Designing Inclusive Clinical Trials: A Reflection from the DSxHE Data Diversity Event

DSxHE wouldn't be what it is without volunteers like Felix Odhul, who generously give their time to grow our community. Here, Felix shares his personal reflections from his first Data Diversity Theme seminar. 

On Monday, 22 June 2026, I joined my first DSxHE Data Diversity Theme seminar whose theme was “Designing Inclusive Clinical Trials: A European Perspective.” This seminar was held in cooperation with READI – Research in Europe and Diversity Inclusion. The seminar sought to provide answers to the research question of how clinical trials could be designed to be  more inclusive and reflective of the populations targeted for the research. 

The discussion began with an introduction of READI and their work  to reframe clinical research within Europe through the inclusion of underrepresented and underserved populations within the research process. Moderated by Dr. Toral Gathani, the seminar was not only informative but also practical in nature. The seminar pointed out that some clinical trials do not adequately represent the diversity of the entire population. The exclusion of some populations from the research may hinder the generalizability of the research results, leading to disparities in access to healthcare services and innovation. 

The subsequent breakout session turned out to be one of the most fascinating elements of the event. This session included a debate about two main questions. One of the questions was concerned with which variables should be recorded in clinical studies in order to be able to report their population diversity.. Being well-versed in data science, I found this question especially relevant since it highlighted the importance of thinking through what is recorded and why. 

A number of important variables were outlined during the session. These variables included age, gender, ethnicity, disability, location, language, socio-economic status, among others. I consider socio-economic status to be an especially important variable as it influences one’s access to clinical trials sites, technology, transport, and time, among others. 

The second question looked at ways to minimize risks associated with under-reporting of relevant data in clinical research and population databases. The problem of missing data is not only a technical one but could be reflective of deeper issues such as poor design, lack of trust, unclear communication, or a system that has been built without the idea of inclusion as its basic assumption.

One moment from the discussion that particularly stayed with me was the conversation around under-reported data. It made me think more deeply about the fact that missing data is not always simply a technical gap to be corrected later. In some cases, it may reveal something more fundamental about how research has been designed, who has been considered from the beginning, and whether participants feel seen, respected, and able to trust the process. This resonated with me because, as someone interested in health data science, it reminded me that the quality of a dataset depends not only on how well it is analysed, but also on how thoughtfully and inclusively it is created.

"The quality of a dataset depends not only on how well it is analysed, but also on how thoughtfully and inclusively it is created."

My main takeaway from the event is that inclusion must be intentional. It is not enough to invite diverse participants at the end of the process. Inclusive clinical trials must be designed with diversity in mind from the very beginning. 

"Inclusion must be intentional. It is not enough to invite diverse participants at the end of the process. Inclusive clinical trials must be designed with diversity in mind from the very beginning."

The session has also changed how I think about the future of health data science. It reminded me that building fairer health systems will require more than advanced models, larger datasets, or better technical tools. It will require asking better questions from the start: Who is represented in the data? Who is missing? Why are they missing? And how might those gaps affect the decisions, policies, and innovations that follow? Going forward, these are questions I want to keep exploring in my own work, particularly in relation to machine learning, clinical decision support, and health systems in low- and middle-income countries.

As someone attending a DSxHE Data Diversity event for the first time, I honestly enjoyed the experience. I learned that beyond the variables we already know and value, there is so much more to understand when we listen to different perspectives. Data diversity is not only about numbers; it is about people, fairness, and making sure health research works for everyone. 

This event reminded me that behind every dataset are real lives. If clinical research is to truly support health equity, then the people represented in the data must reflect the people it aims to serve. 

"Data diversity is not only about numbers; it is about people, fairness, and making sure health research works for everyone"

If you weren't able to attend, the recording is available to watch here.

About the author

Felix Odhul is a Health Data Scientist passionate about using data, machine learning, and responsible AI to inform clinical decision-making, improve health outcomes, and advance health equity. His interests sit at the intersection of health data science, clinical decision support, and inclusive research, with a particular focus on how data-driven approaches can strengthen healthcare systems in low- and middle-income countries.

He is preparing to pursue a Master’s in Health Data Science, with research interests focused on the application of machine learning and health data analytics to improve healthcare decision-making. He is particularly interested in predictive modelling for early disease detection, maternal and child health analytics, health systems optimisation, and explainable AI for clinical decision support.

Felix is motivated by the belief that health data should reflect the people and communities it aims to serve. His engagement with the DSxHE Data Diversity Theme reflects his growing commitment to understanding who is missing from health datasets, how those gaps shape research and innovation, and what this means for health equity. Through his work and writing, he hopes to contribute to more inclusive, evidence-based approaches that improve patient outcomes, support better health policy, and strengthen healthcare systems across Africa.

About the Data Diversity Theme:

Health datasets often don’t reflect the diversity of the populations they aim to serve. This lack of representativeness can limit the generalisability of research, reduce the effectiveness of new tools, and risk widening health inequalities.

A partnership between Data Science for Health Equity and Cancer Research UK, the Data Diversity Theme brings together researchers, clinicians, funders, and patient advocates to co-create practical ways of embedding diversity across the research lifecycle. It is co-led by Dr Toral Gathani (University of Oxford) and Dr Brieuc Lehmann (UCL).