi techniques.
Digital and Electronic healthcare, vigilance and monitoring of classical trials of medical and surgical interventions. Focused trials of diagnostic accuracy, effectiveness and implementation of novel digital solutions ranging from decision tools, diagnostic technologies, remote monitoring and digital and robotic surgery.
Next generation real-time AI and ML algorithmic approaches: Support vector machine, deep learning, logistic regression, discriminant analysis, decision tree, Random forest, linear regression, naïve Bayes, K-nearest neighbor, hidden Markov, genetic algorithms