I've always been drawn to solving complex problems at the intersection of biology, technology, and healthcare. Early in my career managing disease outbreaks, I saw how critical data accuracy was and how systems often couldn't communicate to each other. As I moved into working on clinical applications, that problem became undeniable. I became fascinated with solving it, knowing the potential impact on the other side. At IQVIA through DigiONE, I found where it converges: bridging what clinicians need and what data can deliver for precision oncology.

I’m a biomedical scientist with master’s degrees in Genetics & Genomics, Bioinformatics & Biostatistics, and Epidemiology & Public Health. I began my career in disease surveillance and public health before moving into clinical data management, where I worked on developing an epidemiological surveillance system, translating public health requirements into practical solutions. I now focus on data discovery and integration across healthcare systems at IQVIA, supporting areas such as precision oncology and real-world evidence, and helping make healthcare data standardised and actionable.

My role focuses on understanding healthcare data and making it work across systems. I work with clinicians to understand what the data represents, then analyse and discover its structure, quality, meaning, and potential. I do semantic mapping and data transformation with data engineers, converting between different data representations so clinical concepts align with technical standards. Together we bridge clinical and technical perspectives, ensuring integrations are built on accurate understanding of the data.

I’m building foundational knowledge for clinical data interoperability at scale, working towards smarter and more streamlined data discovery and transformation. I’m particularly interested in collaborations that push the boundaries of how healthcare data can be standardised and made truly actionable. My vision is to become a reference point for clinical data interoperability, helping organizations and networks to navigate the complexity of making disparate data work together effectively.

A few lessons have stood out through my experience in this field. Build both technical and clinical foundations, they reinforce each other. Just as importantly, learn how to work with people from diverse backgrounds: clinicians, engineers, data scientists, and project managers. Each perspective is essential. Learn to translate between these worlds. Listen carefully, ask thoughtful questions, and take the time to understand how others think. The best solutions emerge from collaboration, where different perspectives and expertise come together. It’s the kind of work where you’re constantly learning.

Data integration may look straightforward in theory, but in practice, every dataset brings something unexpected. The biggest surprise is the variability; two centres can represent the same clinical concept in completely different ways. It’s not just formats, but levels of detail, interpretations, and underlying assumptions. What seems like a simple mapping task quickly becomes a puzzle. The rewarding part is solving it, each challenge reveals something new about how healthcare truly works.