CONSULT: Collaborative Mobile Decision Support for Managing Multiple Morbidities

Lead Research Organisation: King's College London
Department Name: Informatics


The provision of healthcare to people with long-term conditions is a growing challenge, which is particularly acute for the growing proportion of the UK population that suffers from multiple morbidities.

Research has established that involving patients in the management of their own disease has long-term health benefits. Advances in wireless sensor technology means that it is practical for patients to monitor a wide range of health and wellness data at home, including blood pressure, heart function and glucose levels, without direct supervision by medical personnel. The advent of smart phone technologies, appearing widely throughout the nation's population, enables the exciting possibility of putting state-of-the-art intelligent decision-support systems into the hands of the general public.

However, such sensor data is currently disconnected both from the patient context, provided by the Electronic Health Record, and from the treatment plan, based on current best-evidence guidelines and customised by the patient's GP. In cases of multi-morbidities, there is no clear strategy for combining multiple guidelines into a coherent whole. Furthermore, personalised treatment plans are rigid and do not dynamically adapt to changes in a patient's circumstances. Finally, the record of patient condition and decisions made is not routinely captured in a standardised way, preventing learning from feedback about treatment effectiveness.

To address these problems, CONSULT will combine wireless "wellness" sensors with intelligent software running on mobile devices, to support patient decision making, and thus actively engage patients in managing their healthcare. Our software will use computational argumentation to help patients follow treatment guidelines and will learn details specific to individuals, personalising treatment advice within medically sound limits. Critically, the software will detect conflicts in treatment guidelines that frequently arise in the management of multiple morbidities. The software will provide advice regarding which treatment options to follow, when the conflicts can be resolved by the patient and when a resolution requires an intervention from a clinician. The software will thus help patients handle routine maintenance of their conditions, while ensuring that medical professionals are consulted when appropriate. This will enable patients to take charge of their own conditions, while being fully supported in both traditional and new ways. By routinely capturing the data provenance of the recommendations made, actions taken and the resulting patient progress, the software will provide valuable insights into the effectiveness of treatments and underlying guidelines in multi-morbidity scenarios.

The technology will be evaluated across multiple dimensions in a proof-of-concept study, engaging stroke patients, their carers and medical professionals, while capitalising on King's College London's world-leading position in stroke research and its established patient groups, particularly those connected to the South London Stroke Register programme.

Helping patients to govern their own care will reduce the demands made on medical professionals, while reaping the health benefits of self-management. Integrating live information from monitoring devices will make it possible to distinguish between situations that need attention from medical professionals, and those that do not, reducing the number of extra appointments that patients and doctors need to schedule. Using live information will also make it possible to detect changes in the course of a disease, allowing pre-emptive actions to be taken, and thus reducing the amount of time that someone suffering from a long-term condition may have to spend in hospital. Overall, our approach will not only provide more efficient care, but also allow care to be better tailored to the needs of each individual.

Planned Impact

Chronic health conditions are widespread in the UK. NHS England estimates that around 15 million people in England (30% of the population) suffer from chronic health conditions. In Scotland and Wales, the proportion of the population affected is even higher. Such conditions require constant management, and they account for 50% of all GP appointments and 64% of all outpatient appointments. With careful monitoring, it is possible for those who are chronically ill to lead high-functioning lives and to have their care managed at home. However, many chronic conditions can easily lead to hospital stays, with the result that 70% of all inpatient bed days are required to treat the chronically ill. The prevalence of chronic conditions is closely correlated with age. For example, 14% of those younger than 40 report a long-term condition, compared with 58% of those over 60. With an ageing population nationwide, dealing with chronic conditions will consume even more resources in the future.

The number of people with more than one chronic disease is also growing. It is predicted that there will be 2.9 million people with two or more long-term conditions by 2018, an increase of one million since 2008. Such multiple morbidities are difficult to navigate because traditionally each disease has been managed separately, so drug regimes and treatment plans are developed in isolation and may conflict with each other. This growth in multiple morbidities presents a further challenge to our healthcare system: by 2018, dealing with them will cost GBP 5 billion more than in 2011.

