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Our research is devoted to advancing the science of healthcare delivery for management of major cardiovascular conditions and harnessing the power of Big Data to drive change. Large healthcare datasets are being used to create more transparency about healthcare performance and for better risk stratification of populations for more targeted interventions.

Deepmedicine logo

The Healthcare Innovation and Evaluation unit is an interdisciplinary research unit of The George Institute at the Oxford Martin School and was established in 2010 by Kazem Rahimi. The aim of the unit is to conduct high impact research to advance our understanding of common chronic diseases, to investigate how they are currently managed in clinical practice and how their management at individual and population level could be further improved. The main clinical emphasis of the programme is on cardiometabolic risk factors and conditions such as hypertension, diabetes, ischaemic heart disease, stroke, heart failure, atrial fibrillation, and vascular dementia.

The research methods in the Healthcare Innovation and Evaluation unit are based on epidemiology, statistics and clinical medicine, which are combined with methodologies from other disciplines such as data science, engineering and social sciences to tackle some of the most challenging healthcare problems of the 21st century.


Deep Medicine

This programme will use some of the largest and most complex biomedical datasets that have ever been collected to generate insights into complex disease patterns, risk trajectories and treatment effects. Through an interdisciplinary approach, established and novel techniques in data mining, machine learning and deep learning will be applied to complex biomedical datasets.

Advances in medicine over the past few decades have led to an unprecedented increase in life expectancy and reduction in major disabilities. But this longevity brings with it new problems: a rise in chronic conditions such as diabetes, dementia and heart failure, with many patients suffering from multiple conditions at the same time (multimorbidity), which are poorly understood.

The advent of ‘Big Data’ can offer unprecedented opportunities for extending our knowledge and understanding of complex disease patterns and risk, but harnessing its full potential will require new and innovative methods of analysis.

We are developing scalable methods for analysis of datasets, prepare and transform data in order to apply new and existing algorithms, and advance our assessment of treatment effects by including patient features such as multimorbidity in analysis of trial data.

Blood Pressure Lowering Treatment Trialists' Collaboration (BPLTTC)

The BPLTTC was conceived and initiated in 1995 as a collaboration between the principal investigators of all the major ongoing clinical trials of blood pressure lowering agents. Although previous cycles of BPLTTC have clarified the benefits and risks of blood pressure lowering, significant uncertainties of the benefits and risks of blood pressure lowering remain. A new cycle of the BPTTC aims to substantially extend the current collaboration to include up to a half a million individual participant data with all available baseline and outcome data in an effort to reduce the remaining uncertainties about the safety and efficacy of blood pressure lowering.

This new cycle of the collaboration is coordinated by the George Institute for Global Health in Oxford. Its work is governed by a Steering Committee which consists of the Director of BPLTTC, Kazem Rahimi, as well as select Principal Investigators of the largest trials included in BPLTTC. The Steering Committee is responsible for the general oversight of BPLTTC, providing scientific leadership regarding all aspects of the proposal development, analysis, interpretation and reporting.

Our team











Principal Investigator

Selected publications

Clinical studies and trials

In the UK, the provision of care for patients with chronic heart failure outside hospitals and GP surgeries is often fragmented and inadequate. The National Heart Failure Audit Report states that approximately half of patients hospitalised with heart failure will either die or be re-admitted within one year. This is costly, inefficient and has a detrimental effect not only on the patient but also on their families and loved ones.

The George Institute for Global Health and the Institute of Biomedical Engineering at the University of Oxford have developed an innovative research project that aims to address these issues by developing and evaluating a simple heart failure monitoring and risk prediction system that patients can use effectively in their own homes. 

The SUPPORT-HF (Seamless User-centred Proactive Provision Of Risk-stratified Treatment for Heart Failure) system is comprised of a tablet PC application that allows patients to report severity of their symptoms and to wirelessly collect information on weight, blood pressure, heart rate and physical activity on a daily basis. This information will then allow researchers to identify patterns that might predict which patients are going to need hospitalisation enabling them to intervene earlier in a more informed fashion.

If the system is successful, it will reduce the cost to the health service of un-predicted hospitalisations and help patients better manage their conditions at home.


John Cleeland, Imperial College London

Terry Dwyer, University of Oxford

Raymond Fitzpatrick, University of Oxford

Simon Lovestone, University of Oxford

Stephen MacMahon, University of Oxford

Stephen Smith, University of Oxford

Lionel Tarrassenko, University of Oxford

Andrea Vedaldi, University of Oxford

Mark Woodward, University of Oxford

Related research themes