1,700 GP practices contribute patient data for COVID-19 research

Oxford researchers praise widespread response from primary care operators to call for valuable patient data on which to base coronavirus response

Patient data is vital in helping researchers and scientists to understand the spread of COVID-19 and to inform local strategies

Researchers have revealed a ‘fantastic’ response from more than 1,700 UK GP practices who have stepped forward to contribute patient data for vital COVID-19 research at the University of Oxford – tripling the amount of data available.

The data, which has already led to important published research on COVID-19 risk factors, has come from a daily pseudonymised feed from participating practices provided by EMIS since July.

EMIS has also given researchers first access to its new powerful clinical data and analytics product.

Built on the cloud-based EMIS-X platform, the new EMIS-X Analytics technology is now being piloted in a number of academic studies relating to COVID-19, ahead of its launch to NHS customers.

The sheer numbers coming forward have surprised and delighted me, but primary care has really stepped up to the plate on all fronts in this crisis

The response from GPs follows a joint appeal in March by the Oxford Royal College of General Practitioners Research and Surveillance Centre (RSC) at Oxford University, and clinical systems supplier, EMIS, for practices to share their data under strict governance guidelines.

To date, 1,774 practices covering nearly 10 million patients have volunteered to take part in the research, significantly boosting the network of practices from 500 last year.

Professor Simon de Lusignan, director of the RSC, said: “We had a resource before, but it is on a fantastic scale now and is already helping us to answer important questions about COVID-19.

“The sheer numbers coming forward have surprised and delighted me, but primary care has really stepped up to the plate on all fronts in this crisis.

“It would be difficult to do this research without carefully-coded primary care data.

“For example, ethnicity is well recorded by many GP practices, and this was vital to help us understand the impact of COVID-19 on BAME communities.”

The data is enabling the researchers to understand the spread of COVID-19, including tracking when it peaks and helping to inform strategy on local social distancing and lockdown.

It is also being used to evaluate rapid COVID-19 fingerprick tests against swab tests.

And some practices are also taking part in rapid clinical trials of antibiotics to reduce the duration and severity of the virus.

Our technology provides the secure, powerful processing tools needed by researchers, healthcare providers, and the Government to answer important questions quickly by running complex queries over aggregated datasets at speed

Dr Shaun O’Hanlon, chief medical officer at EMIS, said: “We are proud that our advanced analytics technology is being deployed to support vital research at The University of Oxford.

“The COVID-19 outbreak has highlighted the need to obtain good-quality data quickly, and the power of data to make a difference.

“Our technology provides the secure, powerful processing tools needed by researchers, healthcare providers, and the Government to answer important questions quickly by running complex queries over aggregated datasets at speed.”

With the launch of the new EMIS-X Analytics product suite, healthcare organisations and researchers will benefit from:

  • Cloud analytics tools designed to deliver scalable insights from large healthcare datasets
  • Secure authorised access to data from up to 40 million patient records held by more than 4,000 healthcare organisations, enabling intelligence-driven health transformation
  • The ability for analytics partners to deploy their own assured algorithms and applications on the EMIS-X platform and share results with clinicians via EMIS Web
  • The ability to integrate artificial intelligence tools to allow users to research, build and deploy state of the art machine learning applications
  • Combining datasets from multiple sources to build rich and tailored data resources

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