โก Quick Summary
A recent scoping review examined the usability-related barriers and facilitators impacting the adoption of AI scribes in healthcare. While these tools show promise in reducing documentation burden and improving workflow, significant usability challenges remain.
๐ Key Details
- ๐ Studies Reviewed: 14 studies met the inclusion criteria from a total of 4588 records.
- ๐งฉ Methodology: Scoping review based on Arksey and O’Malley’s framework.
- โ๏ธ Technologies: AI scribes utilizing speech recognition and natural language processing.
- ๐ Key Findings: Reduced cognitive load, faster documentation, and improved work-life balance.
๐ Key Takeaways
- ๐ Documentation Burden: AI scribes can significantly alleviate the burden of clinical documentation.
- ๐ก User Experience: Positive user experiences reported, especially in routine visits.
- โ ๏ธ Common Barriers: Frequent errors, excessive note length, and poor EHR integration hinder usability.
- ๐ ๏ธ Editing Demands: Time savings can be negated by the need for substantial corrections.
- ๐ Usability Ratings: Generally rated more favorably in protocol-driven visits.
- ๐ Workflow Impact: Mixed outcomes reported regarding long-term burnout and workflow efficiency.
- ๐ Future Improvements: Enhancing accuracy, integration, and customization is essential for broader adoption.
๐ Background
Clinical documentation is a significant contributor to physician burnout, prompting the exploration of AI scribes as a potential solution. These tools aim to automate the generation of clinical notes from patient-provider conversations, leveraging advanced technologies such as speech recognition and natural language processing. Despite their potential, the effectiveness and usability of AI scribes remain critical areas of investigation.
๐๏ธ Study
The scoping review aimed to synthesize existing evidence on the usability-related barriers and facilitators influencing the adoption of AI scribes in healthcare settings. The review followed established methodologies and included studies published in English from 2015 to 2025, ensuring a comprehensive overview of the current landscape.
๐ Results
The findings revealed that AI scribes are associated with reduced cognitive load, faster documentation, and an improved work-life balance. However, challenges such as frequent errors, excessive note length, and poor integration with electronic health records (EHR) were prevalent. Notably, the editing demands varied based on clinician experience, with some users experiencing diminished time savings due to necessary corrections.
๐ Impact and Implications
The implications of this study are significant for the future of clinical documentation. By addressing the usability challenges identified, healthcare organizations can enhance the adoption of AI scribes, ultimately leading to improved clinical workflows and reduced burnout among healthcare providers. The potential for AI scribes to transform documentation practices is immense, paving the way for a more efficient healthcare system.
๐ฎ Conclusion
This scoping review highlights the promise of AI scribes in alleviating documentation burdens while also underscoring the need for improvements in usability. By focusing on enhancing accuracy, integration, and customization, we can support broader adoption and sustained use of AI scribes in clinical practice. The future of healthcare documentation looks promising, and continued research in this area is essential.
๐ฌ Your comments
What are your thoughts on the adoption of AI scribes in healthcare? Do you see them as a viable solution to reduce physician burnout? ๐ฌ Share your insights in the comments below or connect with us on social media:
Usability-Related Barriers and Facilitators Influencing the Adoption and Use of AI Scribes in Healthcare: A Scoping Review.
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.
Author: [‘Atiku S’, ‘Olakotan O’, ‘Owolanke K’]
Journal: J Eval Clin Pract
Citation: Atiku S, et al. Usability-Related Barriers and Facilitators Influencing the Adoption and Use of AI Scribes in Healthcare: A Scoping Review. Usability-Related Barriers and Facilitators Influencing the Adoption and Use of AI Scribes in Healthcare: A Scoping Review. 2026; 32:e70365. doi: 10.1111/jep.70365