AI Scribes in Health Care: Balancing Transformative Potential With Responsible Integration.

The administrative burden of clinical documentation contributes to health care practitioner burnout and diverts valuable time away from direct patient care. Ambient artificial intelligence (AI) scribes-also called "digital scribes" or "AI scribes"-are emerging as a promising solution, given their potential to automate clinical note generation and reduce clinician workload, and those specifically built on a large language model (LLM) are emerging as technologies for facilitating real-time clinical documentation tasks. This potentially transformative development has a foundation on longer-standing, AI-based transcription software, which uses automated speech recognition and/or natural language processing. Recent studies have highlighted the potential impact of ambient AI scribes on clinician well-being, workflow efficiency, documentation quality, user experience, and patient interaction. So far, limited evidence indicates that ambient AI scribes are associated with reduced clinician burnout, lower cognitive task load, and significant time savings in documentation, particularly in after-hours electronic health record (EHR) work. One consistently reported benefit is the improvement in the patient-physician interaction, as physicians feel more present during a clinical encounter. However, these benefits are counterbalanced by persisting concerns regarding the accuracy, consistency, language use, and style of AI-generated notes. Studies noting errors, omissions, or hallucinations caution that diligent clinician oversight is necessary. The user experience is also heterogeneous, with benefits varying by specialty and individual workflow. Further, there are concerns about ethical and legal issues, algorithmic bias, the potential for long-term "cognitive debt" from overreliance on AI, and even the potential loss of physician autonomy. Additional pragmatic concerns include security, privacy, integration, interoperability, user acceptance and training, and the cost-effectiveness of adoption at scale. Finally, limited studies describe adoption or evaluation of these technologies by nonphysician clinicians and health professionals. Although ambient AI scribes and AI-driven documentation technologies are promising as potentially practice-changing tools, there are many questions remaining. Key issues persist, including responsible deployment with the goal of ensuring that ambient AI scribes produce clinical documentation that supports more efficient, equitable, and patient-centered care. To advance our collective understanding and address key issues, JMIR Medical Informatics is launching a call for papers for a new section on "Ambient AI Scribes and AI-Driven Documentation Technologies." As editors, we look forward to the opportunity to advance the science and understanding of these fields through publishing high-quality and rigorous scholarly work in this new section of JMIR Medical Informatics.

Duke Scholars

Author Arriel Benis Biomedical Engineering