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A collaboration which used anonymised GP records to identify 1.5m people most at risk from Covid-19 and prioritise them for vaccination has won the prestigious John Perry Prize for computing innovation in primary care.

The BCS logo, and a portrait of Prof Julia Hippisley-Cox

Using QResearch, a database of more than over 35 million anonymised health records derived from GP practices using the EMIS clinical computer system, researchers at the University of Oxford and the New and Emerging Respiratory Virus Threats Advisory Group (NERVTAG) developed a population-wide risk assessment model called QCovid.

QCovid was used by NHS Digital to predict on a population basis whether adults with a combination of risk factors may be at more serious risk from Covid-19 and should be prioritised for vaccination.

As a result, in February 1.5m high risk individuals were identified, added to the Shielded Patient List as a precautionary measure and prioritised for earlier vaccination. The research also played a vital role in raising public awareness of key Covid-19 risk factors. 

The research was commissioned by England’s chief medical officer, Professor Chris Whitty and funded by the National Institute of Health Research. It found that there are several health and personal factors which, when combined, could mean someone is at a higher risk from COVID-19. These include characteristics like age, ethnicity and body mass index, as well as certain medical conditions and treatments.

The John Perry Prize, awarded by BCS, the Chartered Institute for IT, rewards those who have made an outstanding contribution to innovation and excellence within primary care computing and informatics. 

Professor Julia Hippisley-Cox, professor of clinical epidemiology and general practice at the University of Oxford, said: “Identifying patients at highest risk for interventions quickly in a pandemic at national scale is not something any of us were expecting to have to do. So, we are very grateful to the many hundreds of GP practices who contribute anonymised data to QResearch and to fantastic support from Oxford, EMIS, the Department of Health and Social Care, NHS Digital, National Institute for Health Research, our patient advisers and many academic collaborators, which made this possible.”

This is the second time Julia Hippisley-Cox has led a project winning the award, the first was in 2013 for the Open Pseudonymiser tool.

Dr Shaun O’Hanlon, chief medical officer at EMIS, said: “EMIS is incredibly proud to have supported this important piece of research, which continues to enable the NHS to protect more vulnerable people, more quickly, from Covid-19.

“This is the latest in a long list of research projects that has been powered by real-life data collected from thousands of GP consultations every day. We are thankful to the GP practices that have supported QResearch over the last 15 years - without them this important initiative would not be available to help improve the health of the nation.”

Professor Jonathan Benger, chief medical officer of NHS Digital, said: “Data has been critical in powering our nation’s response to the pandemic in so many different ways, including support to world-leading research and prioritising the care of those most vulnerable in society.

“I am immensely proud of the team, both at NHS Digital and across our many partner organisations, for coming together to develop and implement this innovative solution which has identified and protected those most at risk from Covid-19.”

Dr Philip Scott, chair of the BCS Health and Care specialist group said: “This project showed how data science and health informatics can help us to identify vulnerable people at scale to prioritise their treatment. It is a great example of how informatics can be used to improve population health.

“During the pandemic we have seen a massive acceleration in digital health, and this project is just one example that shows how, during such a crisis, tech can be used to tackle a problem effectively, ethically and quickly. BCS is proud to give this award to such a worthwhile project that sets a pattern for real world research based on routine clinical data.” 


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