Improving asthma care through personalised risk assessment and support from a conversational agent

Lead Research Organisation: Imperial College London
Department Name: Design Engineering (Dyson School)


Over 5.4 million people have asthma in the UK, and despite £1Billion a year in NHS spending on asthma treatment, the national mortality rate is the highest in Europe. One of the reasons for this statistic, is that risk is often dramatically underestimated by many with asthma. This leads to neglect of early care, poor control, and eventually, hospitalisation. Therefore, improving accurate risk assessment and reduction via relevant behaviour change among people with asthma could save lives and dramatically reduce health care costs.

Our multi-sector and international team will aim to address this early-care gap by investigating a new type of low-cost, and scalable personalised risk assessment, combined with follow-up automated support for risk reduction. The technology will leverage artificial intelligence to calculate a personalised asthma risk score based on voice features and self-reported data. It will then provide personalised advice on actions that can be taken to lower risk followed by customised conversational guidance to support the process of healthy change.

We envision our work will ultimately lead to a safe and engaging system where the patients are able to see their current risk of an asthma attack after answering a series of questions, akin to clinical history taking, and record their voice. They then get ongoing customised support from an automated coach on how to reduce that risk. Any progress they make will visibly lower their risk (presented, for example, as "Strengthening their shield"), in order to make their state of asthma control more tangible and motivating.

The technology will be developed collaboratively with direct involvement from people with asthma and clinicians through co-design methods and regular feedback in order to ensure risk assessment, feedback and guidance are clinically sound, and delivered in a way that is autonomy-supportive, clear, useful, and engaging to patients.

Similar risk assessment approaches have already proven successful for improving cardiovascular and mental health, but this will be the first time personalised risk assessment is applied to asthma and integrated with support from a conversational agent.

The risk calculation and feedback will involve three novel approaches:
1) A data-driven model of asthma risk drawing on routinely collected de-identified Electronic Health Record data which will be used to identify which factors most accurately predict asthma exacerbation.
2) Machine learning techniques for detecting asthma risk from voice features (eg, wheezing, breath rate, coughing).
3) Natural Language Processing techniques for developing an autonomy-supportive conversational agent to support health behaviour change.
The project will be based at Imperial College London with clinicians and researchers from organisations in the UK, the US, and Australia. The project will also be undertaken in partnership with YourMD Ltd which will facilitate running a pilot study within their commercial app which will provide access to sufficient data for proof-of-concept testing. This will allow the algorithms to use dialogue and voice data from a larger number of participants, and will also accelerate translation for future project phases.


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Description In the first 12 months of the project, we have developed basic statistical risk models of asthma exacerbations based on a larger (1.2M) medical records and on a set of voice recordings. We have also developed a conversational system based on a micro service architecture, with WhatsApp and Web interfaces. We involved nurses and health professionals and are getting ready to start a feasibility study with patient.
So far the project is going according to plans.
The implementation stage, where results are taken by the NHS or healthcare companies will start in the next phase of the project.
Exploitation Route Approaches to commercialisation will be developed over the second year of the project. A follow up project to measure the efficacy of the intervention, using a Randomised Control Trial is planned
Sectors Healthcare