FROM LAB TO BEDSIDE: A LONGITUDINAL STUDY ON THE REAL-WORLD IMPACT OF AI IN CHRONIC DISEASE MANAGEMENT

Abstract Objective: The increasing prevalence of chronic diseases such as diabetes, hypertension, and chronic obstructive pulmonary disease (COPD) poses significant global health challenges. The management of chronic diseases for patients becomes possible through USBs which provide both remote monitoring systems linked to disease pattern predictions alongside decision-making support. The research focuses on evaluating AI tool performance as it affects chronic disease management in different healthcare settings considering both implementation expenses and scalability possibilities. Materials and Methods: The study combined outcomes from patient health and organizational costs through both data collection and interviews from providers and patients within its longitudinal framework. Three chronic disease programs namely diabetes, hypertension and COPD had their AI tools evaluated across 24 months at healthcare facilities conducting operations in high-income as well as low- to middle-income regions.