ABSTRACT Background Clinical documentation is a major contributor to physician burnout, and artificial intelligence (AI) scribes are increasingly being adopted to help reduce the burden of documentation. These tools automatically generate clinical notes from patient–provider conversations using speech recognition and natural language processing. However, their usability and effectiveness still remain an issue. Aim To synthesise the existing evidence on usability‐related barriers and facilitators influencing the adoption and use of AI scribes for clinical documentation in healthcare settings. Method The scoping review employed the methodology developed by Arksey and O'Malley in 2005 and further expanded by Levac and Colquhoun in 2010. We searched PubMed, Scopus, Ovid MEDLINE, and Web of Science to identify relevant studies published in English between 2015 and 2025. All findings were reported according to PRISMA guidelines for scoping reviews. Results Of 4588 identified records, 14 studies met the inclusion criteria and employed qualitative, quantitative, and mixed‐methods. AI scribes were consistently associated with reduced cognitive load, faster documentation, improved work–life balance, and positive user experience. However, common barriers included frequent errors, excessive note length, limited formatting options, and poor integration with electronic health records (EHR). Editing demands varied by clinician experience, with some finding that time savings were lost when substantial corrections were needed. Overall, usability was rated more favourably in routine or protocol‐driven visits, with mixed outcomes reported on long‐term burnout and workflow impact. Conclusion AI scribes show promise in reducing documentation burden and improving clinical workflow, but important usability challenges remain. Enhancing accuracy, streamlining integration, and allowing greater customization will be essential to support broader adoption and sustained use in clinical practice.
Atiku et al. (Wed,) studied this question.