In the July 2023 edition of AI^2, we had the privilege of hosting two esteemed guests from NHS England: Dr. Dan Schofield and Dr. Jonathan Pearson. They provided us with a fascinating overview of NHS England and its Digital Analytics Research Team (DART).

Despite being a small team, DART is making significant strides in transforming and modernising analytics in health and care, with the ultimate goal of improving outcomes for everyone. Their focus lies in bridging the gap between academia and the NHS, actively pushing innovative ideas into clinical practice. DART’s primary areas of innovation include representation of complex data, advances in data science and RAP, synthetic data, privacy enhancing technologies, natural language processing, and simulations.

During the session, our speakers highlighted the PhD internship scheme at DART. This program offers PhD students the opportunity to work on individual projects within the DART team for up to 5 months. It serves as a unique platform to connect academia with NHS England, fostering collaborative research efforts and providing students with valuable hands-on experiences. If you’re interested in this exciting opportunity, keep an eye out for internship applications opening in October. You can find more details about the PhD internships at NHS England here.

Dan then shared insights into the ways of working within the DART team, offering valuable tips to enhance productivity and achieve better outcomes. He emphasized the importance of adopting agile practices, such as sprints, regular stand-ups, and reviews, to receive continuous feedback and facilitate seamless collaboration. Additionally, breaking down projects into work packages and utilising tools like Trello for project tracking can help ensure progress towards set goals. Dan also highlighted the significance of reproducibility and open availability of code in healthcare, as it promotes transparency and facilitates continuous improvement.

The second hour of the forum was both educational and entertaining as teams engaged in explaining AI concepts to different age groups. From explaining gradient boosting to a 5-year-old to elaborating on U-nets for PhD students, participants had only 3 minutes to convey complex ideas in a simple and engaging manner. The committee members thoroughly enjoyed participating in this fun exercise, pretending to be young and listening to imaginative explanations. For example, U-nets were humorously depicted as super tools to find Wally in Where’s Wally in just seconds, and Reinforcement Learning was playfully linked to a reason why a baby brother misses out on chocolate if he misbehaves. Well done to the Reinforcement Learning team for winning over the judges with your variety of explanations and team working skills. Let’s hope the AI^2 hat makes its debut again soon, bringing with it more exciting activities!

Blog written by: Zoe Hancox