ASPIRE: Automated Sensing & Predictive Inference for Respiratory Exacerbation

There is an urgent, unmet need for reliable, intelligent systems that can monitor patient condition in the home, and which can help patients manage long-term conditions. Delays in recognition of the changes in physiological state worsen outcomes and increase healthcare costs. The ASPIRE programme uses chronic obstructive pulmonary disorder (COPD) as an exemplar, which affects over 210 million people globally. This condition costs the National Health Service over £800 million each year, over half of which is spent treating patients in hospital, rather than caring for them in their homes.

Intelligent monitoring systems are required to address the needs of patients with long-term conditions in their homes. However, no wearable systems have penetrated into clinical practice at scale, due to: (i) poor tolerance of existing wearable devices for 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 helping the patient understand and manage their condition.

We propose to develop an "intelligent" home-based system, with smart algorithms embedded within lightweight healthcare sensors, to overcome these limitations. Our 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 "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. This work will include close working with world-leading clinicians to ensure that the recommendations provided by the system are correct for the individual patient.

The proposed programme has the potential for very significant impact for patients in the home who are suffering from long-term conditions, by optimising self-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 presentation to a hospital Emergency Department), and which will reduce the incidence of preventable morbidity and death. Patient-specific care will be enabled using an intelligent system that learns patient characteristics in real-time, thus improving patient outcomes by improving the efficacy of care provided, and the recommendations made to patients to assist in (i) understanding their own condition and (ii) taking steps to maintain stability.

The translation of such systems into the home-care environment will provide benefit to patients with long-term conditions, such as chronic obstructive pulmonary disease (COPD, our exemplar) - this will be achieved via predictive systems that allow patients to track view their condition, and robust forecasts of their health status, without the false-alarm rate associated with existing systems, and which prevents existing systems providing benefit to existing patients. Healthcare workers, including community nurses and GPs, will benefit from the outputs of the programming by being given a quantitative assessment of cohorts of their home-based patients, and records of both their physiological data and associated predictions / recommendations made by the ASPIRE system.

The NHS as an organisation will benefit from the research because improved patient outcomes are associated with lower healthcare costs, as a result of fewer stays in hospital and fewer unplanned admission to (expensive) higher levels of care. Additionally, patients can be stratified according to risk of severity / deterioration, allowing improved use of community 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 community care, and integrated home/hospital provision. 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 charities described in "Pathways to Impact".