The growing integration of artificial intelligence (AI) and wearable technology in healthcare presents opportunities to reduce health inequities tied to social determinants of health (SDOH). However, low adoption rates—AI (2%) and wearable devices (4%)—raise concerns about their effectiveness in improving clinical quality outcomes (CQOs). This study explores whether AI and wearable devices moderate the impact of SDOH factors (geographic location, insurance status, race/ethnicity) on adverse CQOs.
Using secondary data from the All of Us Research Program (N = 39,632), binary logistic regression assessed AI and wearable usage on adverse CQOs (15%). Findings indicate AI adoption is minimal (2%) and does not significantly reduce rural-urban (p = 0.276) or insurance-based disparities (p = 0.194). Instead, AI usage increases the risk of adverse CQOs for people of color (OR = 2.864, p < 0.001), pointing to potential algorithmic bias or systemic inequities in AI-driven healthcare.
Wearable device usage (4%) had mixed effects. Uninsured users faced a 64% higher risk of adverse CQOs (OR = 1.641, p = 0.034), suggesting wearables are more reactive than preventive. However, Hispanic individuals using wearables saw an 87% reduction in adverse CQOs (OR = 0.126, p = 0.042), indicating population-specific benefits.
These findings highlight the need for equity-driven AI policies to ensure healthcare technologies address rather than reinforce disparities. Future research should focus on strategic AI implementation and policy-driven incentives to enhance digital health equity and clinical quality.
KEYWORDS: Artificial Intelligence, Fitbit, Social Determinants of Health, Clinical Quality Outcomes, Adverse Events, Health Equity, Digital Health, All of Us