The Wearable Clinic: Connecting Health, Self and Care

An increasing number of people live with long term physical and mental health conditions, such as diabetes, heart disease or depression. Many of these people find that their symptoms fluctuate in severity over time, including periods of relative calm and episodes during which symptoms become much worse. However, patients with long term conditions typically see their doctor during pre-arranged visits at fixed intervals, rather than on the basis of their current symptoms. For instance, people with chronic kidney disease commonly have appointments every 3 months. These visits are often felt unnecessary during stable periods, during which patients could probably manage well by themselves, but irregular enough to spot worsening symptoms early enough and prevent more severe episodes of illness - what we call 'fall back episodes'.

We 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. This could prevent unplanned admissions to hospital, which are not only distressing and disruptive for patients and their families, but expensive for the NHS. Furthermore, it could make it easier to integrate care for patients with multiple long term conditions (e.g. both diabetes and chronic kidney disease), who are often treated by different doctors, at different places, and at different times.

For patients, using the Wearable Clinic starts with measuring symptoms in daily life using wearables. These data are then automatically combined with data held in NHS records on their diagnoses, lab results, and treatments in order to predict the likely future course of symptoms, and whether there is a risk of a fall back episode. Finally, the software will propose a modifiable care plan that takes account of the patient's range of existing conditions, current and predicted health status, availability of local care resources, and the patient's own preferences. Where it is possible and safe to do so, care plans will remove clinically unnecessary and unwanted appointments, saving time and money for both the patient and the NHS.

To achieve this vision, we propose to apply data science techniques to analyse data collected from a) medical records and b) wristband wearables and smartphone technologies ('wearables') worn by patients with long term conditions. While the Wearable Clinic concept could potentially be useful for managing a range of long term conditions, we will first test it out in two different conditions, where symptoms are known to fluctuate over time: schizophrenia and chronic kidney disease. Statistical techniques will be applied to see if data collected from patients using wearables can be used to a) predict changes in symptoms and b) produce tailored care plans for individual patients. We will trial methods that collect and use data in ways that take into account individual risk factors (e.g. age, ethnicity) and conserve the battery life of devices.

While the project primarily aims to develop new computer algorithms, statistical models and computer software, we will trial the technical aspects of the Wearable Clinic with a small number of healthy volunteers, people with schizophrenia and people with chronic kidney disease. We will also investigate costs, benefits, and potential risks of the Wearable Clinic in its earliest stages of development and, where necessary and feasible, integrate solutions during the lifetime of the project. A series of workshops open to the public will be held to explore cross-cutting issues such as trustworthy data use and privacy. This will pave the way for future studies and maximise the chances that the Wearable Clinic actually makes it into practice - thus improving the quality of care for patients with long term conditions.

Economic: To quantify the potential value of the Wearable Clinic for the healthcare system and for patients, we will conduct an early economic evaluation and value of information analysis. This will assess the economic feasibility of the Wearable Clinic concept and inform future investment decisions as well as decisions to conduct further research to reduce the key (developmental and post-market) uncertainty.

The market size for wearable sensors is estimated to be £70B by 2025, giving substantial scope for commercialisation work. It is highly likely that patentable IP will be produced as part of the Wearable Clinic and the research team will work with UMIP (University of Manchester Intellectual Property) and Trustech (the North West's NHS innovation service) who have a strong track record in bringing innovative products to market. This make take the form of creating spin-out companies or licensing technologies as deemed most appropriate. To de-risk this process industrial support has been secured from a number of partners (see Letters of Support), including Cerner as major provider of healthcare IT systems and Withings as an SME in wearable health technology. This will be complemented by support from the Greater Manchester Connected Health Ecosystem, a partnership of >70 organisations, including NHS, social care, patient groups and industry.

Societal: Long-term conditions conditions place a major burden on health and social care services, coupled with significant reduction in quality of life for affected individuals. Half of GP appointments, and £7 in every £10 spent on health and social care, is spent on treating and caring for people with LTCs. The number of people affected, especially those with multiple coexisting conditions, increases steadily as a result of population ageing and lifestyle factors. The NHS faces the challenge of implementing £22bn of efficiency savings by 2020, and it is expected that important savings will have to come from the intelligent use of IT for patients with long-term conditions.

The Wearable Clinic has the potential to improve patient activation and stimulate self-management in patients with LTCs. Improved patient activation and better self-management are associated with improved health outcomes and lower care consumption, and generally lead to higher satisfaction with health services. The research team has established collaborations with the Manchester Academic Health Sciences Centre (MAHSC) partnership between the University of Manchester and the NHS, the Greater Manchester Academic Health Science Network and Health Innovation Manchester to ensure future translation of the results of the project into the NHS.