There is an extremely high demand for laboratory-based blood tests from community settings in the UK and analysis suggests an important role in the future for remote blood monitoring that would enable patients and health professionals to carry out their own tests remotely, greatly benefiting patients and speeding up decision making. The COVID-19 pandemic has further highlighted the need for remote and connected blood testing that is beyond the online virtual clinics in the NHS outpatient setting. In current blood testing services for community healthcare, it is challenging to obtain and process blood samples outside of the clinical setting without training and lab facilities, and patients are required to attend a GP surgery or hospital for tests with travel burden and infection risk. Many blood analyses are done in batches that take a long time to build up, meaning the speed of blood sample analysis of routine tests and time taken for diagnosis are further challenges. Despite recent innovations in point of care, current blood analysis tools in practice are mainly mechanical or labour-intensive that require extensive filtering and manual tweaking and not suitable for regular at-home monitoring and longitudinal analytics. There is no personalised real-time approach available to inform disease complexity and conditions over time, which are critical for early detection of acute diseases and the management of chronic conditions. In England, around 95% of clinical pathways rely on patients having access to efficient, timely and cost-effective pathology services and there are 500 million biochemistry and 130 million haematology tests are carried out per year. This means inefficient and infrequent blood testing leads to late diagnosis, incomplete knowledge of disease progression and potential complications in a wide range of populations. Taking those challenges into account and current digital transformation in healthcare, this is a timely opportunity to bring researchers, clinicians and industrialist together to address the challenges of blood monitoring and analytics. The proposed Network+ will build an interdisciplinary community that will explore future blood testing solutions to achieve remote, inclusive, rapid, affordable and personalised blood monitoring, and address the above challenges in community health and care. To achieve the Network+ vision, research of technologies will be conducted from collaborations among information and communication technology (ICT), data and analytical science, clinical science, applied optics, biochemistry, engineering and social sciences in the Network+. The network will address three key technical challenges in blood testing: Remote monitoring, ICT, Personalised data and AI in a range of examplar clinical areas including cancer, autoimmune diseases, sickle cell disease, preoperative care, pathology services and general primary care.

Around 1 in 4 people have multiple long-term conditions (MLTCs) rising to up to two-thirds in people over the age of 65 years. Treatment for this group is estimated to take up 70% of health care expenditure. Such people have poorer health, poorer quality of life, and a higher risk of dying. Key challenges for this group of people include maintaining their independence in their homes, avoiding developing further conditions that can threaten their health, and which would further impair their quality of life and minimising the high burden of healthcare for this group potentially made worse by uncoordinated health and social care. Our challenge is to improve outcomes through informed self-care and maintaining independence, while reducing healthcare costs. The current model for many MLTCs is for people to present to urgent care services when they can no longer cope at home. This reactive approach leads to frequent use of emergency hospital services when a severe episode occurs, shifting the focus of care to hospitals. Management then follows generic pathways within acute healthcare, in an attempt to stabilise the condition of the patient. Information-driven technologies will enable people to perform their own health management, which will change the model of care. Individuals will be able to manage their condition proactively. The integration of knowledge concerning individuals' co-morbidities (which are common in MLTCs) will allow personalised therapy, further maintaining independence, improving patient outcomes, and optimising the use of resources. The proposed programme "Healthcare Wearables for Independent Living" (HW-IL) aims to develop, for the first time, a suite of predictive tools, based on regular wearable devices, to allow a step-change in the self-care of patients with MLTCs, and in the maintenance of their independence by avoiding deterioration. Patients and their carers will be guided, using such tools, to preventative management. For the first time, such tools will incorporate an integrated approach, exploiting patient-worn devices (at or near the patient), and healthcare data (from GPs and hospital information systems), working in real-time. All work will be ethically approved, and accord to the highest standards of patient confidentiality.

Data science and artificial intelligence will transform the way in which we live and work, creating new opportunities and challenges to which we must respond. Some of the greatest opportunities lie in the field of human health, where data science can help us to predict and diagnose disease, determine the effectiveness of existing treatments, and improve the quality and affordability of care. The Oxford EPSRC CDT in Health Data Science will provide training in: - core data science principles and techniques, drawing upon expertise in computer science, statistics, and engineering - the interpretation and analysis of different kinds of health data, drawing upon expertise in genomics, imaging, and sensors - the methodology and practice of health data research, drawing upon expertise in population health, epidemiology, and research ethics The training will be provided by academics from five university departments, working together to provide a coordinated programme of collaborative learning, practical experience, and research supervision. The CDT will be based in the Oxford Big Data Institute (BDI), a hub for multi-disciplinary research at the heart of the University's medical campus. A large area on the lower ground floor of the BDI building will be allocated to the CDT. This area will be refurbished to provide study space for the students, and dedicated teaching space for classes, workshops, group exercises, and presentations. Oxford University Hospitals NHS Foundation Trust (OUH), one of the largest teaching hospitals in the UK, will provide access to real-world clinical and laboratory data for training and research purposes. OUH will provide also access to expertise in clinical informatics and data governance, from a practical NHS perspective. This will help students to develop a deep understanding of health data and the mechanisms of healthcare delivery. Industrial partners - healthcare technology and pharmaceutical companies - will contribute to the training in other ways: helping to develop research proposals; participating in data challenges and workshops; and offering placements and internships. This will help students to develop a deep understanding of how scientific research can be translated into business innovation and value. The Ethox Centre, also based within the BDI building, will provide training in research ethics at every stage of the programme, and the EPSRC ORBIT team will provide training in responsible research and innovation. Ethics and research responsibility are central to health data science, and the CDT will aim to play a leading role in developing and demonstrating ethical, responsible research practices. The CDT will work closely with national initiatives in data science and health data research, including the ATI and HDR UK. Through these initiatives, students will be able to interact with researchers from a wide network of collaborating organisations, including students from other CDTs. There will also be opportunities for student exchanges with international partners, including the Berlin Big Data Centre. Students graduating from the CDT will be able to understand and explore complex health datasets, helping others to ask questions of the data, and to interpret the results. They will be able to develop the new algorithms, methods, and tools that are required. They will be able to create explanatory and predictive models for disease, helping to inform treatment decisions and health policy. The emphasis upon 'team science' and multi-disciplinary working will help to ensure that our students have a lasting, positive impact beyond their own work, delivering value for the organisations that they join and for the whole health data science community.