Intelligent Wearable Sensors for Predictive Patient Monitoring

There is an urgent, unmet need for reliable, continuous patient monitoring, both in hospitalised patients, and in home settings. Delays in recognition of the changes in vital signs associated with physiological deterioration worsen outcomes and increase healthcare costs. In hospital wards patients are mainly mobile and unmonitored by electronic systems. Manual observations of vital signs (heart rate, respiratory rate, blood pressure, temperature, oxygen saturation and level of consciousness) are made by a nurse every 4 - 12 hours, resulting in late recognition of deterioration. Over 40,000 patients deteriorate on UK wards each year until they are so unwell that they require admission to an Intensive Care Unit (ICU). Care is delayed in over a third of these patients, and the mortality rate for such emergency admissions is around one quarter.

Wearable monitoring systems are required to address the needs of ambulatory patients in hospital. However, no wearable systems have penetrated into clinical practice at scale, due to: (i) poor tolerance of existing wearable devices for vital-sign monitoring; (ii) a lack of robustness in the estimates of the vital signs that wearable sensors produce; (iii) very limited battery life that requires batteries to be re-charged at a rate that prevents their use on a large scale; and (iv) limited subsequent use of the data for clinical decision support.

We propose to develop a range of "intelligent" wearable sensors, with smart algorithms embedded within them, to overcome these limitations. Our disruptive sensor system will build on major EPSRC-funded grants that have demonstrated proof-of-concept: the "Hospital of the Future" Grand Challenge in Healthcare IT (2009-2013) and the "Centre of Excellence in Medical Engineering" (2009-2015, funded jointly by the Wellcome Trust).

The proposed programme has the potential for very significant impact for patients in the hospital or in the home, by optimising patient management and improving outcomes. Patients who are deteriorating will be identified early, which will allow preventative action to be taken, avoiding serious escalation (such as unplanned admission to an ICU), and which will reduce the incidence of preventable morbidity, cardiac arrests, and death. Those hospital patients recovering faster than expected, and who are deemed to be sufficiently stable, can be discharged home earlier. Patient-specific care will be enabled using intelligent sensors that learn patient characteristics in real-time, thus improving patient outcomes by improving the efficacy of care provided. The translation of such systems into the "hospital-at-home" environment will provide benefit to patients with long-term conditions, such as chronic obstructive pulmonary disease (COPD) and heart failure - this will be achieved via predictive systems that allow clinicians to track patient condition without the false-alarm rate associated with existing systems, and which prevents existing systems providing benefit to existing patients.

The NHS as an organisation will benefit from the research because improved patient outcomes are associated with lower healthcare costs, as a result of shorter stays in hospital and fewer unplanned admission to (expensive) higher levels of care. Additionally, patients will be stratified according to risk of severity / deterioration, allowing improved use of clinical staffing resources. Clinicians will benefit by being able to interpret, for the first time, the very large and heterogeneous datasets that are available for their patients - enabled by robust, probabilistic tools created during the programme.

Companies in the commercial private sector will benefit from the research, where involvement of the industrial partners will allow rapid implementation of the techniques developed during the programme. Such companies have an interest in "intelligent healthcare" algorithms that can be integrated into existing healthcare IT products, which will add significant value and market differentiation. Additionally, the rapidly-growing market for wearable devices is currently focused only on consumers - the proposed work will extend this market to healthcare technologies, by exploiting the opportunities for large-scale innovation and clinical validation that exist in the programme. The UK economy will benefit by the possible creation of new spin-out activity based around the activity of the proposed work, in addition to the "first to market" advantage conferred on the industrial partners.

The scientific community, and the UK research base in particular, will benefit from developing capacity in an emerging field of global importance, and where the proposed project will train the next generation of researchers in intelligent healthcare sensing. The methodology developed within the proposed programme will be of translational benefit to other scientific disciplines, including other computational and mathematical sciences. Results from the research will feed into the Alan Turing Institute, where researchers involved across the UK will benefit from the development of large-scale sensing methodologies for data science.

The public will benefit via public-engagement activities run in collaboration with the Institution for Engineering & Technology (the world's largest multidisciplinary engineering professional institution), the George Institute for Global Health (which holds public-engagement conferences at its bases in Oxford, Sydney, Beijing, and New Delhi), the Royal Academy of Engineering (which supports Clifton's work via a Research Fellowship), and the NIHR Oxford Biomedical Research Centre (which holds regular public outreach events).