A key feature of our proposed CONSULT approach is that it goes far beyond what is possible with medical advice web sites. By adopting a collaborative approach based on integrating wellness sensor data with a patient's electronic health record (EHR), it is possible to provide personalised care in home settings, reducing the amount of hospital and GP time required, and improving treatment outcomes. By specifically targeting the issues that arise in handling multiple morbidities, our approach aims to help the most vulnerable members of the chronically ill population.

Specifically, our research will impact several categories of stakeholders:

* Patients will be assisted in the management of their conditions. Our work will literally put up-to-date information and support at their fingertips. Our goal is to help patients sort through what is relevant to them, understand their options, avoid information overload and make the best decisions, even when treatment guidelines conflict. By engaging patients in this way, we aim to help them obtain the health benefits that have been shown for the chronically ill who self-manage their treatment. Overall, our approach offers both economic benefits, reducing the cost of long-term care, and social benefits in terms of increased quality of life.

* Carers will be empowered in the management of patients with chronic conditions. Our work will provide live support and connection to their patients, and the added security of knowing that medical professionals will be informed of any relevant changes in patients' conditions.

* Clinicians' efforts will be better apportioned. Since wellness sensor data will be integrated into the EHR, clinicians will be able to monitor patients' conditions without bringing them into clinic, and receive alarms when there are situations that require immediate attention.

In addition, medical researchers will be able to obtain integrated data from sensors and EHRs to conduct observational studies on efficiency of treatments and accuracy of measurements in the home setting. The provenance of data collected, backing decision support, will enable commissioning bodies to gain unique insight into the efficiency and cost-effectiveness of treatments. Technology will also impact commercial decision-support providers looking to deliver collaborative home care solutions.
Description Collaboration with National Institute for Health and Care Excellence on use of provenance for managing recommendations and evidence 
Organisation National Institute for Health and Care Excellence (NICE)
Country United Kingdom 
Sector Public 
PI Contribution My team is applying the Provenance Template modeling method to the challenges that NICE has in managing their research metadata. Specifically, they are interested in exploring the versioning of their guideline recommendations, and its relationship to the changing evidence base.
Collaborator Contribution NICE is conducting a survey of their stakeholders (industrial partners, clinical organizations etc.) to understand their needs with respect to data provenance of NICE guidelines. This is a valuable piece of work that they are uniquely positioned to deliver, and will be of significant use to my team in developing the provenance research portfolio further. As a secondary benefit, the NICE employee placed in my group is advising on the ROAD2H and CONSULT projects which both have elements of guideline modeling, to ensure its applicability to the UK national requirements.
Impact We have jointly obtained the "Towards Computable Guidelines", MRC Industry Proximity Award funding worth £30K, to establish a pilot collaboration through hosting a NICE employee in my group, and are currently working on a paper and a larger grant proposal. This is a multi-disciplinary collaboration that spans informatics and public health.
Start Year 2017
Title Nokia Health Middleware 
Description Middleware designed to improve interactions with the Nokia Health API, and thus Nokia Health devices. 
Type Of Technology Software 
Year Produced 2018 
Open Source License? Yes  
Impact None as yet. 
Description Presentation to the Clinical Research Network South London, Stroke Specialty Group 
Form Of Engagement Activity A talk or presentation
Part Of Official Scheme? No
Geographic Reach Regional
Primary Audience Professional Practitioners
Results and Impact The CRN South London Stroke Specialty Group brings together researches and medical professionals engaged in research around stroke. At the meeting on 10th January 2018, Dr Talya Porat gave a presentation to the group about the aims of the CONSULT project.
Year(s) Of Engagement Activity 2018
Description Talk to the Petnica Science Camp students in Serbia 
Form Of Engagement Activity A talk or presentation
Part Of Official Scheme? No
Geographic Reach International
Primary Audience Schools
Results and Impact Presentation on data provenance and the Learning Health System given to 120-odd high school students attending the Petnica Science Camp in Serbia. The talk coincided with the summer term at PSC, and the students present there were all involved in their own research projects. The presentation kick-started a longer discussion on the role of data provenance in alleviating privacy fears around how people's private medical data are being used.
Year(s) Of Engagement Activity 2017