Ambient AI Tool Adoption in US Hospitals and Associated Factors

OBJECTIVES: To estimate the prevalence of ambient artificial intelligence (AI) documentation tool adoption among US hospitals using Epic electronic health record (EHR) systems and to identify hospital characteristics associated with adoption.​ Study Design: Cross-sectional observational study of US hospitals using Epic.​ Methods: Among a national sample of US hospitals using Epic, we assessed ambient AI adoption using Epic Showroom (June 2025) to identify eligible ambient applications and health systems that had implemented or were implementing these applications. We linked adoption data to hospital characteristics from the American Hospital Association Annual Survey (2012-2023; most recent response per hospital) and estimated multivariable logistic regression models with robust SEs clustered at the domain level, reporting adjusted predicted probabilities (margins).​ Results: Among 6561 US hospitals, 2784 (42.4%) were Epic users. Among Epic hospitals, 62.6% adopted ambient AI. In adjusted analyses, adoption was higher across workload quartiles (61.7% in quartile [Q] 1 vs 73.1% in Q4; P = .003) and among hospitals in the top operating margin quartiles (58.0% in Q1 vs 67.6% in Q4; P = .001 vs Q1). Adoption was higher among metropolitan hospitals (64.7% vs 54.3% in nonmetropolitan hospitals; P = .012) and nonprofit hospitals (70.2% vs 28.8% in for-profit hospitals; P < .001).​ Conclusions: Ambient AI documentation tools were widely adopted among US hospitals using Epic EHR systems, with adoption associated with workload, financial performance, ownership, and select structural characteristics. These patterns suggest potential for uneven diffusion across hospitals and underscore the need for research on impacts on clinician outcomes, care quality, and equity.