Reducing Treatment Burden Among People With Chronic Conditions Using Machine Learning: Viewpoint

Area of Science:

• Health Informatics
• Chronic Disease Management
• Digital Health

Background:

• The COVID-19 pandemic highlighted healthcare system challenges, particularly for individuals with chronic conditions.
• Digital health technologies (eHealth) offer potential solutions for improving care quality and self-management.
• Existing eHealth models lack comprehensive frameworks for implementation.

Purpose of the Study:

• To define treatment burden and burnout risk in chronic conditions.
• To assess the utility of an eHealth-enhanced Chronic Care Model for prioritizing digital health solutions.
• To introduce a machine learning model designed to mitigate treatment burden and burnout.

Main Methods:

• Literature review and conceptual framework development.
• Definition and analysis of treatment burden and burnout.
• Description of an emerging machine learning model for chronic care.

Main Results:

• Treatment burden and burnout are significant challenges in chronic condition management.
• An eHealth-enhanced Chronic Care Model can guide the selection of digital health interventions.
• Machine learning models show promise in reducing patient treatment burden.

Conclusions:

• eHealth, particularly machine learning, presents a disruptive opportunity to support chronic condition management.
• Addressing treatment burden and burnout is crucial for improving patient outcomes.
• Further development and implementation of eHealth solutions are warranted.