Clinical informatics platforms

Secure and trusted use of data at scale to improve health and care

Theme Translational data science

Integrated Care Systems (ICS) have a wealth of routinely collected data about the people in their region. This is known as administrative or routine data.

We are working to integrate these electronic health and social care records with other data in a Trusted Research Environment, sometimes called Secure Data Environments (TREs and SDEs). These environments are the preferred model for securely analysing administrative data at scale whilst protecting confidentiality.

With NHS colleagues we have created a clinical informatics platform. This integrates GP practice, hospital and social care records of around 1.1 million citizens in our regional care system. We have shared data extracts with the NIHR Health Informatics Collaborative. We also lead analysis in population-level TREs created during the pandemic.

We are implementing a new regional TRE platform providing secure access to linked routine and research data. Building on our partnership with Imperial BRC we are conducting research using linked hospital data from several trusts within a TRE.

Working with our partners at the Bradford Institute for Health Research, we are integrating regional administrative and research data as a platform for observational and experimental research. And finally, we are working with the UK Longitudinal Linkage Collaboration to develop and test a new scalable model for data integration across ICSs.

View all research projects

Great Western Secure Data Environment

NHS England are developing and deploying a national secure data environment for research. A secure…

Theme Translational data science

Workstream Clinical informatics platforms

Preventing cardiovascular events in stroke patients

Having a stroke means you are more likely to experience a subsequent cardiovascular event. Cardiovascular…

Theme Translational data science

Workstream Genetic evidence to prioritise intervention

Exploring how obesity influences cancer survival

Evidence from different studies suggests that obesity or body mass index (BMI) might play a…

Theme Translational data science

Workstream Genetic evidence to prioritise intervention

Using biomarkers and machine learning to predict antidepressant resistance

Around half of patients with depression don’t improve after taking antidepressants. Clinicians need to…

Theme Translational data science

Workstream Omics for prediction and prognosis

Can DNA methylation biomarkers predict whether pleural effusion is caused by cancer?

Pleural effusion, where fluid builds up in the cavity around the lungs, can develop…

Theme Translational data science

Workstream Omics for prediction and prognosis

Using DNA methylation biomarkers to understand Parkinson’s disease severity and progression

The Biogen Tel Aviv Parkinson Project (BeatPD) looks in-depth at clinical and genetic information…

Theme Translational data science

Workstream Omics for prediction and prognosis

Biomarkers for screening and diagnosing lung cancer

In the UK, only 15 per cent of people diagnosed with lung cancer will still…

Theme Translational data science

Workstream Omics for prediction and prognosis

Creating the infrastructure to enable translational data analysis at scale

Our priority is to create the data infrastructure to enable analysis of linked administrative and…

Theme Translational data science

Workstreams Clinical informatics platforms Large, complex datasets

Data driven approaches to drug target prioritisation

Despite more money going towards developing drugs, the success rate of getting new drugs to…

Theme Translational data science

Workstream Genetic evidence to prioritise intervention