£10 million Intelligent Healthcare Technologies research to support self-management of chronic conditions
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Eight new research projects that will help people self-manage chronic conditions, such as COPD and diabetes, at home while linked to support from carers and clinicians, were announced today by the Engineering and Physical Sciences Research Council (EPSRC) which is funding the work with a £10 million investment.
The research will take place at seven UK universities; involve over 30 partners from industry, charities and the public sector who will add contributions worth over three quarters of a million pounds.
It will explore how to improve the use and development of networks that connect patients, and those supporting them, to clinicians and treatment. It will investigate how using wearable devices like sensors, smart-watches, activity and heart rate monitors can allow clinicians to get the right sort of data to make choices and advise patients.
The studies will use conditions such as COPD, rheumatoid arthritis, diabetes, and dementia as examples to test technology and systems' designs. The projects have different emphases, some will concentrate on how systems can be made more intelligent and interpret data coming into them, while others will develop and refine sensors and monitoring equipment and ensure they can be accessed securely.
Professor Philip Nelson, Chief Executive of the Engineering and Physical Sciences Research Council (EPSRC), said:
The UK has an aging population and the demands on our health services are growing. Monitoring chronic conditions through outpatients' clinics is both costly and time consuming for patients, surgeries and hospitals. Using these new technologies provides ways of gauging a patient's health in real-time and detecting any deterioration quickly. This will help people remain in their homes for longer, avoid congestion and delay and mean treatment can be targeted quickly and when it can be most appropriate and effective.
Details of the projects are listed below:
ASPIRE: Automated Sensing & Predictive Inference for Respiratory Exacerbation
Led by Professor David Clifton, University of Oxford
The team proposes to develop an 'intelligent' home-based system, with smart algorithms embedded within lightweight healthcare sensors, to overcome current limitations. Its novel work will incorporate next-generation machine learning algorithms to combine information from healthcare sensors with information from GP and hospital visits.
This will enable the system to learn the 'normal' health condition for individual patients, with knowledge of other conditions from which they may be suffering, and which can then make recommendations to the patient concerning self-management of their condition.
Project partners: Microsoft Research Ltd; Oxford University Hospitals NHS Trust; Oxehealth Ltd.
EPSRC Grant £1.5 million - Total partners' contribution £416,000
PAMBAYESIAN: Patient Managed decision-support using Bayesian networks
Led by Professor Norman Fenton, Queen Mary University of London
The overall goal of the project is to create a new generation of intelligent medical decision support systems with real-time monitoring for chronic conditions, based on expert-built Bayesian Networks (BNs).
The team's aim to extend current techniques for developing BNs so that they can be used in a system with several layers (sensor, patient, clinician) in a co-ordinated way.
They will design systems that fit into, and improve, the interaction between patients and the healthcare team. Enable BNs to run in the computing resources available at different places in the overall system eg on small computers.
They will also look towards practical applications. This project has a number of technology companies involved on the Advisory Board to help ensure effective commercial exploitation in the long run.
Project Partners: Mediwise Ltd; IBM United Kingdom Limited; Rescon Technologies; uMotif Limited; SMART Medical Limited; Hasiba Medical GmbH; Be More Digital Ltd.
EPSRC Grant £1.5 million - Total Partners' contribution £63,000
ARISES: An Adaptive, Real-time, Intelligent System to Enhance Self-care of chronic disease
Led by Dr Pantelis Georgiou, Imperial College London
This project will develop an Adaptive, Realtime, Intelligent System (ARISES) that will run on a smart phone locally and collect data from multiple sources to deliver an intervention to the patient that allows self-management of chronic disease.
The core of ARISES will use Case-based reasoning (CBR), a consolidated artificial intelligence technique which can solve problems in much the same way as a
human does, using historical data and scenarios as a reference to recommend a current solution which can treat the patient.
CBR is also powerful in that it has the capability to be adaptive according to patient lifestyle and behaviour and always provide the most optimum solutions for a given set of resources. ARISES will have the capability to collect data from wearable devices such as smart-watches, activity monitors, heart rate monitors and continuous glucose meters and using smart-algorithms will be able to extract meaningful information to provide to the CBR system.
Underpinning this will be energy efficient algorithms which make ARISES aware of what sensors are connected to the patients local area network, safety systems that minimise the risk of any possible undesired event related to the management of the disease, and a data security to make sure information is protected against non-authorised access.
ARISES will provide a generic framework which can be used to treat many chronic diseases such as asthma, chronic obstructive airways disease, hypertension, heart failure, ischaemic heart disease, arrhythmias and chronic neurological conditions.
The team will use diabetes, which currently affects 3 per cent of the world's population, as an exemplar chronic condition to demonstrate the ARISES framework, and will target improvement in glycaemic control which can reduce micro- and macrovascular complications associated with the disease.
In this context the system will promote the self-management of diabetes by optimising glucose control through insulin dosage recommendations, exercise and physical activity support, carbohydrate recommendations to prevent hypoglycaemia, and behavioural change through education.
