David Amadi

What is your name?

My name is David Amadi   

Can you tell me about your background?

My education was in information technology, and later it was in public health informatics. I've worked on longitudinal health data for quite a time. Now I’ve moved to working on trying to bridge these issues of population health data using OMPO CDM (The Observational Medical Outcomes Partnership Common Data Model).

Are you currently studying?

I'm not studying at the moment. But my role requires frequent upskilling when things in the world are changing so frequently, for example, when it comes to real-world evidence methodologies and issues to do with artificial intelligence and machine learning analytics.

What's your background in health data science?

I've worked on longitudinal data. In Africa we have a health and demographic surveillance system, so I've worked more on these areas to get information. That's why I was keen to apply to the summer school, so that we can try and analyse this data through some of the tools that have been presented on the program.

How did you feel when you were offered the bursary onto the summer school?

I was incredibly excited about being selected, and I'd like to thank the selection committee for allowing me to come to this training. This is my first time in Europe, and in Oxford. Oxford is one of the world class universities so to me, personally and professionally, I was very excited to join the course here.

How have you enjoyed the week?

The week has been very transformative. From the morning sessions, where we've had presentations from experts, to the afternoon, where we were working on technical coding. One of the reasons I came here was that I'd like to see what happens downstream when it comes to data standardisation. In our organisation, after data standardisation and harmonisation, we were lacking the analytics bit, and this where this training has been very helpful. I was in the analytic track and using R packages that have been very exciting to use.

What's been the highlight?

The biggest highlight is on the issues to do with data standardisation and scalable workflows. Instead of reinventing the wheel, these packages will greatly help when it comes to analytics in our organisation. Because you're starting from characterisation of your data going through definition, phenotyping, up to survival analysis, I think it's very critical in the data that we're handling in our organisation.

How will the opportunity help with your future career?

I think one of the best things will to be to advocate for the OPOM CDM tool and try to advocate for other countries to implement it too. That would give the advantage of federated analytics, where the methods are consistent and could be shared between one country to another. The analytics tool is also the best way to accelerate discovery. I think it will be very beneficial to strengthen collaboration between southern part of Africa and the West.