PAMBAYESIAN: PAtient Managed decision-support using Bayesian networks

Patients with chronic diseases must take day-to-day decisions about their care and rely on advice from medical staff to do this. However, regular appointments with doctors or nurses are expensive, inconvenient and not necessarily scheduled when really needed. Increasingly, there are low cost and highly portable sensors that can measure a wide range of physiological values. Can such 'wearable' sensors be used to improve the way that chronic conditions are managed? Patients could have more control over their own care if they wished; doctors and nurses could monitor their patients without the expense and inconvenience of visits, except when they are actually needed. Remote monitoring of patients is already in use for some conditions but there are barriers to its wider use: it relies too much on clinical staff to interpret the sensor readings; patients, confused by the information presented, may become more dependent on health professionals, whose work may be increased rather than reduced.

The project seeks to overcome these barriers by addressing two weaknesses of the current systems. First is their lack of intelligence. Intelligent systems that can help medical staff in making decisions already exist and can be used for diagnosis, prognosis and advice on treatments. One especially important form of these systems uses belief or Bayesian networks, which show how the relevant factors are related and allow beliefs, such as the presence of a medical condition, to be updated from the available evidence. However, these intelligent systems do not yet work easily with data coming from sensors. The second weakness is any mismatch between the design of the technical system and the way the people - patients and professional - interact. We will work on these two weaknesses together: patients and medical staff will be involved from the start, enabling us to understand what information is needed by each player and how to use the intelligent reasoning to provide it. The medical work will be centred on three case studies, looking at the management of rheumatoid arthritis, diabetes in pregnancy and atrial fibrillation (irregular heartbeat). These have been chosen both because they are important chronic diseases and because they are investigated by significant research groups in our Medical School, who are partners in the project. This makes them ideal test beds for the technical developments needed to realise our vision and allow patients more autonomy in practice.

To advance the technology, we will design ways to create belief networks for the different intelligent reasoning tasks, derived from an overall model of medical knowledge relevant to the diseases being managed. Then we will investigate how to run the necessary algorithms on the small computers attached to the sensors that gather the data as well as on the systems used by the healthcare team. Finally, we will use the case studies to learn how the technical systems can integrate smoothly into the interactions between patients and health professionals, ensuring that information presented to patients is understandable, useful and reduces demands on the care system while at the same time providing the clinical team with the information they need to ensure that patients are safe.

If successful, our results will be useful not only for the examples of chronic diseases studied on the project but also for managing other chronic medical conditions, when the same techniques can be applied. Although the project will produce prototype systems, several stages of product development and clinical trials will be needed before real systems are available for patients; we will prepare for these and make a first evaluation of the economic benefits of the proposed systems during the project. Also, several technology companies are involved in the project's Advisory Board to help ensure effective commercial exploitation in the long run.

Technology has the potential both to improve healthcare and reduce its cost, to the benefit of both patients and healthcare professionals. This project aims to develop a new generation of intelligent decision-support systems to work alongside the latest portable sensors so that patients can manage chronic medical conditions with efficient support from healthcare professionals. Achieving these benefits clearly relies on many strands of research (e.g. sensors, communications), but the contribution of this project will allow digital health technology developers to design systems that place less reliance on continuous supervision by health professionals and allow more patient autonomy.

The project's strategy for impact is: (i) early engagement with clinical stakeholders and patients, leading into clinical evaluation through a sequence of pilots and then trials, in parallel with (ii) progressive engineering refinement of the research prototypes developed within the project, in partnership with companies, all supported by (iii) attention to ethical and regulatory requirements and economic viability from an early stage so that these do not become barriers. This strategy will be applied firstly in the context of three medical case studies, in the management of rheumatoid arthritis, diabetes in pregnancy and atrial fibrillation but equally extends to the application of the technical advances to the management of other chronic medical conditions.

Early in the project, we will set-up clinical focus groups to ensure that technology solutions fit the actual problems. Initial engagement with patients and health care professionals will be around the case studies with patient advisory groups formed from existing PPI groups. Later, we will also engage with wider patient groups such as Patient Opinion, UK eHealth Association, Healthwatch and NHS Choices to explore the next stage of the translation of our research into practice. Agile and startup companies have an important role in introducing this disruptive technology, and a group of UK medical technology companies (BeMoreDigital, Mediwise, Rescon, SMART Medical, uMotif) as well as IBM UK and Hasiba Medical GmbH, have committed to advising the project (minimum 2 Advisory Group meetings per year) with a view to exploiting the technology.

We will expand this with advice from our innovation team on IP, licensing and spin-outs. The project includes an initial study of the potential health economic impact in the case studies. To address the traditional 'regulatory barrier' we have made safety a core research objective and will progressively engage key regulators (e.g. the Information Governance Alliance) and the Caldicott Guardian and Digital Health lead for Barts Health, with whom initial contracts have already been made.

In addition to the medical beneficiaries, the project promises major impact in the data sciences with advances in methods for building and running Bayesian network (BN) models that combine expert judgment and data. Although many researchers and system developers already use BNs for decision-support and probabilistic analysis, more widespread use is limited by constraints on the size and complexity of models. The project will deliver a framework for integrating models at different levels of granularity, providing a practical way to build more complex models efficiently. Since current state-of-the-art BN technology has extremely limited support for the modelling concepts proposed the research will be of benefit to those applying BNs in other applications. The project will deliver open source code so that academic researchers and users can use the new techniques in their own work without restriction.