EnsoTherapy

AI-Powered PAP Therapy Adherence Support

Managing PAP adherence for a large and growing patient population poses a significant challenge for DMEs and physicians.

Our therapy adherence prediction technology helps DMEs simplify the process of identifying the patients who can be impacted most by intervention.

Help the Right Patients at the Right Time

EnsoData’s cutting-edge technology predicts 90-day therapy adherence within days of sleep apnea patients starting treatment, equipping DMEs with the expertise to prioritize reaching out to the patients who can be impacted the most by intervention.

  • Eliminate the need for manual sorting through countless patients

  • Reduce time spent on patients who will reach 90-day compliance without intervention

  • Improve compliance by focusing on patients who need support

  • Increase coach-to-patient ratios and ability to manage more patients

Joey Sasvari, Director of Sleep, Aeroflow

“Through EnsoData’s AI-powered technology, our team has already witnessed significant improvements in our ability to promptly deliver personalized support to the patients in greatest need. Though it is still early, we have already seen an uptick in the number of patients meeting their usage requirements within the initial 30 days. We are excited to see the outcomes at the 90-day mark. Compliance holds a deeper significance for us beyond mere numerical targets; it embodies our commitment to fostering improved health outcomes for our patients. EnsoTherapy unquestionably contributes to this mission.”

AI-Powered Priority and Task Compliance Management

EnsoTherapy prediction adherence technology identifies data or therapy issues based on a patient’s PAP machine usage. 

Priority

The assignment of scores to patients to predict the likelihood of the patient achieving 90-day adherence without intervention.

Tasks

The identification of therapy issues based on patient and PAP usage data for timely coaching and intervention.

Patient Populations Supported

  • 0-90 Day PAP

  • 90+ PAP

  • Long-Term Ventilation and Oxygen

Examples of data used to generate tasks include (but are not limited to):

  • Missing data

  • Mask leak

  • Nightly usage trends

  • Apnea Hypopnea Index (AHI)

  • Central Apnea Index (CAI)

  • Compliance status

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Read About Our Product Journey

Our strategic collaboration: EnsoData and React Health Partner to Use Predictive AI to Improve PAP Adherence

Our R&D: Deep Learning to Predict PAP Adherence in Obstructive Sleep Apnea

Chris Fernandez Headshot

Our Sleep Review interview: AI Predicts CPAP Adherence Within Weeks of Treatment

Justin Mortara, EnsoData CEO and President - horizontal

Our HME News in 10 Podcast interview: Justin Mortara on AI-Powered CPAP Therapy