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

Abstract

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 underresourced hospitals. As the integration of ML into EHR systems expands, disparities in both adoption and oversight become increasingly critical. To ensure the equitable, safe, and effective implementation of ML technologies in health care, well-designed policies must address these gaps and promote inclusive innovation across all hospital settings.

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