Evidence map›Paper›PMID 42386325›Full record

ArticleBMJ health & care informatics2026

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

Charlotte Blease, Anna Kharko, Carolina Garcia Sanchez, David Navarro, Brian McMillan, Jens Gaab, Cosima Locher, Enrico Coiera

Abstract read
In one paragraph

Article in BMJ health & care informatics, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

8 authors.

Charlotte BleaseDepartment of Women's and Children's Health, Uppsala University, Uppsala, Sweden charlotte.blease@uu.se.ORCID http://orcid.org/0000-0002-0205-1165
Anna KharkoDepartment of Women's and Children's Health, Uppsala University, Uppsala, Sweden.
Carolina Garcia SanchezDepartment of Women's and Children's Health, Uppsala University, Uppsala, Sweden.
David NavarroCentre for Health Informatics, Australian Institute for Health Innovation, Macquarie University, Sydney, New South Wales, Australia.
Brian McMillanThe University of Manchester, Manchester, UK.ORCID http://orcid.org/0000-0002-0683-3877
Jens GaabDepartment of Clinical Psychology and Psychotherapy, Faculty of Psychology, University of Basel, Basel, Switzerland.
Cosima LocherClinical Psychology and Psychosomatics, Faculty of Psychology, University of Basel, Basel, Switzerland.
Enrico CoieraCentre for Health Informatics, Australian Institute of Health Innovation, Macquarie University, Sydney, New South Wales, Australia.ORCID http://orcid.org/0000-0002-6444-6584

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

objectiveTo 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.

methodsWe 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.

resultsIn 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. DISCUSSION: 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.

conclusionAmbient 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.

Indexed as

Artificial IntelligenceDocumentationElectronic Health RecordsGeneral PractitionersPrimary Health CareFemaleHumansMaleSurveys and QuestionnairesUnited KingdomWorkflowArtificial intelligenceDocumentationElectronic Health RecordsInformaticsPrimary Health Care

Identifiers

PMID42386325
PMCPMC13331185

What OpenQuestion holds

Textmetadata
LicenceCC BY
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Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.