Artificial Intelligence Adoption, Organizational Readiness, and Hospital Quality Trajectories: Secondary Quantitative Evidence from the United States Hospitals

Abstract

Within United States hospitals, artificial intelligence has progressed beyond experimental clinical use and is increasingly being incorporated into routine practice; however, the evidence base remains dispersed across clinician acceptance, organizational adoption, and hospital performance. The study sought to determine the relationship between organizational readiness and hospital artificial intelligence adoption, examine how adoption is associated with operating or quality outcomes, and test whether perceived risk serves as a clinician-level mechanism connecting expectations about artificial intelligence with intended use. Evidence was synthesized quantitatively from verified United States studies published through August 2026 that reported hospital-level survey, administrative, financial, or quality data, or clinician-level structural equation estimates. The analytical model combined the technology–organization–environment perspective and the unified theory of acceptance and use of technology. The synthesis covered three complementary quantitative approaches: clinician mediation through structural equation modeling, hospital quality trajectories through longitudinal mixed-effects modeling, and organizational adoption or performance through weighted or endogeneity-aware models. Across national hospital studies, higher adoption was repeatedly associated with scale, health-system membership, electronic health record infrastructure, interoperability, and telehealth capacity. In one weighted national analysis, machine-learning adoption was 26.8 percentage points higher for health-system members and 20.6 points higher for hospitals using a leading electronic health record vendor. Several operational and financial indicators showed favorable performance associations, whereas hospital quality trajectories varied and were not consistently beneficial. At the clinician level, intended use declined as perceived risk increased, and perceived risk significantly mediated the effects of expectancy and general artificial-intelligence perception. Overall, the results frame artificial intelligence not as a stand-alone technology but as an organizational capability situated within digital infrastructure, managerial processes, and human acceptance. Accordingly, managerial and policy responses should combine adoption incentives with interoperability, evaluation governance, regional capacity building, and clinician-centered risk management, while refraining from causal interpretations beyond what the observational evidence supports.