As the threat of COVID-19 intensified in March 2020, Chris Whitty, the Chief Medical Officer for England, announced a set of clinical criteria for identifying people likely to be at high risk.
It included patients who had received organ transplants, those with severe respiratory conditions such as cystic fibrosis, people being treated for cancer and other vulnerable groups. The urgent cross-system plan was to identify all those at risk, tell them about their situation, and ask them to ‘shield’.
In England, a significant part of that technical challenge, which stretched across several government departments and agencies, fell to NHS Digital. Teams worked day and night to bring hospital and medicines data together and give a view of the vulnerable people who met the predefined conditions. Within about a week, the first version of the 'Shielded Patient List' was in use and saving lives.
In many ways, it was a historic achievement. National data was not just being used in the aggregate to identify trends and guide planning and drive research, it was picking out individuals who needed help and getting guidance and resources to them.
But the Shielded Patient List was also limited: essentially, it was a list of people with the predetermined conditions. It did not seek to identify combinations of factors that might increase or decrease risks. The next question was whether, by looking at a wider range of demographic and medical factors such as age, gender, ethnicity and other underlying health conditions, it would be possible to predict the actual risk coronavirus might pose to any given individual?