A clinical investigation launched today will explore how insights from artificial intelligence (AI) can improve care and prevent emergency hospital admissions for people with chronic obstructive pulmonary disease (COPD).
The study will use machine learning in live point-of-care workflows to identify patients at highest risk of adverse events and support the proactive delivery of guideline-based care.
COPD is a progressive and preventable disease that affects around 1.2 million people in the UK and is the second most common cause of emergency hospital admissions. The annual economic burden of COPD on the NHS is estimated as £1.9 billion with treatment following exacerbations of symptoms accounting for 70 per cent of these costs.
Supported by a £1.2 million NHS Artificial Intelligence in Health and Care Award the “DYNAMIC-AI” clinical investigation is a 12-month feasibility study now underway in COPD patients at NHS Greater Glasgow and Clyde (NHSGGC).
The project between Lenus Health and NHSGGC is the first to operationalise predictive AI in direct patient care of chronic conditions.
“This is an incredibly exciting project. It’s the first time we’re bringing together predictive AI insight for COPD into live clinical practice,” said Dr Chris Carlin, consultant respiratory physician at NHSGGC, who is leading the investigation. “With the ageing population and rising prevalence and complexity of long-term conditions, clinicians are overwhelmed with data that they don’t have the capacity to review.
“We need to deploy assistive technologies to provide us with prioritised insights from patient data. These have the potential to give us back time to focus on patient-clinician human interactions and allows us to optimise preventative management to improve patient outcomes and quality of life rather than continue to firefight with unsustainable reactive unscheduled care.”
Moving from rule-based care to machine learning models
Lenus Health’s team of data scientists and engineers have pioneered the development and training of four machine learning models to proactively identify patients with COPD who are at risk of adverse events and provide actionable insights to improve care-quality.
The proprietary AI algorithms are UKCA marked and were trained using close to one million data points from historical electronic health records from a de-identified cohort of more than 55,000 patients with COPD resident in NHSGGC.
Lenus Health uses more than 80 data points to support the delivery or risk scores, significantly more than in a traditional rule-based system, which are known to cause numerous false alarms, leading to clinicians experiencing alarm fatigue.
“Rule-based systems are static whereas machine learning is much more robust in the context of routine care,” explained Dr Carlin.
Under the study, clinical care teams will be provided with actionable insights from the models to use in multi-disciplinary team (MDT) reviews. By identifying high risk patients, they can be offered pro-active, preventative care to avoid the COPD symptom flare ups that currently cause 1 in 8 emergency hospital admissions.
“Up until now, AI models have been used retrospectively in cohorts in which we can provide predictions looking back. We believe this is the first time there will be AI-derived predictive scores used directly within the day-to-day clinical workflow in COPD care,” said Dr Carlin. “One of the key things we hope this will tell us is which patients are at risk of adverse outcome so we can provide anticipatory care. This will help us transform to a preventative, predictive and proactive care model that improves outcomes for patients and relieves pressures on the care system.”
Addressing equality issues
COPD disproportionately affects deprived populations. It is estimated that the prevalence of COPD in the most deprived 10% of areas in the UK is almost double that of the least deprived 10%.
The project’s pioneering work on fairness provides a meaningful scientific contribution to identifying biases held in health data and ensuring models perform appropriately across age groups, gender, deprivation categories and ethnicity. As a result, it has the potential to improve access to healthcare to people living in socio-economically disadvantaged areas.
“Fairness is important across everything we do in healthcare,” said Dr Carlin. “A significant benefit of bringing in data-driven technology is that it has the potential to address equality and access issues across health and social care delivery.”
The technology has the potential to be quickly scaled and the model can be retrained on new data to predict other long-term conditions such as heart failure or for patients with more than one long-term condition.
Lenus Health CEO, Paul McGinness, said: “This trial is the culmination of many years’ work training and testing models, developing the technical infrastructure on Azure to automate generation of model risk scores, and establishing processes and explainability features with the clinical team to act on the insights provided.
“We are confident that the introduction of clinical decision support based on AI-generated insights is the intervention which can truly transform management of chronic conditions like COPD by enabling prioritised care optimisation and enhanced proactive self-management support.
“We anticipate that the data science approach, technology infrastructure and wider learnings from this exemplar study will accelerate the application of AI across other long-term conditions to help address growing demands on health systems caused by increasing levels of multi-morbidity.”
Digital service model
The AI study builds on a previous collaboration between Lenus Health and NHSGGC, which produced a digital service model for supported self-management of COPD patients.
Patients currently using the digital COPD service at NHSGGC will have the option to consent to take part in the AI study, which has been given ethics and Medicines and Healthcare Products Regulatory Agency (MHRA) approval.
