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

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.Author summary: We studied how official guidance governs artificial intelligence scribes, which are tools that listen to clinical consultations and create draft notes. The study matters because adoption is moving faster than the guidance that explains who is responsible when a note is wrong. We found that official documents do not leave artificial intelligence scribes unregulated. Instead, governance is spread across patient leaflets, privacy guidance, professional duties, medical device rules, safety documents and vendor checks. These documents rely on the same practical idea. The artificial intelligence note is only a draft until a clinician checks it and saves it. This can reassure patients and protect the record, but it can also shift hidden work to clinicians. They must catch missed negations, fabricated diagnoses and omitted treatment steps, while also managing consent, refusal, privacy and reporting duties. We argue that future guidance should specify review time, reasonable verification standards, refusal pathways, equity thresholds and incident reporting for artificial intelligence-generated documentation errors.