Current research in Thames Valley and Surrey
The following research projects are in progress in Thames Valley and Surrey.
This data use register is currently in development and does not represent a full list of current or completed projects.
Projects
ERAS
Enhanced Recovery After Surgery (ERAS) Insights Solution
The objective of this project is to extract insights from Electronic Patient Record (EPR) data to help OUH provide proactive and timely patient care and identify gaps in their adherence to ERAS pathways.
Currently, members of the multidisciplinary team create multiple notes (both structured and unstructured) detailing the care delivered, which are saved in the EPR. This data holds valuable insights that can enable OUH to deliver ERAS care more proactively.
Clinical staff often respond reactively to situations and have limited ability to predict various scenarios. The proposed solution, utilising Google Health Care API and GCP Services, will harness information from these notes and display it as visual data, enhancing the clinical team’s practice and responsiveness.
Organisation
Acuvate
Unique project identifier
SDE_TVS_OUH_Proj_1
Date of signed DAA
13/10/2023
Period of DAA
Two years
Website
Healthcare Solutions | Modernize Healthcare with Data & AI from Acuvate
MORSE
MultiOmics pRoject to Study gEnomics (MORSE); Integrating human functional genomics, epigenomics and transcriptomics to accelerate drug discovery for precision medicine in asthma
The purpose is to analyse anonymised patient data to support the investigation of the impact of comorbidities, treatments and interventions for a cohort of patients with severe eosinophilic asthma and other characteristics of systemic immunological dysfunction over a period of time.
Organisation
Arcturis Data (UK) Ltd
Unique project identifier
SDE_TVS_OUH_Proj_2
Date of signed DAA
23/01/2024
Period of DAA
Two years
Website
Arcturis | Advancing Insights Using Real-World Data
Haematological Malignancies
Haematological Malignancies
Developing synthetic control arms and identifying features associated with progression and prognosis within and across subtypes of haematological malignancies.
Organisation
Arcturis Data (UK) Ltd
Unique project identifier
SDE_TVS_OUH_Proj_3
Date of signed DAA
23/01/2024
Period of DAA
Two years
Website
Arcturis | Advancing Insights Using Real-World Data
NASH
A real-world evidence study of the patient journey for patients with Non-alcoholic Fatty Liver Disease (NAFLD)/Non-alcoholic Steatohepatitis (NASH) in the UK secondary care setting
The purpose is to analyse anonymised patient data to support the investigation of the complete patient journey from metabolic syndrome diseases to NAFLD/NASH, as well as the demographic and disease characteristics, treatment pathways and outcomes for a cohort of patients with NAFLD/NASH over a period of time.
Organisation
Arcturis Data (UK) Ltd
Unique project identifier
SDE_TVS_OUH_Proj_4
Date of signed DAA
23/01/2024
Period of DAA
Two years
Website
Arcturis | Advancing Insights Using Real-World Data
Oncology Research Database
Oncology Research Database
Curation of a commercially viable, deep, longitudinal anonymised oncology research dataset that will allow Arcturis to efficiently deliver unique insights to pharma in relation to the development of treatments that will deliver significant benefit to patients with cancer.
Organisation
Arcturis Data (UK) Ltd
Unique project identifier
SDE_TVS_OUH_Proj_5
Date of signed DAA
23/01/2024
Period of DAA
Two years
Website
Arcturis | Advancing Insights Using Real-World Data
RSV
Respiratory Syncytial Virus (RSV)
The purpose is to analyse anonymised patient data to support the investigation of the current state of testing coverage, healthcare resource utilisation (HCRU) and economic cost of hospitalisations for Respiratory Syncytial Virus (RSV).
Organisation
Arcturis Data (UK) Ltd
Unique project identifier
SDE_TVS_OUH_Proj_6
Date of signed DAA
23/01/2024
Period of DAA
Two years
Website
Arcturis | Advancing Insights Using Real-World Data
Liver Disease and Viral Hepatitis
NIHR Health Informatics Collaborative (HIC) Liver Disease and Viral Hepatitis - GlaxoSmithKline (GSK) phase 3a
Feasibility assessment
Assessment of availability and completeness of electronic health data on adult patients with chronic HBV infection within the UK National Institute for Health Research (NIHR) Health Informatics Collaborative (HIC) Network.
Organisation
GSK Plc
Unique project identifier
SDE_TVS_OUH_Proj_8
Date of signed DAA
22/11/2023
Period of DAA
Two years
Website
Viral Hepatitis and Liver Disease - Health Informatics Collaborative
Cardiovascular
NIHR Health Informatics Collaborative (HIC) Cardiovascular
This theme has collated anonymised clinical information on patients presenting with this condition from five of the UK's major cardiac centres, including Imperial, University College London, St Thomas', Oxford and King's College Hospital. Data include information on the state of the arteries and the heart muscle and a measure of the degree of damage to the heart. This information is being used by NHS researchers to help our understanding of the factors that are important in the outcome of patients with acute coronary syndromes.
Organisation
Imperial College Healthcare NHS Trust
Unique project identifier
SDE_TVS_OUH_Proj_9
Date of signed DAA
01/07/2023
Period of DAA
Five years
Website
Cardiovascular - Health Informatics Collaborative
DiAlS
Digital Alerting for Sepsis (DiAlS) - NIHR Health Informatics Collaborative (HIC)
The DiAlS study will investigate the impact of digital sepsis alerts on patient outcomes and staff activity in NHS hospital trusts across England and Wales. As UK hospitals move from paper based to electronic health records, the integration of digital alerts to identify patients at risk of deterioration has also become common.
There is a clear benefit to patients from this research as the results will contribute to improved screening for sepsis in NHS hospital trusts and make recommendations for how alerts designed to improve patient outcomes are best introduced and evaluated.
DiAlS utilises the NIHR Health Informatics Collaborative (HIC) Data Sharing Framework.
Organisation
Imperial College Healthcare NHS Trust
Unique project identifier
SDE_TVS_OUH_Proj_10
Date of signed DAA
05/06/2021
Period of DAA
Five years
Website
Health Informatics - The Institute of Cancer Research, London
FIT
Faecal Immunochemical Test (FIT) Clinical Audit
The faecal immunochemical test (FIT) is already an effective test for colorectal cancer.
However, it seems likely that the consideration of FIT test results in the context of other results obtained for the same patients would allow us to improve the sensitivity and specificity of FIT testing, and thus help us to stratify and prioritise patients for further investigation or treatment.
Organisation
Oxford University Hospitals NHS Foundation Trust
Unique project identifier
SDE_TVS_OUH_Proj_11
Date of signed DAA
02/05/2024
Period of DAA
Two years
Website
Home tests for colorectal cancer could prioritise patients for referral - OxCODE
Sonobiometry
RAIQC GEHC Sonobiometry Tool
Premarket evaluation of GE Healthcare's AI tool using retrospective patient data sourced from Oxford University Hospitals SDE and the RAIQC pre-market evaluation platform.
Organisation
RAIQC Ltd
Unique project identifier
SDE_TVS_OUH_Proj_12
Date of signed DAA
15/11/2024
Period of DAA
12 months
Website
Developing tools for pre-market evaluation and post-market surveillance of Medical Imaging AI - gtr.ukri.org
JivaRDX
JivaRDX
Premarket evaluation of JIVA's AI tool using retrospective patient data sourced from Oxford University Hospitals SDE and the RAIQC pre-market evaluation platform.
Organisation
RAIQC Ltd
Unique project identifier
SDE_TVS_OUH_Proj_13
Date of signed DAA
01/10/2024
Period of DAA
12 months
Website
Developing tools for pre-market evaluation and post-market surveillance of Medical Imaging AI - gtr.ukri.org
OxPOS
Oxford Precision Oncology for Sarcoma (OxPOS)
The project is a prospective, observational study of patients with sarcoma. It will involve the collection of additional blood and tissue samples for genetic analysis. It will involve also the re-use of clinical and laboratory data - in particular, medical imaging - collected in the course of routine care.
The purpose of the study is to provide information that will improve the diagnosis and treatment of sarcoma: a disease with relatively low survival rates (currently 55 percent at five years).
Organisation
Roche
Unique project identifier
SDE_TVS_OUH_Proj_14
Date of signed DAA
12/04/2021
Period of DAA
Seven years
Website
OxPOS: Oxford Precision Oncology for Sarcoma - Health Research Authority
Radiology Reports
Automated generation of radiology reports from PET-CT scans using machine learning
The primary aim of this project is to assess the feasibility of creating pre-reports to assist radiologists in radiology report writing for PET-CT scans. The quality of the generated pre-reports will be assessed with a variety of metrics designed to capture clinical accuracy and readability of reports and similarity to the 'real' reports which will be an outcome measure of this study.
The clinical accuracy refers to firstly diseases being correctly identified, and secondly changes from previous scans correctly captured. The readability and similarity of the reports will be assessed by more common NLP metrics with scores such as: BLEU, METEOR, ROUGE.
By stratifying the data based on the ID of radiologists that compiled the original report, we will also assess whether the AI generates reports that agree equally well with the reports generated from different radiologists or there is model bias to specific writing style.
The proposed research will generate novel techniques for jointly modelling visual and textual information from medical databases, an area where current generation of algorithms is still inadequate. Such models will be able to extract wealth of information from routinely produced radiology reports to learn how to better detect pathologies in medical scans.
When these models make a prediction for the existence of a pathology in a new scan, these models will also be able to generate explanations for their prediction in natural language that can be easily interpreted by the human user. These mechanisms will enhance predictive performance and explainability of the models, factors that are necessary for reliable adoption of such tools in clinical workflows.
Organisation
University of Oxford
Unique project identifier
SDE_TVS_OUH_Proj_15
Date of signed DAA
21/06/2024
Period of DAA
Seven years
Website
MCGOWAN GROUP - Department of Oncology
BTRU
NIHR Health Informatics Collaborative (HIC) Patient Blood Management and Perioperative Care. Blood and Transplant Research Unit (BTRU)
The overall aim for the NIHR HIC Patient Blood Management and Perioperative Care research database is to improve patient blood management for patients with or at risk of anaemia, who may require blood transfusion, by building and curating a database of highly granular, longitudinal electronic healthcare record data.
Patient Blood Management (PBM) is the systematic and patient-centred approach taken to conserve blood and minimise transfusion rates to improve patient outcomes and reduce costs. Approximately half of hospital inpatients have anaemia. This area of research has been prioritised in light of recent national blood shortages.
Organisation
University of Oxford
Unique project identifier
SDE_TVS_OUH_Proj_16
Date of signed DAA
05/03/2024
Period of DAA
Five years
Website
NIHR BTRU in Data Driven Transfusion Practice - Radcliffe Department of Medicine
CSOR
Children's Surgery Outcome Reporting system
CSOR is a collaboration between the National Perinatal Epidemiology Unit (NPEU), based at the University of Oxford, and ten hospitals across England and Scotland. The team includes paediatric surgeons, other healthcare professionals, researchers, IT specialists and parents of children with surgical conditions, led by Professor Marian Knight and Professor Simon Kenny. Mr Benjamin Allin is responsible for the day-to-day management of the programme.
CSOR utilises the NIHR Health Informatics Collaborative (HIC) Data Sharing Framework.
Organisation
University of Oxford
Unique project identifier
SDE_TVS_OUH_Proj_17
Date of signed DAA
01/07/2023
Period of DAA
Five years
Website
Children's Surgery Outcome Reporting (CSOR) Research Database
DART
The Integration and Analysis of Data Using Artificial Intelligence to Improve Patient Outcomes with Thoracic Diseases (DART)
The DART project will collect and transfer clinical data, CT scans, digitised images of stained tissue sections (digital pathology) and blood-derived data from the consented participants of the lung cancer screening programme to Oxford.
Organisation
University of Oxford
Unique project identifier
SDE_TVS_OUH_Proj_18
Date of signed DAA
07/12/2021
Period of DAA
Ongoing
Website
Want to know more about the DART project aims and objectives? | DART
FAME
The Fractured Ankle Management Evaluation (FAME) Study
This project is designed to investigate the feasibility of extracting data for an economic evaluation of participants within the FAME trial (ISRCTN67007305) from the routine health record.
