Adoption of Machine Learning in US Hospital Electronic Health Record Systems: Retrospective Observational Study

Background: While machine learning (ML) technologies have shifted from development to real-world deployment over the past decade, U.S. healthcare providers and hospital administrators have increasingly embraced ML, particularly through its integration with electronic health record (EHR) systems. This evolving landscape underscores the need for empirical evidence on ML adoption and its determinants; however, the relationship between hospital characteristics and ML integration within EHR systems remains insufficiently explored. Objective: To examine the current state of ML adoption within EHR systems across U.S. general acute care hospitals and to identify hospital characteristics associated with ML implementation. Methods: We used linked data between the 2022-2023 American Hospital Association (AHA) Annual Survey and the 2023-2024 AHA Information Technology Supplement Survey. The sample includes 2,562 general and acute care hospitals in the U.S. with a total of 4,055 observations over two years. Applying inverse probability weighting to address non-response bias, we used descriptive statistics to assess ML adoption patterns and multivariate logistic regression models to identify hospital characteristics associated with ML adoption. Results: Overall, about 75% of hospitals had adopted ML functions within their EHR systems in 2023-2024, and the majority tend to adopt both clinical and operational ML functions simultaneously. The most commonly adopted individual functions were predicting inpatient risks and outpatient follow-up. ML model evaluation practices, while still limited overall, showed notable improvement. Multivariate regression estimates indicate that hospitals were more likely to adopt any ML if they were not-for-profit (4.4 percentage-points; 95% CI [0.6, 8.2]; P=.02), large hospitals (15 percentage-points; 95% CI [9.4, 21]; P<.001), operated in metropolitan areas (4.3 percentage-points; 95% CI [0.8, 7.8]; P=.02), contracted with leading EHR vendors (20.6 percentage-points; 95% CI [17.1, 24]; P<.001), and affiliated with a health system (26.8 percentage-points; 95% CI [22.4, 31.3]; P<.001). Similar patterns were observed for predicting the adoption of both clinical and operative ML. We also identified specific hospital characteristics associated with the adoption of individual ML functions. Conclusions: ML adoption in hospitals is influenced by organizational resources and strategic priorities, raising concerns about potential digital inequities. Limited quality control and evaluation practices highlight the need for stronger regulatory oversight and targeted support for under-resourced hospitals. As the integration of ML into EHR systems expands, disparities in both adoption and oversight become increasingly critical. To ensure equitable, safe, and effective implementation of ML technologies in healthcare, well-designed policies must address these gaps and promote inclusive innovation across all hospital settings.