Regulating the drafting fiction: A qualitative content analysis of official guidance and regulator documents on AI scribe adoption and use in healthcare.

Authors

Samuel Oluwatobi Atiku

Published in

PLOS digital health. Volume 5. Issue 10. Pages e0001776. Epub Oct 06, 2026.

Abstract

Artificial intelligence scribes use automatic speech recognition and generative language models to document clinical consultations, but official expectations for safe adoption are scattered across privacy, professional, medical device, safety and health-system guidance. We conducted a qualitative content analysis of 74 unique English-language official documents issued by regulators, government bodies, medical colleges and health-system organisations. Documents were identified through a staged grey literature search and coded using a hybrid deductive and inductive framework. We developed the analytic concept of a "drafting fiction" to describe a recurrent cross-document pattern in which artificial intelligence-generated clinical text remains provisional until clinician review and authentication. Across guidelines, transparency is common, consent is uneven, verification is treated as the central safeguard in direct AI-scribe guidance, privacy governance extends from secure storage to voice identifiability, and medical device classification shifts when transcription becomes summarisation. Organisational safety cases, vendor due diligence and data protection assessments narrow product-level uncertainty, but they do not define the encounter-level support needed for clinicians to detect hallucination, omission, automation bias or model drift. The study concludes that artificial intelligence scribe governance is taking shape as a sociotechnical architecture, although it relies on an underspecified human control assumption. Future guidance should define reasonable verification standards, allocate protected review time, resource consent refusal pathways and require monitoring of performance across accents, language and speech differences.

PMID:
42837370
Bibliographic data and abstract were imported from PubMed on 07 Oct 2026.

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