Once the feasibility has been determined, the process will be used in future clinical trials and the code generated shared with other NHS sites involved in the study.
Organisation
University of Oxford
Unique project identifier
SDE_TVS_OUH_Proj_19
Date of signed DAA
07/12/2020
Period of DAA
Eight years
Website
FAME Overview - The FAME Study
GAinS
Genomic Advances in Sepsis (GAinS)
The overall aim of this study is to understand how and why some patients have an extreme response to infection in order to improve patient care.
To achieve this objective, we propose to establish a sample collection from patients with sepsis and a comparator control group of patients admitted into hospital for non-septic causes to enable application of cutting edge immunological and -omic technologies that will enable us to investigate the dysregulated innate immune response seen in patients with sepsis.
Organisation
University of Oxford
Unique project identifier
SDE_TVS_OUH_Proj_20
Date of signed DAA
08/02/2022
Period of DAA
Five years
Website
Sepsis patients could get the right treatment faster based on their genes - Immunology
IORD
Infections in Oxfordshire Research Database (IORD)
The Infections in Oxfordshire Research Database (IORD) is an important part of the Modernising Medical Microbiology and Big Infection Diagnostics research theme. It includes routinely collected electronic data from hospital and GP records, covering about one percent of England.
The overall goal of IORD is to improve the management of infections and potentially infection-related episodes in UK hospitals.
Organisation
University of Oxford
Unique project identifier
SDE_TVS_OUH_Proj_21
Date of signed DAA
07/10/2019
Period of DAA
Ongoing
Website
IORD | NIHR Oxford Biomedical Research Centre
Colorectal Cancer
NIHR Health Informatics Collaborative (HIC) Colorectal Cancer
The aim of the project is to collect detailed information from a number of the UK's major colorectal cancer centres. This information will provide a significant data resource outside of a clinical trial setting, to help our understanding of the factors that are important in the outcome of patients presented with colorectal cancer, which helps the development of new and improved treatments and medicines.
Organisation
Oxford University Hospitals NHS Foundation Trust
Unique project identifier
SDE_TVS_OUH_Proj_22
Date of signed DAA
18/08/2022
Period of DAA
Five years
Website
Cancer: Colorectal - Health Informatics Collaborative
Liver Disease and Viral Hepatitis Research Database
NIHR Health Informatics Collaborative (HIC) Liver Disease and Viral Hepatitis research database
The NIHR Health Informatics Collaborative (HIC) viral hepatitis and liver disease theme is collecting electronic health care records from people infected with viruses that predominantly affect the liver, to use in research for patient benefit. Our main focus is currently advancing hepatitis B research.
Chronic hepatitis B infection affects 254 million people globally and caused around 1.1 million deaths in 2022, primarily from cirrhosis and liver cancer (hepatocellular carcinoma [HCC]). We are leading a nationwide initiative, building on clinical expertise to collect and analyse mass real-world data from electronic patient records using multidisciplinary techniques (informatics, engineering, and analytics).
This programme aims to understand the clinical trajectories and outcomes of HBV infection, including responses to antiviral therapy, development of advanced liver disease stage and liver cancer (HCC), and HBsAg loss. We are also evaluating the impact of recently expanded treatment guidelines, the effectiveness of combination antiviral therapies, and examining how other health conditions (comorbidities) and metabolic factors affect disease outcomes.
Additionally, we are investigating factors associated with care discontinuation. Hepatitis D virus (HDV) coinfection, which may worsen outcomes in people with hepatitis B, will also be characterised in terms of its prevalence in the UK and its impact on progression to severe liver disease. By informing intervention and care strategies, this programme is working towards better patient care and health outcomes for people living with hepatitis B in the UK and globally.
Organisation
Oxford University Hospitals NHS Foundation Trust
Unique project identifier
SDE_TVS_OUH_Proj_23
Date of signed DAA
26/02/2021
Period of DAA
Five years
Website
Viral Hepatitis and Liver Disease - Health Informatics Collaborative
Lung Cancer Radiomics
Lung Cancer Radiomics
This project aims to develop a novel deep learning segmentation method that can reliably segment a single tumour into different subregions according to its heterogeneity using complementary information provided in PET/CT images for lung cancer patients.
