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.
Improving decisions on what to focus on in research using large datasets
Theme Translational data science
Workstream Large, complex datasets
Exploring inflammation as a driver for post-operative complications
Theme Translational data science
Workstream Omics for prediction and prognosis
Investigating new approaches to drug development using human genetics
Theme Translational data science
Workstream Genetic evidence to prioritise intervention
Exploring the link between obesity and heart failure using genetics
Theme Translational data science
Workstream Genetic evidence to prioritise intervention
Lung development in early life and respiratory diagnosis and treatment
Themes Respiratory disease Translational data science
Workstream Exacerbation prediction and aerosol emissions
Can we use DNA methylation to predict disease in diverse populations?
Theme Translational data science
Workstream Omics for prediction and prognosis
South West Secure Data Environment
Theme Translational data science
Workstream Clinical informatics platforms
Preventing cardiovascular events in stroke patients
Theme Translational data science
Workstream Genetic evidence to prioritise intervention
Exploring the link between genes and cognitive decline
Theme Translational data science
Workstream Genetic evidence to prioritise intervention
Do ethnicity and coexisting health conditions impact high-risk diabetes?
Theme Translational data science
Workstreams Clinical informatics platforms Large, complex datasets
Handling missing data in large electronic healthcare record datasets
Theme Translational data science
Workstream Large, complex datasets
Biomarkers for screening and diagnosing lung cancer
Theme Translational data science
Workstream Omics for prediction and prognosis
Treatment resistance and drug side effects in schizophrenia
Theme Translational data science
Workstream Genetic evidence to prioritise intervention
Exploring how obesity influences cancer survival
Theme Translational data science
Workstream Genetic evidence to prioritise intervention
Using biomarkers and machine learning to predict antidepressant resistance
Theme Translational data science
Workstream Omics for prediction and prognosis
Can DNA methylation biomarkers predict whether pleural effusion is caused by cancer?
Theme Translational data science
Workstream Omics for prediction and prognosis
Using DNA methylation biomarkers to understand Parkinson’s disease severity and progression
Theme Translational data science
Workstream Omics for prediction and prognosis
Data driven approaches to drug target prioritisation
Theme Translational data science
Workstream Genetic evidence to prioritise intervention