Hospital Artificial Intelligence/Machine Learning Adoption by Neighborhood Deprivation

Datos Bibliográficos

ID9102489
AutoresJie Chen (0000-0002-9254-4413, Department of Health Policy and Management, School of Public Health, University of Maryland, College Park, MD, autor de correspondencia), Alice Shijia Yan (0009-0002-5299-2649, Department of Health Policy and Management, School of Public Health, University of Maryland, College Park, MD, autor de correspondencia)
Año2025
Volumen63
Número3
Páginas227-233
Fecha de publicación2025-03-01
Peer ReviewedSí
Open AccessSí
TipoARTICLE
RevistaMedical Care (JOURNAL)
Identificadores de la revistaISSN: 0025-7079 • E-ISSN: 1537-1948
EditorialOvid Technologies (Wolters Kluwer Health) (PUBLISHER)
DOI10.1097/mlr.0000000000002110
PMID39947693
OpenAlexW4406438340
IdiomaEN
Referencias citadas19

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 disadvantaged and rural areas, particularly in workforce management and electronic health record implementation, suggests that these communities may not fully benefit from advancements in AI-enabled health care. Our results further indicate that value-based payment models could be strategically used to support AI integration

Disadvantaged · Equity (law) · Health care · Machine learning · Political science · Workforce · Artificial Intelligence · Artificial Intelligence in Healthcare and Education · Computer Science · COVID-19 Digital Contact Tracing · Ethics and Social Impacts of AI · Medicine · Psychology · Gerontology