I was drawn to RWE because it sits so close to the realities of clinical care. In oncology, decisions are often complex, and routine care often includes patients and circumstances that are less visible in clinical trials. That gap really interests me. I became increasingly motivated by the idea that routinely collected data, if used carefully and responsibly, can help us understand cancer care as it is actually delivered, highlight variation in practice and outcomes, and complement clinical trials in areas where conventional research can be difficult.

My background combines biomedical science, bioinformatics and health data science. I completed a BSc in Biomedical Science at the University of Sheffield and an MSc in Bioinformatics at the University of Manchester, before joining the Data Scientist Development Programme at Leeds Institute for Data Analytics. My PhD at the University of Leeds focused on federated learning for outcome prediction modelling in anal cancer, and included establishing atomCAT, an international consortium bringing together 16 radiotherapy treatment centres from Europe and Australia.

In my current role, I build data infrastructure for collaborative oncology research. Over the last three years, I have led the development of the Leeds Teaching Hospitals NHS Trust OMOP database, with a focus on representing detailed oncology and non-oncology information in a standardised format. This work supports OMOP-based studies nationally through HERON-UK, across Europe through DIGICORE’s DigiONE network, and internationally through FALCON. The aim of my work is to generate robust real-world evidence without losing the clinical detail that makes oncology data meaningful.

Next, I am further developing the LTHT OMOP database by adding more in-depth clinical data, including biomarkers and laboratory test results. I am also involved in new studies across existing networks, including a pan-cancer study within DIGICORE’s DigiONE network and a metastatic bladder cancer study within FALCON, exploring how RWE can be used to evaluate guideline adoption and clinical practice. Looking ahead, I hope to build new collaborations that connect data, clinical expertise and research questions in ways that can have practical impact for cancer services and patients.

My advice would be to stay curious and be open to learning from different disciplines. Real-world evidence and precision oncology sit at the intersection of clinical care, data science, epidemiology and informatics, so no single person has all the answers. Building a good understanding of the clinical context is just as important as developing technical skills. I would also encourage people to get involved in collaborative projects early, as this field moves forward through shared standards, shared questions and continued learning from each other.

People might assume my work is mainly about coding or analysing data, but a big part of it is translation: understanding clinical workflows, interpreting how information is recorded, and working out how to represent it in a standardised way without losing meaning. In oncology especially, the detail really matters, so much of my work involves bridging the gap between messy real-world clinical data and research-ready evidence.