Hospital Artificial Intelligence/Machine Learning Adoption by Neighborhood Deprivation

作者：Jie Chen, Alice Shijia Yan · 发表于：Medical Care · 年份：2025 · DOI：10.1097/mlr.0000000000002110 · 被引用次数：18 · 研究领域：Artificial Intelligence in Healthcare and Education、Ethics and Social Impacts of AI、COVID-19 Digital Contact Tracing

OBJECTIVE: To understand the variation in artificial intelligence/machine learning (AI/ML) adoption across different hospital characteristics and explore how AI/ML is utilized, particularly in relation to neighborhood deprivation. BACKGROUND: 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. METHODS: We used linked datasets from the 2022 American Hospital Association Annual Survey and the 2023 American Hospital Association Information Technology Supplement. The data were further linked to the 2022 Area Deprivation Index (ADI) for each hospital's service area. State fixed-effect regressions were employed. A decomposition model was also used to quantify predictors of AI/ML implementation, comparing hospitals in higher versus lower ADI areas. RESULTS: Hospitals serving the most vulnerable areas (ADI Q4) were significantly less likely to apply ML or other predictive models (coef = -0.10, P = 0.01) and provided fewer AI/ML-related workforce applications (coef = -0.40, P = 0.01), compared with those in the least vulnerable areas. Decomposition results showed that our model specifications explained 79% of the variation in AI/ML adoption between hospitals in ADI Q4 versus ADI Q1-Q3. In addition, Accountable Care Organization affiliation accounted for 12%-25% of differences in AI/ML utilization across various measures. CONCLUSIONS: The underuse of AI/ML in economically disadvantag...