Open AccessReducing Treatment Burden Among People With Chronic Conditions Using Machine Learning: Viewpoint
Author(s) -
Harpreet Nagra,
Aradhana Goel,
Dan Goldner
Publication year - 2022
Publication title -
jmir biomedical engineering
Language(s) - English
Resource type - Journals
ISSN - 2561-3278
DOI - 10.2196/29499
Subject(s) - ehealth , burnout , digital health , telehealth , telemedicine , health care , risk analysis (engineering) , medicine , nursing , psychology , computer science , clinical psychology , political science , law
The COVID-19 pandemic has illuminated multiple challenges within the health care system and is unique to those living with chronic conditions. Recent advances in digital health technologies (eHealth) 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. There are limited available eHealth models that can adequately describe how this can be carried out. In this paper, we define treatment burden and the related risk of affective burnout; assess how an eHealth enhanced Chronic Care Model can help prioritize digital health solutions; and describe an emerging machine learning model as one example aimed to alleviate treatment burden and burnout risk. We propose that eHealth-driven machine learning models can be a disruptive change to optimally support persons living with chronic conditions.