JMIR Publications

ABSTRACT

Ambient scribe tools are increasingly being used to generate draft clinical documentation in an effort to reduce documentation burden, enhance workflow, and improve clinician and patient experience. Their optimal use, however, depends on a clear understanding of how sound generated during clinical encounters is transformed into draft notes and where characteristic failure modes may arise. This narrative review explains the processing pipeline underlying ambient scribe technology and translates that account into a practical framework for clinician use. Literature was reviewed between November 2025 and March 2026 using PubMed, IEEE Xplore, IsisCB Explore, ACM Digital Library, arXiv, Google Scholar, and citation tracking. Priority was given to peer-reviewed sources, with selective inclusion of foundational technical and conceptual works when appropriate. The review is organized around three processing stages: acoustic capture and digitization; speech recognition and transcript generation; and draft progress note generation. Ambient scribes are understood here as multistage, machine-mediated systems in which predictable failure modes may arise, including omission of clinically relevant information, speaker misattribution, contextual distortion, unsupported insertion, reversal of clinical meaning, and operational inefficiency from output that is verbose or factually incorrect. This review links these mechanisms to a practical clinician framework centered on judgment before the encounter, deliberate communication and acoustic capture practices during the encounter, and targeted review of predictable artifact classes after note generation. By focusing on mechanism, predictable failure modes, and clinician response, this review provides a practical foundation for informed clinical use. That foundation is intended to support use that is both more effective and more cautious.