Asthma is a chronic respiratory condition marked by airway inflammation, difficulty breathing and cough, affecting more than 265 million globally. Uncontrolled asthma is expected to cost the US alone $300 billion in the next 20 years.
Asthma is also a major cause of emergency department visits and hospitalizations in children, and many could be prevented.
Asthma affects nearly 300 million people worldwide. It is particularly prevalent among children and is a leading cause of hospitalization in this demographic. Symptoms include coughing, wheezing, and shortness of breath, significantly impacting the quality of life. The management of asthma is highly impactful, not just for individual health, but also for easing the burden on healthcare systems globally. In fact, patients with asthma prefer to have less cough and are willing to accept greater levels of other symptoms to achieve it (ref)
Cough is a primary symptom of asthma, serving as an indicator of exacerbations and a lack of control over the condition. Monitoring cough patterns is, therefore, critical in effective asthma management. Traditional approaches to monitoring asthma symptoms have limitations, often relying on patient self-reporting or infrequent clinical assessments. This is particularly an issue when it comes to cough measurement and evaluation.
The advent of AI-powered cough monitoring tools marks a significant advancement for asthma care. Modern tools offer for the first time ever, continuous, real-time monitoring of cough frequency and patterns, enabling data-driven and timely management of asthma.
Continuous monitoring allows for the provision of real-time, objective cough data. This enables, for the first time, consistent data-informed decisions in asthma management. Objective cough data helps in adjusting therapy regimes, medication dosages, identifying triggers, and understanding the effectiveness of current treatment plans.
Cough monitoring helps identify asthma patients with excessive coughing, which may represent a neuro-phenotype and hence developing treatment for this symptom is important for reducing the burden of disease on patients’ lives and currently represents a major unmet clinical need. (ref)
Patients with asthma often prioritize reducing cough over other symptoms, because the impact on quality of life of reduced coughing is immediate and significant. Cough monitoring helps patients and their providers focusing on managing and minimizing cough in the long term, thus improving the overall quality of life for patients.
AI cough intelligence models, such as those developed by Hyfe, can detect subtle changes in cough patterns. This allows the prediction of asthma attacks before they occur and can be a critical and convenient way to prevent severe exacerbations and hospitalizations.
Continuous cough monitoring helps in identifying triggers, whether environmental or lifestyle-related. Cough intelligence reports highlight things like cough rate and intensity by time of the day, further highlighting triggers and correlations. This sort of knowledge is invaluable for personalizing asthma management strategies for each patient.
AI models are great at establishing baseline cough rates for each patient. This allows for more accurate assessments of asthma control and the effectiveness of treatments. It also functions as an early detection system for any deviation from the baseline.
Early intervention facilitated by AI-assisted cough monitoring can significantly reduce emergency visits and hospitalizations. This not only benefits patients financially but also reduces the overall strain on healthcare systems.
Asthma is a leading cause for missed school or work days. Effective asthma management leads to fewer missed days of work or school and reduced medication use, translating to broader public health benefits.
The routine integration of cough monitoring into asthma management represents a transformative step in addressing this widespread chronic condition. It also aligns with the trend towards digital health solutions, offering significant benefits in patient outcomes and healthcare cost reduction. Strategic investments and collaborations in this area are crucial for realizing its full potential, promising substantial returns both financially and in public health terms.
Hyfe (www.hyfe.ai) builds software that detects coughing via audio analysis on phones, watches, wearables, and other devices with mics. Cough detection is fully automated, using AI algorithms trained on the sounds of coughs of tens of thousands of individuals. Since the system runs fully on-device, no audio data is sent to servers, thereby ensuring full patient privacy. By using Hyfe’s technology to detect and quantify cough, healthcare organizations seeking to roll out or improve CHF remote patient monitoring can improve outcomes and cut costs.
The Lancet - Cough and cough hypersensitivity as treatable traits of asthma, Prof Kefang Lai, MD, Imran Satia, MD, Woo-Jung Song, MD, Prof Gang Wang, MD, Prof Akio Niimi, MD, Philip Pattemore, MD, Prof Anne B Chang, PhD, Prof, Peter G Gibson, MD, Prof Kian Fan Chung, DSc MD
The Korean Journal of Medicine - Cough in Severe Asthma - Hwa Young Lee, Young Jae Lee, Woo-Jung Song