How AI Could Up CPAP Adherence: Earn Continuing Education

Learning Objectives: Understand the strengths of artificial intelligence in sleep medicine compared to human intelligence; Describe how AI-powered solutions like EnsoTherapy support sleep coaches in prioritizing and troubleshooting patients more effectively than traditional methods; Explain how AI enhances CPAP pressure personalization beyond existing in-lab titrations and APAP algorithms.

Learning Objectives: Describe how miscommunication—patients using non-medical language and physicians interpreting those terms literally—can lead to misdiagnoses, such as depression, instead of narcolepsy; Identify recommendations for clinicians: ask specific, impact-oriented questions like “What can’t you do because of your sleepiness?” to better understand symptom severity and guide diagnosis; Summarize strategies to enhance narcolepsy diagnosis: utilizing family or caregiver observations, asking them to document or video symptoms, and avoiding assumptions about patient-reported experiences.

Learning Objectives: Recognize why sleep rings are increasingly transitioning from home testing to longitudinal monitoring; Describe the science behind sleep ring sensors; List limitations of consumer sleep rings that are popular but not FDA cleared.

Learning Objectives: Describe how common OSA screeners fail to adequately detect OSA in women by excluding symptoms like insomnia and by inherently weighting gender; Explain that women with OSA often present with symptoms such as headaches, dry mouth, nasal congestion, and fragmented sleep, and are also significantly less likely to be aware of snoring; Identify recommended practices to improve OSA screening in women.

Learning Objectives: Understand that tracking and analyzing quality metrics—such as failed studies—on a monthly or quarterly basis rather than only at reaccreditation time can save time, money, space, staffing, and potentially support business viability; Identify the recommendation to transition from retrospective to prospective performance monitoring; Describe how the plan–do–check–act framework can be applied to sleep lab performance metrics.