AI-assisted hierarchical primary care and health equity among older adults with chronic diseases: a matched observational mixed-methods study in China.

Background: Population aging and the growing burden of chronic diseases have increased the need for more accessible and equitable primary healthcare. Artificial intelligence (AI)-assisted hierarchical diagnosis and treatment has been increasingly introduced into primary care to support patient guidance, triage, follow-up, referral coordination, and risk warning. However, evidence remains limited on whether AI-enabled primary care is associated with more equitable chronic disease management among older adults. Objective: This study examined the associations between AI-enabled primary care within China's hierarchical medical system and health outcomes among older adults with chronic diseases. It further assessed whether these associations differed across income, education, and digital literacy groups, and whether health information acquisition and medical service accessibility were plausible pathways linking AI-enabled care to patient outcomes. Methods: A multicenter, institution-based, matched observational mixed-methods study was conducted in Guangdong Province, China. The analytic sample included 420 older adults with chronic diseases, including 210 patients recruited from AI-enabled primary healthcare institutions and 210 from matched comparison institutions. Multivariable regression models, subgroup analyses, interaction tests, exploratory pathway analyses, and robustness checks were used to examine associations between AI exposure and chronic disease control, self-rated health, and hospitalization-related outcomes. Semi-structured interviews with 48 medical staff and institutional managers were analyzed to contextualize the quantitative findings and to better understand how AI-enabled services were implemented in routine primary care. Results: Compared with patients in comparison institutions, patients in AI-enabled institutions showed a higher chronic disease control rate, better self-rated health, and lower hospitalization risk. Adjusted regression results indicated that institutional AI access was positively associated with chronic disease control (OR = 1.69, 95% CI: 1.12-2.56) and self-rated health (β = 0.24, 95% CI: 0.08-0.39), and negatively associated with hospitalization (OR = 0.63, 95% CI: 0.39-0.99). Subgroup analyses suggested that these associations were more evident among low-income and low-education patients, whereas digital literacy remained an important boundary condition. Exploratory pathway analyses showed that health information acquisition and medical service accessibility partly explained the observed associations between AI-enabled care and patient outcomes. Qualitative findings further suggested that AI-supported routine follow-up and patient guidance, but that older adults with low digital literacy often required staff-mediated assistance to benefit from AI-enabled services. Conclusion: AI-enabled primary care within China's hierarchical medical system was associated with better chronic disease management outcomes among older adults in this matched observational study. The findings suggest that AI-enabled primary care may have equity-relevant associations when accompanied by better health information acquisition, service accessibility, follow-up, and referral coordination. These associations should not be interpreted as causal relationships. Their potential equity relevance depends on whether AI tools are integrated into primary care workflows and supported by inclusive, human-assisted service models for older adults with limited digital literacy.