Project partners: Imperial College Healthcare NHS Trust; Dexcom Inc; ICON Clinical Research (UK) Ltd.
EPSRC Grant £1.3 million - Total partners' contribution £79,000
SCAMPI: Self-Care Advice, Monitoring, Planning and Intervention
Led by Professor Neil Maiden, City, University of London
The project's prime objective is to prototype a new computerised toolset to support the more seamless care planning, monitoring and interventions by people with chronic conditions and their carers.
To achieve this, a team of leading researchers in computer science, the health sciences and digital business at City, University of London will co-design, prototype and evaluate the feasibility of the toolset with people with two example chronic conditions - dementia and Parkinson's disease. The researchers will work closely with the Alzheimer's Society and Parkinson's UK in order to engage people with these conditions, their families, carers and disease experts.
Project partners: Parkinson's UK; Evalucom Consulting Limited; NHS Sutton Clinical Commissioning Group; CWHHE CCGs Commissioning Collaborative; My Home Life; Alzheimer's Society.
EPSRC Grant £0.98 million
CONSULT: Collaborative Mobile Decision Support for Managing Multiple Morbidities
Led by Professor Simon Parsons, King's College London
CONSULT will combine wireless 'wellness' sensors with intelligent software running on mobile devices, to support patient decision making, and thus actively engage patients in managing their healthcare. Software will use computational argumentation to help patients follow treatment guidelines and will learn details specific to individuals, personalising treatment advice within medically sound limits.
The technology will be evaluated in many ways in a proof-of-concept study, engaging stroke patients, their carers and medical professionals, while capitalising on King's College London's world-leading position in stroke research and its established patient groups, particularly those connected to the South London Stroke Register programme.
EPSRC Grant £1.35 million
The Wearable Clinic: Connecting Health, Self and Care
Led by Dr Niels Peek, The University of Manchester
The research team propose to develop a set of software tools for smartphones and tablets, called the 'Wearable Clinic'. This will help patients with long term conditions, together with their carers and doctors, to better manage their health in daily life, respond more quickly to changes in symptoms and prevent fall back episodes.
The project will trial methods of data collection and use that will take account of individual risk factors (eg age, ethnicity) and conserve the battery life of devices.
While the Wearable Clinic concept could potentially be useful for managing a range of long term conditions, the team will test it with two different conditions, where symptoms are known to fluctuate over time: schizophrenia and chronic kidney disease.
Project partners: Cerner Corporaton; Withings SAS; UK Renal Registry; Manchester Mental Health & Social Care; Nat Institute for Health and Care Excellence; Health and Social Care Information Centre; Manchester mHealth Ecosystem; Health Innovation Manchester.
EPSRC Grant £1.6 million - Total partners' contribution £115,000
STRETCH: Socio-Technical Resilience for Enhancing Targeted Community Healthcare
Led by Mr Blaine Price - Open University
The STRETCH project aims to coordinate
circles of support for older adults recovering at home following hospital treatment.
These 'circles', which include relatives and neighbours, the voluntary sector, carers and medical professionals, would be coordinated through the use of both wearable and smart home technologies with the double benefit of increasing NHS capacity to cope with an increasing number of older patients, while improving care by ensuring that medical professionals have timely and accurate information at all times about patients.
Project partners: Age UK Milton Keynes; Milton Keynes University Hospital NHS Foundation Trust; Age UK Exeter; Exwick Health Centre; NEW Devon CCG.
EPSRC Grant £1 million - Total partners' contribution £95,000
A Novel Wearable Technology for Early Detection of Exacerbations in COPD
Led by Professor Esther Rodriguez-Villegas - Imperial College London
The aim of the project is to create a small, lightweight and low-cost novel wearable wireless technology which can be worn around the neck to monitor patients suffering from Chronic Obstructive Pulmonary Disease, currently the fourth leading cause of death in the world that affects about 3 million people in the UK.
It is hoped that the devices will be able to provide early detection of potential exacerbations and inform patients and medical professionals so they can be treated promptly.
It is also anticipated that the devices will reduce the cost of hospitalisations and visits to GPs, therefore resulting in savings to the NHS.
Project partner: Biocompatibles UK Ltd.
EPSRC Grant £0.97 million - Partner contribution £15,000
Notes to Editors:
The Engineering and Physical Sciences Research Council (EPSRC)
As the main funding agency for engineering and physical sciences research, our vision is for the UK to be the best place in the world to Research, Discover and Innovate.
By investing £800 million a year in research and postgraduate training, we are building the knowledge and skills base needed to address the scientific and technological challenges facing the nation. Our portfolio covers a vast range of fields from healthcare technologies to structural engineering, manufacturing to mathematics, advanced materials to chemistry. The research we fund has impact across all sectors. It provides a platform for future economic development in the UK and improvements for everyone's health, lifestyle and culture.
We work collectively with our partners and other Research Councils on issues of common concern via Research Councils UK.