Tackling health and social inequalities in data-driven systems
Research to examine the interaction between data-driven systems and health and social inequalities, in the wake of COVID-19.
Data-driven systems and inequalities in health and social care
A knotted pipeline
The COVID-19 pandemic, and the wider governmental and societal response, have brought health inequalities into sharp focus.
Data-driven technologies and the systems within which they operate have increasingly become a central part of the health infrastructure – a trend accelerated by the pandemic. Tools, such as symptom tracking and digital contact tracing apps, are being mobilised at pace and their use during the pandemic may well become the norm for the future.
However, little is known about the long-term impact of data-driven approaches. There is a risk that they are exacerbating health inequalities, but also there is great potential for them to shed light on, and address inequalities when designed well.
In partnership with the Health Foundation, we have explored how the accelerated adoption of data-driven technologies and systems during the pandemic may have affected inequalities. Our report, A knotted pipeline, describes the interaction between these technologies and health outcomes and outlines some of the ways that health inequalities can arise from data-driven technologies in the UK’s health ecosystem.
Evidence was gathered through a desk-based scoping exercise of publicly available documents, with additional information shared by technology organisations. It was supplemented by stakeholder interviews.
Together with the Health Foundation, we are continuing to gather evidence of the ways data-driven systems impact inequalities. Our goal is to produce recommendations for how these systems can be designed and deployed in ways that mitigate negative impacts and promote their benefits.
The advisory group
Throughout the research, an advisory group of specialists in philosophy, global and public health, patient and public advocacy, as well as ethics and industry have provided guidance.
The data divide
Public attitudes to tackling social and health inequalities in the COVID-19 pandemic and beyond
Why the COVID-19 shielded patient list might both compound and address inequalities
Wicked problems in the use of data-driven systems
Don’t start with the data: a people-centred approach to addressing health inequalities
Why identities and experiences can’t be reduced to categories
Minding the genomic data gap: COVID-19, genomics and health inequalities
The role of genomics in the data-driven pandemic response