AI-Assisted Clinical Decision Support in Nursing: A Narrative Review of Implementation Outcomes and Frontline Adoption Barriers

Clinical practice has transformed due to a surge in the development of artificial intelligence tools to support decision-making. AI-Assisted Clinical Decision Support (AI-CDS) is one of the most promising technological tools in improving care outcomes. AI-CDS tools are digital systems relying on artificial intelligence (AI) to assist healthcare professionals in making clinical decisions. There exists a research gap in two aspects of the phenomenon: AI-CDS implementation outcomes and frontline adoption barriers. This narrative review assessed and synthesized evidence on the implementation outcomes reported for AI-CDS tools used by nurses as the primary clinical users and identified the main barriers to frontline adoption of AI-CDS from the perspective of nurses. The review found that AI-CDS tools can boost nurse confidence and improve workflow efficiency, patient safety, and care outcomes, but some barriers undermine their potential. The barriers include workflow integration and cognitive burden, trust issues, organizational support and training, technical capacity, and data management. These barriers are associated with ongoing frontline personnel resistance or with circumvention of tool adoption, with poorly characterized outcomes. They also affect the healthcare system and health outcomes. However, there are intervention strategies and recommended guidelines that can improve adoption and implementation rates. The review highlights that AI-CDS should be nurse-centered, supported by a robust governance framework, and locally validated to help institutions realize the full benefits of these tools while ensuring patient safety. The review prioritizes the input of frontline personnel, policymakers, and researchers in improving the benefits of AI-CDS tools.