Ambient AI in primary care: an exploratory mixed methods survey of UK general practitioners.

Abstract

To examine UK general practitioners' (GPs) adoption of ambient artificial intelligence (AI) scribes and to assess user-reported error rates, workflow impact and consent practices in primary care. We conducted a nationwide online mixed-methods survey of GPs recruited via Doctors.net.uk. Detailed analyses of use, errors and workflow impact focused on current users of ambient AI scribes. In August 2025, of 1003 respondents, 14% (n=141) reported current use of ambient AI scribes, 39% (n=396) intended to adopt them soon and 46% (n=466) had no plans to use them. Among users (n=141), Heidi Health predominated (86%). Most reported efficiency gains: 80% (n=112) reported reduced time spent on documentation and 70% (n=99) reduced cognitive load. Documentation quality was judged positively, with 55% (n=78) rating outputs as better than standard notes. Errors were common but usually minor: 32% (n=45) reported errors often/always, including 14% (n=20) with significant-to-critical implications. Errors were most frequent in multiparty consultations (38%), complex histories (35%) and non-English encounters (31%). Consent practices varied: 63% (n=89) routinely sought consent, with ≤10% of patients declining. Free-text responses (21% of users) highlighted benefits for workflow, alongside concerns about accuracy, ethics and system integration. Findings suggest that ambient AI scribes deliver meaningful efficiency gains and improved perceived documentation quality, but introduce non-trivial risks related to accuracy, equity and medicolegal accountability. The uneven performance in complex and multilingual consultations raises particular concerns about potential exacerbation of healthcare disparities. Ambient AI scribes are already in use across UK primary care. Proactive regulation, consistent consent practices and independent evaluation, including patient perspectives, are urgently needed to ensure safe, equitable and sustainable implementation.