Keywords: Patient feedback, data analytics, patient-centric treatment, intervention repositories, treatment efficiency design, Obstructive Sleep Apnea (OSA), Continuous Positive Airway Pressure (CPAP), CPAP Adherence, Personalized Care.
Abstract: This study presents a groundbreaking strategy for the homecare management of Obstructive Sleep Apnea (OSA). With an emphasis on patient empowerment through the integration of feedback management systems into sleep treatment, this study offers an innovative approach to the homecare management of obstructive sleep apnea (OSA). Fundamentally the research attempts to significantly increase, via tailored interventions, adherence to Continuous Positive Airway Pressure (CPAP) therapy. It accomplishes this by combining qualitative patient feedback with quantitative CPAP machine monitoring data to improve patient clustering and in turn treatment outcomes. The research methodology is comprehensive, encompassing various stages that include advanced patient grouping, continuous incorporation of new patient data, integration of feedback from surveys on both intervention and medical sleep, and a thorough cycle of interventions and evaluations. This iterative refinement process is essential as it allows for the dynamic updating of patient profiles and clustering based on evolving data and treatment responses. All these efforts are focused on fostering a more tailored approach to patient care. The creation of patient-centered treatment plans that maximize treatment efficacy by utilizing intervention repositories and data analytics is at the heart of this research. The study also explores how personalized care can improve CPAP adherence and underscores the need to tailor interventions and content based on patient feedback. This multi-layered approach aims to improve adherence and create a more efficient patient-centered care model for individuals with OSA by continuously adapting and personalizing the care provided to each patient, thereby fostering a stronger relationship between patients and their treatments.
Track: 10. Digital health
Registration Id: PGN2ZT4MLVM
Submission Number: 388
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