Using Machine Learning To Reduce Treatment Burden

Recent advances in digital health technologies present opportunities to improve quality of care, self-management, and decision-making support to reduce treatment burden and the risk of chronic condition management burnout. In this paper, the JMIR Biomedical Engineering authors define treatment burden and the related risk of affective burnout; assess how an eHealth enhanced Chronic Care Model (CCM) can help prioritize digital health solutions; and describe an emerging machine learning model as one example aimed to alleviate treatment burden and burnout risk. Machine learning may help mitigate treatment burden and burnout risk by providing self-management and decision-making interventions that guide and support people with chronic conditions.