Homecare interventions as a Service model for Obstructive sleep Apnea: Delivering personalised phone call using patient profiling and adherence predictions.

BACKGROUND AND OBJECTIVE: Obstructive Sleep Apnea (OSA) is a sleep disorder that leads to different pathologies like depression and cardiovascular problems. The first-line medical treatment for OSA is Continuous Positive Airway Pressure (CPAP) therapy. However, this therapy has the lowest adherence level when compared to other homecare therapies. Consequently, the main objective of this paper is to increase this adherence level with methods that can be replicated in a large number of patients. METHODS: The Homecare Intervention as a Service model can build, verify, and deliver per-sonalised home care interventions. With the Homecare Intervention as a Service model, we build and provide on-demand personalised interventions according to the patient's needs. The 2 core components of this model are patient clustering and CPAP adherence predictions. To define the patient profiles and predict the adherence level, we apply the K-means and the Logistic Regression algorithm respectively. To support these algorithms, we use the CPAP monitoring data and qualitative data on the patients. RESULTS: We demonstrate that there are 3 patient profiles (non-adherent, attempter, and adherent). We draw a comparison with multiple machine learning algorithms to predict CPAP adherence at 30, 60 and 90 days. In this case, the Logistic Regression gives the best results with a f1-score of 0.84 for30 days, 0.79 for 60 days and 0.76 for 90 days. These newly build profiles were to be used to deliver personalised phone call interventions. The phone call intervention shows an increase in adherence by 1.02 h/night for non-adherent patients and 0.69 h/night for attempter patients. CONCLUSIONS: This is the first study in CPAP therapy that formalises the process of transforming raw data into effective home care interventions that can be delivered directly to the patients. In fact,it is the first time that both patient characterisation and predictions based on data are used to provide personalised patient management for CPAP therapy. Our model is flexible to be extended to new types of interventions and other homecare therapies.

Obstructive Sleep Apnea compliance: verifications and validations of personalized interventions for PAP therapy.

The Positive Airway Pressure (PAP) therapy is the most capable therapy against Obstruction Sleep Apnea (OSA). PAP therapy prevents the narrowing and collapsing of the soft tissues of the upper airway. A patient diagnosed with OSA is expected to use their CPAP machines every night for at least more than 4h for experiencing any clinical improvement. However, for the last two decades, trials were carried out to improve compliance and understand factors impacting compliance, but there were not enough conclusive results. With the advent of big data analytic and real-time monitoring, new opportunities open up to tackle this compliance issue. This paper's significant contribution is a novel framework that blends multiple external verification and validation carried out by different healthcare stakeholders. We provide a systematic verification and validation process to push towards explainable data analytic and automatic learning processes. We also present a complete mHealth solution that includes two mobile applications. The first application is for delivering tailored interventions directly to the patients. The second application is bound to different healthcare stakeholders for the verification and validation process.