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Driving Responsible Data Science: A Case Study of NHS England's Data Science Team

Authors: Noonan, Mia; Sands, Harriet; Wallace, Ben; Gillespie, Stuart; Sharan, Malvika; Bennett, Arielle; Demertzi, Léllé;

Driving Responsible Data Science: A Case Study of NHS England's Data Science Team

Abstract

The NHS is built on values of fairness, trust, compassion and safety. As artificial intelligence (AI) becomes a more prominent tool in healthcare – from automation of administrative processes to disease prediction and medical imaging analysis – NHS England’s data science team is working to ensure these values remain central to technological advancement. This is much more than just a box-ticking exercise: it’s about shaping best practices, embedding those practices in projects right from the start, and being as transparent and open as possible throughout the development lifecycle. That way, patients can have confidence that their (often sensitive) personal information is being used safely, fairly and responsibly, and that the innovative use of data-driven technologies including AI can add genuine value to services and activities, improving outcomes for the tens of millions of people who rely on NHS England to support their healthcare needs. Members of the NHS England data science team have been taking part in the 2024 cohort of The Turing Way Practitioners Hub’s Experts in Residence programme, giving them a platform to discuss with other professionals the opportunities, challenges and best practices of AI development and adoption within a large public-sector organisation. This case study will explore some of the insights and ideas arising from those discussions, alongside a selection of NHS England’s ongoing projects. Particularly, we put a spotlight on ethical considerations in data science, from design to monitoring and evaluation, ensuring responsible decision-making at all stages.

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
0
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