These novel subregional segmentations will allow the automated extraction of clinically interesting features (e.g. radiomics) that can support personalised decision-making process.
Ultimately, the project will also seek to link genetic mutation of the patients to the radiomics features observed in the PET/CT images. Merging genetic and imaging information can further enhance treatment planning and patient outcome predictions.
This project will bring valuable biological insights, such as understanding what phenotypic features make tumours more aggressive.
Organisation
University of Oxford
Unique project identifier
SDE_TVS_OUH_Proj_24
Date of signed DAA
10/07/2024
Period of DAA
Four years
Website
MCGOWAN GROUP - Department of Oncology
ORCHARD
Oxford Cognitive Comorbidity and Ageing Research (ORCHARD) - Clinical Record Interactive Search (CRIS) Dementia Linkage Project
Primary objective
To develop a semi-automated CT-brain analysis tool to extract standardised data on stroke lesions, white matter disease and brain atrophy.
Secondary objectives
- To extract data on markers of brain ageing (stroke lesions, white matter disease and brain atrophy) from standard NHS-acquired CT-brain and MR-brain imaging for linkage to EPR clinical data and clinical research data. The linked data will be used to determine the factors associated with delirium and cognitive decline in acutely hospitalised patients including those with stroke.
- Use the resulting CT-brain and MRI-brain analysis tools as the basis of an application for external funding towards a clinically approved tool for use in routine clinical practice.
Organisation
University of Oxford
Unique project identifier
SDE_TVS_OUH_Proj_25
Date of signed DAA
03/09/2024
Period of DAA
Two years
Website
Old Age Neuroscience and the ORCHARD portfolio - Nuffield Department of Clinical Neurosciences
OxPOS Health Economics
Oxford Precision Oncology for Sarcoma (OxPOS) Health Economics Project
Health economics is important for medical research. By comparing the health benefits and costs of medical treatments, diagnostic tools, and ways of organising care, economists provide evidence to guide better ways of spending the NHS budget.
Sarcoma patients could benefit from innovations like more accurate and comprehensive diagnostic tests and digital tools that help doctors access and interpret patient records. These innovations, currently being tested as part of the Oxford Precision Oncology for Sarcoma study, have the potential to improve the quality of care patients receive and reduce healthcare costs.
The health economics project within this study aims to provide the NHS with data to decide if these innovations are worth adopting.
Organisation
University of Oxford
Unique project identifier
SDE_TVS_OUH_Proj_26
Date of signed DAA
12/11/2024
Period of DAA
Two years
PARADISE
Predicting AF after Cardiac Surgery - the PARADISE Score. A Clinical Prediction Rule for Post-operative Atrial Fibrillation in Patients Undergoing Cardiac Surgery
The PARADISE study will develop and test new prediction tools to identify which patients are most at risk of developing AF after heart surgery. We will focus our tools on those patients who most commonly develop AF, such as those who have had surgery to repair a valve or blood vessel in their heart.
Organisation
University of Oxford
Unique project identifier
SDE_TVS_OUH_Proj_27
Date of signed DAA
23/05/2022
Period of DAA
Two years - under amendment for extension
Website
Welcome to PARADISE | PARADISE
STARLIGHT
Towards a STandardised diagnostic pAthway for hypeRtension using routine hospitaL data: An InvestiGation of the Health and economic ouTcomes (STARLIGHT)
The aim of the STARLIGHT study is to assess the value of implementing a diagnostic prediction model for hypertension within a standardised care pathway that integrates patient information between secondary and primary care.
Organisation
University of Oxford
Unique project identifier
SDE_TVS_OUH_Proj_29
Date of signed DAA
06/06/2024
Period of DAA
Two years
Website
STARLIGHT - Health Research Authority
True Colours
True Colours (Inflammatory Bowel Disease Quality of Life Study)
Post Operative Quality of Life assessment in Stricturing Crohn's Disease: A Digital Exploration with the True Colours App.
