Hospital AI/ML Adoption by Neighborhood Social Vulnerability

OBJECTIVES: AI/ML-assisted care coordination has the potential to reduce health disparities, but there is a lack of empirical evidence on AI’s impact on health equity. The objective of the study is to understand the variation in AI/ML adoption across different hospital characteristics and explore how AI/ML is utilized, particularly in relation to neighborhood social vulnerability.

METHODS: We used the linked datasets from the 2022 American Hospital Association (AHA) Annual Survey and the 2023 AHA Information Technology Supplement. The data was further linked to the 2022 Area Deprivation Index (ADI) for each hospital’s service area. State-fixed effect regressions were used. A decomposition model was also used to quantify predictors of AI/ML implementation, comparing hospitals in higher vs. lower ADI areas.

RESULTS: Approximately 73% of hospitals utilized AI/ML. Hospitals serving the most vulnerable areas (ADI Q4) were significantly less likely to use AI/ML (coef = -0.08, p<0.05) and provided fewer AI/ML-related workforce applications (coef=-0.37, p=0.01), compared to those in the least vulnerable areas. Decomposition results showed that our model specifications explained 81% of the variation in AI/ML adoption between hospitals in ADI Q4 versus ADI Q1 - Q3. Additionally, Accountable Care Organization affiliation accounted for 16% - 30% of differences in AI/ML utilization across various measures.

CONCLUSIONS: The underuse of AI/ML in rural and economically disadvantaged areas, particularly in workforce management and EHR implementation, suggests that these communities may not fully benefit from advancements in AI-enabled healthcare. Our results further indicate that financial incentives could be strategically used to support AI integration.