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.

This cross-disciplinary project aims to develop novel data mining and visualization tools and techniques, which will transform people's ability to analyse quantitative and coded longitudinal data. Such data are common in many sectors. For example, health data is classified using a hierarchy of hundreds of thousands of Read Codes (a thesaurus of clinical terms), with analysts needing to provide business intelligence for clinical commissioning decisions, and researchers tacking challenges such modelling disease risk stratification. Retailers such as Sainsbury's sell 50,000+ types of products, and want to combine data from purchasing, demographic and other sources to understand behavioural phenomena such as the convenience culture, to guide investment and reduce waste. To solve these needs, public and private sector organisations require an infrastructure that provides far more powerful analytical tools than are available today. Today's analysis tools are deficient because they (a) are crude for assessing data quality, (b) often involve analysis techniques are designed to operate on aggregated, rather than fine-grained, data, and (c) are often laborious to use, which inhibits users from discovering important patterns. The QuantiCode project will address these deficiencies by bringing together experts in statistics, modelling, visualization, user evaluation and ethics. The project will be based in the Leeds Institute for Data Analytics (LIDA), which houses the ESRC Consumer Data Research Centre (£5m ES/L011891/1) and the MRC Medical Bioinformatics Centre (£7m ES/L011891/1), and provides a development facilities complete with high-performance computing (HPC), visualization and safe rooms for sensitive data. Our project will deliver proof of concept visual analytic systems, which we will evaluate with a wide variety of users drawn from our partners and researchers/external users based in LIDA. At the outset of the project we will engage with our partners to identify analysis use cases and requirements that drive the details of our research, which is divided into four workpackages (WPs). WP1 (Data Fusion) will develop governance principles for the analysis of fine-grained data from multiple sources, implement tools to substantially reduce the effort of linking those sources, and develop new techniques to visualize completeness, concordance, plausibility, and other aspects of data quality. WP2 (Analytical Techniques) and WP3 (Abstraction Models) are the project's technical core. WP2 will deliver a new, robust approach for modelling data as they appear naturally in health and retail data (irregularly dispersed or sampled over time), scaling that approach with stochastic control to guide learning and resource usage, and developing a low-effort 'question-posing' visual interface to drastically lower the human effort of investigating data and finding patterns. WP3 (Abstraction Models) focuses on data granularity, and will deliver a tool that implements a working version of the governance principles we develop in WP1, and new computational and interactive techniques for exploring abstraction spaces to create inputs suited to each aspect of analysis. WP4 will implement the above tools and techniques in three versions of our proof of concept system, evaluating each with our partners and LIDA researchers/users. This will ensure that our solutions are compatible with, and scale to, challenging real-world data analysis problems. Success criteria will be time saved, increased analysis scope, notable insights, and tackling previously unfeasible types of analysis - all compared against a baseline provided by users' current analysis tools. We will encourage adoption via showcases, workshops and licensed installations at our partners' sites. The project's legacy will include tools that are embedded as an integral part of the LIDA infrastructure, a plan for their on-going development, and a research roadmap.