Spotlight: Anthony Garratt

Database Engineer at BJSS

Background…

Database Engineer, Anthony Garratt, talks about his route into becoming a developer, how his role at BJSS involves a mix of different disciplines, and how he became involved in a project aiming to understand the risks of Covid-19 to the UK population.

I started my career as a programmer for a series of small software houses before eventually moving into contracting. Over time, I started working more and more with data, initially through writing SQL on Informix-4GL databases on Unix systems and then through various contracts that required Oracle and Database Administrator (DBA) work. I found the work interesting and fulfilling, so I started positioning myself as an Oracle database developer with DBA capabilities, which eventually led me to BJSS.

Working at BJSS

I first encountered BJSS as a contractor, working on a data migration project bringing the NHS’ Hospital Episode Statistics (HES) database inhouse to NHS Digital (then the Health and Social Care Information Centre).
I saw an exciting opportunity with these kinds of projects, and I eventually joined BJSS on a full-time basis in 2013 as a Database Engineer. In this role, I sit between Data Engineering and Database Administration, acting as a facilitator between the two disciplines – effectively making sure datasets are in the right place to allow for further engineering.

Working with NHS data to add 1.7m people to the shielding list

One of the most notable projects I’ve worked on came about in 2020, involving Oxford University’s QCovid™ risk calculator algorithm, which analyses patient data and assigns a score to the individual based on their predicted hospitalisation risk or death in the event of contracting Covid-19.

Following an original BJSS project to deliver a clinical tool allowing medical professionals to run individual patient data through the QCovid™ algorithm, I became part of a small team aiming to apply QCovid™ to the entire UK population. Working on NHS Digital’s Data Platform Service (DPS), we set about the process of finding the data points required by QCovid™ and feeding them into the algorithm. This involved working across billions of rows of data, eventually incorporating additional datasets such as ethnicity and care home data.

The project’s published data resulted in an extra 1.7 million individuals being added to the shielding list.

One of the things I enjoyed the most about this project was the challenge of building something that didn’t already exist.

Additionally, the responsibility of dealing with people’s health and delivering positive outcomes for the public gives you that warm, fuzzy feeling. Even though it’s been a challenging project in many ways, it feels so much more worthwhile when you’ve made a positive contribution to people’s lives.