Organisation
University of Oxford
Unique project identifier
SDE_TVS_OUH_Proj_30
Date of signed DAA
20/11/2023
Period of DAA
Two years
Website
True Colours
GI Bleeding
Machine learning for risk prediction in gastrointestinal bleeding
Use of machine learning models for improving care in gastrointestinal bleeding using electronic patient records.
Organisation
University of Oxford
Unique project identifier
SDE_TVS_OUH_Proj_31
Date of signed DAA
09/11/2024
Period of DAA
Two years
Website
Patient Blood Management and Perioperative Care (PBMPC)
CDT Data Challenge
Health Data Science CDT Data Challenge Dec 24
Oxford Engineering and Physical Sciences Research Council (EPSRC) Centre for Doctoral Training (CDT) in Healthcare Data Science aims to produce collaborative, ethically responsible data scientists who can create and apply tools for data-driven healthcare and health research. The first year of the CDT programme includes intensive training in ethics, machine learning, computational statistics, and data engineering. To provide the students with a better understanding of how data science techniques should be applied, as in previous years, they do a two-week data challenge.
During these two weeks, the students will work with OUH clinicians and health researchers to address a range of issues concerning service delivery and the diagnosis and treatment of patients with a range of conditions. Working with realistic data in a translational setting is an essential aspect of the students’ training. The data will be fully anonymised. The challenge will further improve our understanding of how patients with a range of conditions are managed within OUH, and some of the insights gained may inform service planning and future care delivery.
The analysis will be conducted on a secure cloud computing platform within the University of Oxford Big Data Institute.
Organisation
University of Oxford
Unique project identifier
SDE_TVS_OUH_Proj_32
Date of signed DAA
12/11/2024
Period of DAA
Six months
Website
EPSRC Centre for Doctoral Training in Healthcare Data Science (2024 -)
Colorectal Cancer
Patient Journeys and Care Pathways in Colorectal Cancer Care: An Initial Exploration using Oxford Data
The objective of the underlying research is to develop and evaluation data extraction and analysis techniques for the generation of process graphs from time-stamped, coded data. The objective of this engagement with real world data is to briefly examine the patterns of care and to explore whether the patient journeys, at a population level, correspond to our expectations, in terms of standard care pathways and targets.
Organisation
University of Oxford
Unique project identifier
SDE_TVS_OUH_Proj_33
Date of signed DAA
02/04/2021
Period of DAA
Five years
Website
Cancer: Colorectal
UK BioBank
Integrating Multi-Modal Data in the UK BioBank to Enable Cancer Research
The UK Biobank wishes to collaborate with OUH to link digitised pathology slides of UK Biobank participants in the Oxford region with cancer diagnoses in the UK Biobank database. This will enable access to researchers registered with UK Biobank to conduct health-related research in the public interest.
Organisation
UK Biobank Limited
Unique project identifier
SDE_TVS_OUH_Proj_34
Date of signed DAA
07/08/2024
Period of DAA
Two years
Website
Integrating Multi-Modal Data in the UK BioBank to Enable Cancer Research
OMOP Mapping
OMOP Mapping evaluation
As part of this the TVS SDE team will grant access to de-identified data from OUH OMOP (Observational Medical Outcomes Partnership) datasets. They will be using their expert domain knowledge and tooling to evaluate OUH OMOP datasets to support this work. Individual/patient-level data will not leave the OUH SDE environment/servers.
Organisation
University of Oxford
Unique project identifier
SDE_TVS_OUH_Proj_35
Date of signed DAA
07/01/2025
Period of DAA
Two years
Website
Medical Sciences Division Dani Prieto-Alhambra
Partial Knee Replacement
Improving Pre-Operative Approaches to determining suitability for partial knee replacement from X-ray and MRI findings
The project is aimed at improving the accuracy of how we plan alignment assessment and realignment surgery around the knee.
Organisation
University of Oxford
Unique project identifier
SDE_TVS_OUH_Proj_36
Date of signed DAA
19/07/2024
Period of DAA
19/07/2024
Website
NDORMS Simon Abram
Last reviewed:10 March 2025