Data Use Register

The Data Use Register summaries projects on the platform.

Active Projects

Heart Failure

ID: SDE_EE_PROJ_A0001

Data Access Agreement (DAA): Counter signed date: Thursday 18 April 2024 Time period: 14 months

Contract Organisation: Name: Cambridge University Hospitals NHS Foundation Trust Legal Name: Cambridge University Hospitals NHS Foundation Trust

Number of Patients: 24,000

Data sources: CUH, EEAST, MKUH, NNUH, NWAFT

Funding sources: NHS England

Project Start Date: Wednesday 02 October 2024 Planned Project End Date: Wednesday 30 September 2026

PPIE Details: Involvement of patient groups, Public consultation

Summary

This is project aims to improve health outcomes by reducing 30-day readmissions/deaths for patients with a heart failure primary diagnosis

Related links:

EoE-SDE

Project team

Name	Position	Organisation
Dr Paul Cacciottolo	Consultant Cardiologist - Primary Investigator	Cambridge University Hospitals NHS Foundation Trust
Prof Angela Wood	Professor of Health Data Science	University of Cambridge
Wen Shi	Research Associate	University of Cambridge
Dr Jessica Barrett	Principal Research Associate	MRC BSU, University of Cambridge
Mr Teun Petersen	Research Associate	MRC BSU, University of Cambridge

Dataset Criteria

Criteria include: data from the last 10 years; data fields describing demographic attributes, medications during admission/hospital stay, medical history of conditions, physical measurements like weight and height, procedures, death dates and more.

Real-World Data Curation of Cancer in the UK

ID: SDE_EE_PROJ_A0016

Data Access Agreement (DAA): Counter signed date: Thursday 06 October 2022 Time period: 60 months

Contract Organisation: Name: Cambridge University Hospitals NHS Foundation Trust Legal Name: Cambridge University Hospitals NHS Foundation Trust

Number of Patients: 11,000

Data sources: Cambridge University Hospitals

Funding sources: Arcturis Grant to CUH

Project Start Date: Friday 16 May 2025 Planned Project End Date: Thursday 10 June 2027

PPIE Details: The Arcturis Patient and Public Advisory Panel are involved in all aspects of the Arcturis Real-World Data Network’s research operations, including considering and providing feedback on datasets such as this and the research conducted using it.

Summary

The aim of this project is to construct a dataset of real-world oncology data that will be used to fill key evidence gaps regarding treatments and outcomes for patients living with a range of cancers in the UK. The dataset will be used to generate a range of real-world evidence-based insights in the following areas: Real-world use and sequencing of anti-cancer treatments; Identification of populations in which there is an unmet therapeutic need; Comprehensive characterisation of the use of support therapy in the treatment of cancer, and assessment of real-world clinical outcomes; Identification of key groups of patients who are under-represented in phase 3 clinical trials. This oncology dataset is part of the Arcturis Real-World Data Network, a research database for conducting observational research. Research projects which have used or are using data comprising the Arcturis Real-World Data Network are listed on their Public Register of Research.

Related links:

Arcturis | Advancing Insights Using Real-World Data

Project team

Name	Position	Organisation
Benjamin Granville	Partnerships Relations Manager	Arcturis Data Limited
Dr Lewis Carpenter	Chief Scientific Officer	Arcturis Data Limited
Dr Jamie Wallis	Health Care Data Specialist	Arcturis Data Limited
Dr Federica Picariello	Senior Epidemiologist	Arcturis Data Limited
Filipa Tunaru	Epidemiologist	Arcturis Data Limited
Danielle Robinson	Senior Epidemiologist	Arcturis Data Limited
Bernard Cooke	Senior Data Engineer	Arcturis Data Limited
Olivia Wiper	Clinical Data Scientist	Arcturis Data Limited
Anthony Poncet	Senior Software Engineer	Arcturis Data Limited
Alycia Perkins	Epidemiologist	Arcturis Data Limited
Dr Joanne Winter	Senior Epidemiologist	Arcturis Data Limited
Matthew Martin	Senior Data Engineer	Arcturis Data Limited
Dr Joseph Cronin	Senior Machine Learning Researcher	Arcturis Data Limited
Joseph O'Reilly	Associate Head of RWE	Arcturis Data Limited
Keiran Tait	Biomedical Data and Image Analyst - ML Analytics	Arcturis Data Limited
Dr Robert Dürichen	Head of ML Analytics	Arcturis Data Limited
Dr Shammi Luhar	Senior Epidemiologist	Arcturis Data Limited
Steven Soutar	Medical Statistician	Arcturis Data Limited
Robert Fletcher	Epidemiologist	Arcturis Data Limited

Dataset Criteria

The anonymised, baseline and longitudinal demographic and clinical characteristics of patients living with cancer.

External validation of a machine learning algorithm for predicting hospital-acquired pressure ulcers using de-identified patient data from the East of England

ID: SDE_EE_PROJ_A0025

Data Access Agreement (DAA): Counter signed date: Tuesday 20 January 2026 Time period: 12 months

Contract Organisation: Name: Imperial College London Legal Name: Imperial College London

Number of Patients: 263,230

Data sources: CUH

Funding sources: Internal Imperial College Funding

Project Start Date: Sunday 01 February 2026 Planned Project End Date: Monday 01 February 2027

PPIE Details: Designing the Research Project: Members with lived experience of pressure injuries (as either individual or carers), have been invited to partner with the research team and join a PPIE advisory panel. This will ensure the proposed research is aligned with the needs, preferences and experiences of individuals affected by pressure injuries. The PPIE advisory panel is formed of 6 members. This includes 2 each from an established PPIE advisory panel based at the clinical trials unit at the University of Leeds “Pressure Ulcer Research Service User Network (PURSUN)” and a similar advisory panel which is currently being established at the Royal College of Surgeons in Ireland. Finally, two individuals with lived experience in pressure ulcer from North West London make up the final members of the panel. This is to promote diverse representation in perspectives, needs and experiences of PPIE members. This is particularly important considering the existing ethnic, age, and skin tone disparities in individuals affected by pressure injuries. Members of the PPIE advisory panel will have an active involvement in decision making during this proposed project, and together, we will identify the most relevant outcome measures when developing a clinical prediction tool from large data, alongside ethical use of data. They will also be involved in helping design the parameters for assessment of data accuracy and determining what is an acceptable standard of accuracy. Participants will be reimbursed for their time in accordance with NIHR guidance on funding for contribution to PPIE. We have identified specific funding streams to support this work, should the research be approved.
Managing the Research Project: The PPIE advisory panel plans to hold our first meeting this summer and will provide ongoing input and ensure the research remains patient centred. This group will meet bi-monthly during the research to ensure this.
Analysing the Data: The PPIE advisory panel will be involved in decisions about the study outcomes, particularly to critically appraise the accuracy of clinical prediction determined utilising this approach to ensure this is meaningful and relevant to patients.
Disseminating the Study Results: We will seek advice from the PPIE advisory panel on the best channels to sharing the research findings with diverse audiences. This approach ensures that the study results are communicated not only through academic outlets, such as publications and conferences, but also reach local communities and public through accessible, plain language summaries, various media platforms, and broad dissemination efforts.

Summary

What are pressure ulcers and why do they matter?
Pressure ulcers (also called bedsores) are painful wounds that develop when prolonged pressure on the skin cuts off blood flow. They affect around 700,000 people in the UK each year, including 1 in 20 hospital patients. These injuries cause suffering for patients, extend hospital stays, and cost the NHS £3.8 million every day. The good news is that up to 95% of pressure ulcers can be prevented with simple measures like regular repositioning, special mattresses, and good nutrition. However, we still have the challenge of identifying at-risk patients early enough.
The challenge with current methods
Currently, nurses use checklists to assess pressure ulcer risk. These tools are helpful but have limitations. They might overlook important medical conditions that affect a patient's mobility or sensation. They can also miss changes in a patient's condition that occur during their hospital stay. Because of these limitations, some high-risk patients don't receive preventive care soon enough, whilst other low-risk patients receive unnecessary interventions.
What this research project will do
Our team at Imperial College London has developed a tool that uses machine learning, a type of artificial intelligence, to predict pressure ulcer risk more accurately. This tool analyses information that hospitals already collect routinely, such as age, blood test results, blood pressure readings, and pre-existing medical conditions. This identifies patterns in this information that are difficult for humans to spot. When we tested this tool using data from hospitals in North-West London. However, before this tool can be used to help patients, we need to check that it works equally well in other hospitals. This study will test whether our tool performs accurately when applied to patient data from hospitals in the East of England. We will compare patients who developed pressure ulcers with those who did not, to verify that the tool correctly identifies high-risk individuals.
What information we need
We will use anonymised hospital records from adult patients (18 years and older) who stayed in hospital for at least 24 hours between 2017 and 2024. The information we need includes basic demographics (age and gender), vital signs (pulse, blood pressure, breathing rate), blood test results (such as haemoglobin and kidney function), medications prescribed, and pre-existing medical conditions (like diabetes or spinal cord injury). All of this information has already been collected as part of routine hospital care. There is no need to access personal identifiers (names, addresses, NHS numbers).
How this research will benefit patients and the NHS
If our tool works well in East of England hospitals, it will continue to drive the development of this into a tool which could be used in hospitals to help healthcare teams identify high-risk patients within hours of admission, allowing preventive care to begin immediately. This would hopefully reduce the number of patients who develop painful pressure ulcers, shorten hospital stays, and save NHS resources. The tool would work automatically in the background, using information that hospitals already collect, without requiring extra work from busy nursing staff.

Related links:

EoE-SDE

Project team

Name	Position	Organisation
Dr Mikael Sodergren	Clinical Associate Professor, Department of Surgery and Cancer - Primary Investigator	Imperial College London
Dr Simon Erridge	Postgraduate Researcher, Department of Surgery and Cancer	Imperial College London
Dr Pedro Mediano	Lecturer	Imperial College London, Department of Surgery and Cancer
Dr Stuart Bowyer	Assistant Professor	Imperial College London, Department of Surgery and Cancer

Dataset Criteria

Inclusion Criteria:
Adult patients (>=18 years)
Inpatient hospital admissions >= 24 hours durations
Admitted to participating East of England SDE sites.
Exclusion criteria:
Pediatric admissions (age < 18 years)
short-stay admissions (<24 hours duration)
Admissions with pressure ulcers documented as present on admission (community-acquired pressure ulcers).
Variables required include administrative data about a patients stay, demographic information, diagnostic codes including time and date of diagnosis, lab findings, vital signs and medications.

Testing EoE-SDE platform capabilities

ID: SDE_EE_PROJ_A0030

Data Access Agreement (DAA): Counter signed date: Friday 04 April 2025 Time period: 6 months

Contract Organisation: Name: Cambridge University Hospitals NHS Foundation Trust Legal Name: Cambridge University Hospitals NHS Foundation Trust

Number of Patients: 100

Data sources: SDE

Funding sources: NHS England

Project Start Date: Wednesday 30 July 2025 Planned Project End Date: Saturday 04 October 2025

PPIE Details: N/A

Summary

This project is providing a research environment with synthetic data to enable confirmation that the East of England SDE has suitable functionality to meet the researchers needs

Project team

Name	Position	Organisation
Liam Miller	Head Data Architect - Primary Investigator	Kent, Medway and Sussex Secure Data Environment
Emlin Jones	Interoperability Architect	Kent, Medway and Sussex Secure Data Environment

Dataset Criteria

Synthetic data for testing purposes.

Real-World Data Curation of Autoimmune diseases in the UK

ID: SDE_EE_PROJ_A0033

Data Access Agreement (DAA): Counter signed date: Wednesday 12 March 2025 Time period: 60 months

Contract Organisation: Name: Cambridge University Hospitals NHS Foundation Trust Legal Name: Cambridge University Hospitals NHS Foundation Trust

Number of Patients: 1,300

Data sources: Cambridge University Hospitals

Funding sources: Arcturis Grant to CUH

Project Start Date: Friday 16 May 2025 Planned Project End Date: Saturday 04 August 2029

PPIE Details: The Arcturis Patient and Public Advisory Panel are involved in all aspects of the Arcturis Real-World Data Network’s research operations, including considering and providing feedback on datasets such as this and the research conducted using it.

Summary

This project aims to construct a comprehensive research dataset on autoimmune diseases, using data from routine clinical care in secondary healthcare settings. The dataset will include detailed disease-specific severity metrics, patient outcomes, and healthcare resource utilisation data. The dataset will be used to undertake observational research projects including, but is not limited to, feasibility studies, natural history studies, comparison between trial and real-world data, comparative effectiveness studies, Post-Authorisation Safety Studies, causal inference methodological development, prognostic modelling and patient stratification. This autoimmune dataset is part of the Arcturis Real-World Data Network, a research database for conducting observational research. Research projects which have used or are using data comprising the Arcturis Real-World Data Network are listed on their Public Register of Research.

Related links:

Arcturis | Advancing Insights Using Real-World Data

Project team

Name	Position	Organisation
Alycia Perkins	Epidemiologist	Arcturis Data Limited
Dr Lewis Carpenter	Chief Scientific Officer	Arcturis Data Limited
Dr Jamie Wallis	Associate Head of RWE	Arcturis Data Limited
Dr Federica Picariello	Senior Epidemiologist	Arcturis Data Limited
Filipa Tunaru	Epidemiologist	Arcturis Data Limited
Danielle Robinson	Senior Epidemiologist	Arcturis Data Limited
Bernard Cooke	Senior Data Engineer	Arcturis Data Limited
Olivia Wiper	Clinical Data Scientist	Arcturis Data Limited
Anthony Poncet	Senior Software Engineer	Arcturis Data Limited
Dr Joanne Winter	Senior Epidemiologist	Arcturis Data Limited
Matthew Martin	Senior Data Engineer	Arcturis Data Limited
Dr Joseph Cronin	Senior Machine Learning Researcher	Arcturis Data Limited
Joseph O'Reilly	Associate Head of RWE	Arcturis Data Limited
Keiran Tait	Biomedical Data and Image Analyst - ML Analytics	Arcturis Data Limited
Dr Robert Dürichen	Head of ML Analytics	Arcturis Data Limited
Dr Shammi Luhar	Senior Epidemiologist	Arcturis Data Limited
Steven Soutar	Medical Statistician	Arcturis Data Limited
Robert Fletcher	Epidemiologist	Arcturis Data Limited

Dataset Criteria

The anonymised, baseline and longitudinal demographic and clinical characteristics of patients living with autoimmune diseases.

Past Projects

Beta testing for the East of England SDE

ID: SDE_EE_PROJ_A0028

Data Access Agreement (DAA): Counter signed date: Tuesday 01 October 2024 Time period: 11 months

Contract Organisation: Name: Cambridge University Hospitals NHS Foundation Trust Legal Name: Cambridge University Hospitals NHS Foundation Trust

Number of Patients: 100

Data sources: SDE

Funding sources: NHS England

Project Start Date: Tuesday 01 October 2024 Project End Date: Friday 25 July 2025

PPIE Details: N/A

Summary

This project is providing a research environment with synthetic data to allow users to test and give feedback on the SDE

Project team

Name	Position	Organisation
Wai Keong Wong	Clinical Director of Digital Transformation - Primary Investigator	Cambridge University Hospital NHS Foundation Trust
Ari Ercole	Consultant in neurosciences intensive care medicine	Cambridge University Hospital NHS Foundation Trust
Sarah Burge	Director of Clinical Integration	University of Cambridge
Eckart De Bie	PhD student	University of Cambridge
Wen Shi	Research Associate	University of Cambridge
Fatemeh Torabi	Assistant Professor in Health Data Science	University of Cambridge
Rebecca Brock	Research Associate	MRC Biostatistics Unit

Dataset Criteria

Synthetic OMOP data themed on Heart Failure diagnosis

Capability testing for the East of England SDE

ID: SDE_EE_PROJ_A0029

Data Access Agreement (DAA): Counter signed date: Tuesday 01 October 2024 Time period: 9 months

Contract Organisation: Name: Cambridge University Hospitals NHS Foundation Trust Legal Name: Cambridge University Hospitals NHS Foundation Trust

Number of Patients: 100

Data sources: SDE

Funding sources: NHS England

Project Start Date: Tuesday 01 October 2024 Project End Date: Friday 25 July 2025

PPIE Details: The Arcturis Patient and Public Advisory Panel are involved in all aspects of the Arcturis Real-World Data Network’s research operations, including considering and providing feedback on datasets such as this and the research conducted using it

Summary

This project is providing a research environment with synthetic data to enable confirmation that the East of England SDE has suitable functionality to meet the researchers needs

Project team

Name	Position	Organisation
Bernard Cooke	Senior Data Engineer - Primary Investigator	Arcturis Data Limited
Danielle Robinson	Senior Epidemiologist	Arcturis Data Limited
Federica Picariello	Senior Epidemiologist	Arcturis Data Limited

Dataset Criteria

Synthetic data

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