The Future of PAP Adherence with the Use of AI

Published in Respiratory & Sleep on August 14, 2024

The Role of AI in Sleep Medicine

AI holds the key to transforming our current approach to sleep medicine in diagnostics, therapeutics, and workflow practice management. Imagine allowing machine learning to perform all the daily mundane tasks, and only bringing to attention what has true clinical value. The practice of medicine has already seen progression in the use of machine learning in several ways. Telehealth visits are becoming data-driven, allowing providers to address issues promptly. Natural Language Processing (NLP) enables chatbots or virtual assistants to engage with patients, answering questions, providing reminders, and offering support, enhancing patient adherence. AI categorizes patients into phenotypes based on symptoms, genetics, and other factors, guiding personalized treatment approaches and improving outcomes. Additionally, AI assists clinicians in decision-making by suggesting optimal therapy options, dosage adjustments, or alternative treatments, enhancing evidence-based recommendations.

Applications of AI and machine learning are currently being explored for understanding sleep disorders and predicting outcomes. There is also a role for AI in personalizing treatment regimens and evaluating treatment response through remote patient monitoring. AI algorithms analyze patient diagnostic and therapeutic data, including sleep patterns, comorbidities, and adherence history, to create personalized treatment plans.

Challenges in PAP Therapy Adherence

Positive Airway Pressure (PAP) therapy is a highly effective treatment for sleep apnea, yet adherence remains a significant challenge. Recent data from the Centers for Medicare & Medicaid Services (CMS) shows that while initial adherence rates are around 72.6% in the first 90-days, they rapidly decline over time, with overall non-adherence rates between 30-40%.

Several barriers contribute to poor adherence, including interface issues, pressure intolerance, environmental factors, socio-economic challenges, and motivational and behavioral factors. Providers aim to deliver excellent care while minimizing time spent on non-clinical activities, but the current healthcare landscape, marked by high costs, complex processes, and provider shortages, complicates this goal.

Intersection of AI and Sleep Therapy Management

With the use of AI-powered tools, React Health’s partner EnsoData has been able to predict 90-day PAP adherence based on data from the first few days of PAP usage. This allows for early identification of patients at risk for non-adherence and enables targeted and efficient interventions. AI-powered devices monitor PAP usage in real time, detecting issues such as mask leaks and pressure fluctuations, and notifying both patients and providers for prompt troubleshooting. Personalization of therapeutic interventions can take the form of optimization of pressure settings, addressing mask leak, or the addition of other modalities such as positional therapy. AI also analyzes behavioral patterns (e.g., sleep habits, lifestyle) to understand adherence challenges, guiding interventions like motivational messages or tailored education.

React Health Connect’s PAP adherence platform is powered by EnsoData. Within the database, clinicians have dashboards that allow management by exception. There are categories into which patients are sorted and risk stratified based on priority of need. Those at the highest risk are prioritized to the top for prompt identification and attention. These groups can also be sorted based on the type of problem occurring, such as mask leak. This can greatly reduce the clinician’s precious time and energy needed for patient care.

Increasing Operational Efficiencies: Early Identification of At-Risk PAP Patients

Leveraging AI for early identification of at-risk PAP patients not only improves patient care but also enhances operational efficiency within healthcare organizations. Identifying at-risk patients early streamlines administrative tasks, allowing providers to allocate resources efficiently and avoid unnecessary follow-ups with adherent patients. AI alerts notify providers when adherence drops, enabling proactive interventions and ensuring timely patient support. Early identification also allows for better resource allocation, with high-risk patients receiving more attention and low-risk patients requiring fewer resources. Improved adherence rates lead to better patient outcomes, reducing hospital readmissions and associated costs, and contributing to overall healthcare system efficiency.

Future Directions

The future of sleep medicine lies in value-based care models and the use of AI to address various sleep disorders. AI can group patients into phenotypes and offer tailored treatment options. As AI evolves, it will continue to enhance our ability to predict and improve PAP adherence, ultimately leading to better patient outcomes.

AI has the potential to revolutionize PAP therapy by addressing adherence challenges and improving patient care. By embracing these technological advancements, we can look forward to a future where sleep medicine is more efficient, precise, and patient-centered.

This article originated from a presentation given at the 2024 VGM Heartland Conference by Colleen Lance, MD, Chief Medical Officer, React Health.

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