COVID-19 National Notification & Digital Tracing Service
Supporting test results delivery for people in Scotland.
Lenus Health and NHSX
Pioneering patient predictions.
The ask
Chronic obstructive pulmonary disease (COPD) is a group of lung conditions that cause breathing difficulties. It affects 1.2 million people in the UK with a yearly £1.9 billion cost to the NHS.
Our ongoing collaboration with Lenus Health and the NHS is looking to help patients with COPD. A new clinical study is using machine learning to identify patients at highest risk of adverse events related to COPD. The study builds on the Lenus Health COPD app, which helps patients manage their condition. Users of the app have been given the option to take part in the study.
1 in 8
Number of emergency hospital admissions that are related to COPD symptom flare ups
49%
Reduction in admissions during clinical trial
4.74
Annual bed days saved per patient
"This is the first time we’re bringing predictive AI insight for COPD into live clinical practice."
What we did
Lenus has pioneered machine learning models that can proactively identify high-risk patients and provide explainable, actionable insights. Moving from a reactive model of care to a preventative one could have hugely positive impacts. Both on patient outcomes and emergency care requirements.
Lenus has trained the algorithms used on a massive dataset of historical health records, whilst ensuring any pre-existing biases in the data are accounted for. This ensures fairness across age groups, gender, deprivation, and ethnicity.
It's important to present the outputs and insights from these models to patients and clinicians in a clear and consistent way. The design system we built underpins all Lenus interfaces, including the COPD app. Repeatable, carefully designed components - navigation, forms, charts, data visualisations - have been tested and validated extensively with patients and clinicians. These components are key in aiding understanding of patient health.
To support all this work, we also conducted interviews with users of the COPD app. We wanted to understand how they feel about the use of machine learning and AI to make risk predications about their health.
Results
We were surprised by how much patients already knew and were comfortable about AI and its application. Transparent, human explanations of a service's benefits are more important to patients than the model or method used. As one participant put it, "Any tool that a doctor can use to make better predictions has got to be a good thing."
We anticipate that the data science approach, technology infrastructure and learnings from the clinical study will accelerate the application of AI across other long-term conditions.
