Data-driven systems and health inequalities: COVID-19 and beyond Our partnership with the Ada Lovelace Institute will explore how the accelerated adoption of data-driven technologies and systems during the pandemic may have affected inequalities.

We are also collaborating with Public Health Scotland to understand inequalities in one case study – the SPARRA (Scottish Patients at Risk of Readmission and Admission) risk prediction tool.

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, were being mobilised at pace and their use during the pandemic may well become the norm for the future.

And as service providers now deal with the backlog of elective care, data-driven technologies can be a central part of the solution. 

There is a risk that they inadvertently exacerbate health inequalities, but also there is great potential for them to shed light on and address inequalities when designed well.

As a case study to better understand the mechanisms perpetuating or mitigating inequalities in a data-driven technology, Public Health Scotland, in collaboration with the Turing Institute, will assess the fairness of the SPARRA algorithm, develop and share open source code to aid others with fairness analysis, and explore the concepts of inequalities and fairness with patients and the a public engagement group.

The partnership between the Ada Lovelace Institute and the Health Foundation will explore how the accelerated adoption of data-driven technologies and systems during the pandemic may have affected inequalities. Together we will generate an evidence base around the interaction between these technologies and health outcomes, building a shared understanding of the actions needed to reduce health inequalities and improve people’s health.