CHILLI member
David Olawade
MPH, FRSPH, SFHEA
About
A clinical researcher contributing expertise in healthcare improvement, evidence-informed decision-making, clinical evaluation, and responsible digital health innovation.
Profile
David Bamidele Olawade is the Founder and CEO of CHILLI (Consortium for Healthcare Intelligence, Learning, Leadership, and Innovation), bringing together expertise in AI-driven healthcare, clinical research, public health, and academic leadership.
A Senior Fellow of the Higher Education Academy (SFHEA) and Fellow of the Royal Society for Public Health (FRSPH), David has authored 200+ peer-reviewed publications, attracting 6,000+ citations with an h-index of 39. His research spans artificial intelligence in healthcare, predictive analytics, digital health, clinical decision support, mental health, health equity, and global public health.
Within the NHS, David has contributed to the development and deployment of AI-enabled solutions for clinical research and leads research exploring AI readiness, digital inclusion, and equitable healthcare innovation. He also holds visiting lecturer roles at the University of East London and York St John University, supporting postgraduate education and early-career researcher development.
Through CHILLI, David is focused on translating cutting-edge research into responsible, equitable, and scalable healthcare solutions, building partnerships that connect research, technology, clinical practice, education, and real-world impact.
Areas of expertise
Research interests
Clinical decision support, health services research, healthcare improvement, digital health evaluation, implementation research, patient-centred care, responsible innovation, and evidence-informed clinical practice.
Research leadership
Lead research areas
Research area
AI-powered Healthcare Solutions
Developing and evaluating artificial intelligence solutions that support smarter, safer, and more effective healthcare.
Explore research area →Connected work
Projects
Responsible AI Readiness for Clinical Decision Support
This project will examine how healthcare organisations can assess their readiness to introduce responsible AI-supported clinical decision tools. It will consider governance, data quality, clinical safety, transparency,…
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Predictive Analytics for Earlier Identification of Healthcare Risk
This project will explore how routinely collected healthcare data can be used to identify patient or service-level risks earlier. The work will focus on responsible predictive modelling,…
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