How AI could help revolutionize NHS respiratory disease care

Dr. Amira Patel, CEO of Tidalsense, explains how AI can completely transform diagnostics in respiratory care.

Respiratory disease affects one in five people. Already the third biggest cause of death in the UK, the number of people affected by these conditions is increasing. The latest figures from the NHS show that hospital admissions for respiratory illnesses are very close to pre-pandemic levels. Furthermore, analysis by Asthma and Lung UK also shows a direct link between admissions and deprivation due to factors such as air pollution, humidity and increased risk of mould.

Against a backdrop of NHS pressures – most notably, increasing numbers of patients with long-term health conditions, and widespread staff shortages – diagnosis of respiratory diseases is not keeping pace with the increasing prevalence of respiratory conditions.

If we take chronic obstructive pulmonary disease (COPD) as an example, around two thirds of people with the disease in the UK are undiagnosed, with a third only being recognized after hospitalisation, when it Chances are that his disease is already quite advanced. And their symptoms are severe. This goes a long way to explain why the UK has the highest death rate from lung disease in Europe, second only to Turkey.

For lung conditions, it is important to start treatment early. Effective treatment for asthma and COPD reduces symptoms and severity, reducing health care visits – including emergency hospital admissions. But the current diagnostic pathway for respiratory conditions is ineffective, inefficient and costly – many patients are misdiagnosed and not quickly escalated to appropriate treatment. COPD alone is the second most common reason for an emergency hospital admission and total admissions for COPD are estimated to cost the NHS £491 million annually.

It contributes significantly to the financial burden of the NHS – all lung conditions (including lung cancer) cost the healthcare £11 billion annually. COPD and asthma, the two biggest chronic respiratory conditions affecting one in five people in England, cost the NHS around £5 billion each year.

rule out misdiagnosis

Prompt and accurate diagnosis is critical to reducing the growing pressure on our healthcare service, eliminating unnecessary patient appointments while enabling earlier interventions for those who most urgently need them.

But existing diagnostic methods present a significant obstacle to this goal. For example, the current test for COPD and asthma is spirometry, an early-Victorian technique that can be unpleasant for patients and requires specialist training to administer. This 180-year-old approach is not only complex to perform, but also dependent on patient technique. What’s more, interpreting abnormal results can be challenging, meaning misdiagnosis is rife.

Access to spirometry tests is patchy at best and clinical trials have stopped entirely during the pandemic. Conservative estimates estimate that there are currently approximately 27,000–34,000 people waiting for a clinical trial.

Integrating new technologies – such as AI – is essential to catching up with the backlog and opening up the potential for accurate, faster diagnosis.

Therefore, it is perhaps not surprising that the NHS Long Term Plan prioritizes access to accurate early diagnosis and testing for chronic respiratory diseases as a way of creating efficiencies for the NHS and improving the quality of treatment and care for patients. gives.

more than the human eye can see

Thanks to its ability to analyze and understand vast amounts of clinical information, AI has enormous potential to pave the way for highly accurate diagnosis. AI-based technology is already being applied to everything from stroke detection to retinal screening, using trained algorithms and deep learning to quickly detect signs of disease that clinicians can cannot be seen.

There have already been successful demonstrations of identifying respiratory conditions using existing clinical data. For example, AI has been applied to aid in the diagnosis of lung cancer and pulmonary fibrosis by helping physicians identify at-risk patients, speed up decision making, and reduce unnecessary procedures.

If applied to respiratory diagnosis, AI could mean that patients with chronic respiratory diseases would be spared the hassle of spending weeks or months hopping between clinicians to secure a diagnosis, instead correcting them. Access to the right treatment, medicine and dosage will be provided on time. , Better disease management can also deliver significant savings to the NHS.

Going Beyond Diagnostics

AI-led technologies are also opening up powerful predictive and predictive capabilities. For example, these techniques can be used to predict a patient’s future disease development, helping guide clinical decisions and opening up access to early medical or lifestyle interventions. AI can also be used to predict those within a population who are at risk of developing chronic respiratory disease, ensuring they are prioritized for diagnosis or screening programmes.

Additionally, AI has great potential to improve the patient experience – empowering patients to self-monitor and manage their condition outside of the healthcare environment, resulting in improved quality of life and health for the patient. There are more capacities to serve.

In Greater Glasgow and Clyde, 500 COPD patients are being monitored at home to enable earlier interventions as well as reduce pressure on the NHS. The plan links patient records with real-time data from fitness trackers and at-home breathalyzer devices, and users can message doctors with any health concerns directly through a smartphone app. A new trial later this year will also apply AI to this data to quickly identify patients who may be experiencing more severe symptoms. Initial results are positive, suggesting the scheme has already reduced hospital admissions by more than half.

save time, save life

As the number of patients with chronic respiratory conditions continues to rise, it will be impossible for the NHS to meet its objectives of improving the quality of life and health outcomes of people with respiratory disease unless the barrier to earlier diagnosis is removed.

Technology will be critical in bridging the gap between patient demand and clinical supply, with AI enabling faster, more accurate diagnosis and access to diagnosis outside the traditional clinical setting. The enhanced capabilities of digital technologies are paving the way for more effective treatment plans, reducing the likelihood of frequent hospitalizations, and generally contributing to a better quality of life.