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Finding the vulnerable before COVID does
When the University of Oxford developed a model to predict those at higher risk of dying from COVID-19, we had a challenge: to securely integrate this risk assessment tool into our data systems to identify exactly who these people were. Lee Gathercole, Technical Architect, explains how we did it.
29 March 2021
At the beginning of the pandemic, the Chief Medical Officer for England asked leading academics, clinicians and scientists to create a way of predicting who may be at high risk of serious illness if they catch coronavirus (COVID-19).
From this, the COVID-19 Risk Stratification programme was commissioned by the New and Emerging Respiratory Virus Threats Advisory Group to produce a data-driven mathematical model.
The University of Oxford led the work and used anonymised GP data to build a model, QCovid®, that identified combinations of factors such as sex, age, BMI, ethnicity and underlying medical conditions to determine an individual's risk of hospitalisation and death from COVID-19.
Historically, risk tools such as QCovid® were given to GP system suppliers to integrate into the GP system and used locally to identify those at high risk. However, the Chief Medical Officer for England wanted this risk stratification tool to be executed centrally. The research needed turning into reality and this is where NHS Digital came into the picture.
It didn't fail, it was just blisteringly fast.
We've used the University of Oxford’s QCovid® risk prediction model to identify additional people to be added to the Shielded Patient List (SPL).
Mark Reynolds, Interim Chief Technology Officer at NHS Digital, explains the architecture behind the NHS Shielded Patient List, which is helping to protect the most vulnerable members of society during the coronavirus pandemic.
NHS Digital has published the Shielded Patient List (SPL), which is enabling partner organisations across government to support and protect those who need shielding at this time.