Healthcare is a prime example of "big data science" with a number of challenges and successful stories where actionable information extracted from data has improved and saved lives [1]. The majority of concerted efforts focused on real-time processing and integration of structured data streams coming from clinical coding, diagnostic tests, sensor measurements, questionnaires, etc. to support timely clinical interventions and facilitate patients' self-management. Nonetheless, natural language remains the main means of communication within healthcare with its written accounts becoming increasingly available in an electronic form, thus giving rise to big text data. Prominent examples include text data embedded within electronic health records (e.g. referral letters, case notes, pathology reports, hospital discharge summaries, etc.), patient-reported outcome measures (e.g. questionnaires, diaries, etc.) or unsolicited informal feedback shared openly on the Web 2.0 (e.g. social media, fora, etc.). Unfortunately, the capacity to effectively utilise information from unstructured text data on a big scale is lagging behind its structured counterpart. The fact that the majority of actionable information in healthcare is contained within text data (some estimates shows as much as 85%) clearly indicates a potential to dramatically transform community health and care by the ability to process and integrate such information in real time. However, automated and large-scale "understanding" of diverse healthcare sublanguages is still largely unsolved research challenge due to their dynamics, idiosyncrasy, ambiguity and variability. The aim of this proposal is to build a UK-wide multi-disciplinary research network in order to explore the barriers to effectively utilising healthcare narrative text data, road-map research efforts and principles for sharing text data and text analytics methods between academia, NHS and industry. The network will directly address the "Transforming Community Health and Care" grand challenge by enabling research that will deploy healthcare narratives as real-time sensors and integrate them with the structured data streams into a patient-focused collaborative ecosystem, which will involve healthcare professionals, patients, carers and researchers. Such systemic network of healthcare activities will facilitate informed decision making, timely interventions, deeper digital phenotyping for clinical epidemiology and population-based modelling. On the other hand, by processing patient-generated narratives, which are often a preferred and likely means to provide patient responses (e.g. text messages) to complement structured healthcare data (e.g. signals from wearable devices), we will "use real-time information to support self-management of health and wellbeing". The main outcome of the network will be a strong, sustainable community that will continue its mission after the initial 3 years of support. Other outcomes will include (1) reports describing the state-of-the-art and challenges for key barriers in harnessing text narratives and making sense from them; (2) a research roadmap for healthcare text analytics; (3) an enlarged membership and expanded collaborations within the network, in particular with early career researchers and internationally; (4) a series of focused pilot/feasibility projects that will inform further developments and kick-start collaborative projects; (5) a collection of research papers at conferences and journals, improving the UK competitiveness in this growing area; (6) several project proposals scoped during the project and prepared for submission; (7) proposals for discipline-bridging personal fellowships, and (8) an interactive registry of healthcare text analytics expertise, resources and tools so that the users and collaborators can identify existing resources and initiate new collaboration.

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.