Logo: to the web site of Uppsala University

uu.sePublications from Uppsala University
System disruptions
There are recurring service disruptions on some of the search portals due to high traffic. We are working to resolve the issue, you may temporarily encounter an error message.
Change search
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf
Ambient AI in primary care: an exploratory mixed methods survey of UK general practitioners
Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Women's and Children's Health, Participatory eHealth and Health Data Research Group.ORCID iD: 0000-0002-0205-1165
Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Women's and Children's Health, Participatory eHealth and Health Data Research Group. Univ Manchester, Ctr Primary Care & Hlth Serv Res, Manchester, England..ORCID iD: 0000-0003-0908-6173
Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Women's and Children's Health, Participatory eHealth and Health Data Research Group.ORCID iD: 0000-0003-0137-3434
Macquarie Univ, Australian Inst Hlth Innovat, Ctr Hlth Informat, Sydney, NSW, Australia..
Show others and affiliations
2026 (English)In: BMJ Health & Care Informatics, E-ISSN 2632-1009, Vol. 33, no 1, article id e101847Article in journal (Refereed) Published
Abstract [en]

Objective

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.

Methods

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.

Results

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.

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.

Conclusion

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.

Place, publisher, year, edition, pages
BMJ Publishing Group Ltd, 2026. Vol. 33, no 1, article id e101847
Keywords [en]
Artificial intelligence, Primary Health Care, Informatics, Documentation, Electronic Health Records
National Category
Health Sciences Computer and Information Sciences
Identifiers
URN: urn:nbn:se:uu:diva-594377DOI: 10.1136/bmjhci-2025-101847ISI: 001811849500001PubMedID: 42386325Scopus ID: 2-s2.0-105043551096OAI: oai:DiVA.org:uu-594377DiVA, id: diva2:2096259
Available from: 2026-08-28 Created: 2026-08-28 Last updated: 2026-08-28Bibliographically approved

Open Access in DiVA

fulltext(745 kB)43 downloads
File information
File name FULLTEXT01.pdfFile size 745 kBChecksum SHA-512
ce4b6309a52e3b79ec14076ad16803a724f4444fcd8ac34d84c41be9a07b273d42a1728031daef32b71fe5392000d2b117f3045e5144476c8a3dcdc5dd2d26d7
Type fulltextMimetype application/pdf

Other links

Publisher's full textPubMedScopus

Authority records

Blease, CharlotteKharko, AnnaGarcia Sanchez, Carolina

Search in DiVA

By author/editor
Blease, CharlotteKharko, AnnaGarcia Sanchez, Carolina
By organisation
Participatory eHealth and Health Data Research Group
In the same journal
BMJ Health & Care Informatics
Health SciencesComputer and Information Sciences

Search outside of DiVA

GoogleGoogle Scholar
The number of downloads is the sum of all downloads of full texts. It may include eg previous versions that are now no longer available

doi
pubmed
urn-nbn

Altmetric score

doi
pubmed
urn-nbn
Total: 231 hits
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf