Evidence map›Paper›PMID 42877726›Full record

ReviewBMJ digital health & AI2026

Beyond productivity: a

Lucas Martinus Seuren, Robin Williams, Kathrin Cresswell

Abstract readReview
In one paragraph

Review in BMJ digital health & AI, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing 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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

3 authors.

Lucas Martinus SeurenUsher Institute, The University of Edinburgh, Edinburgh, Scotland, UK.ORCID 0000-0001-9631-3113
Robin WilliamsInstitute for the Study of Science, Technology and Innovation (ISSTI), The University of Edinburgh, Edinburgh, Scotland, UK.
Kathrin CresswellUsher Institute, The University of Edinburgh, Edinburgh, Scotland, UK.ORCID 0000-0001-6634-9537

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Ambient artificial intelligence scribes (ambient scribes) are increasingly used across healthcare systems to support clinical documentation. Implementation strategies and associated research have been mainly concerned with the ability to accurately and efficiently produce clinical notes. Some concerns have been raised, such as potential hallucinations or omissions that require editorial work of clinicians, but this research has not moved beyond the technical performance of ambient scribes. We sought to synthesise emerging evidence and perspectives on ambient scribes from a sociotechnical perspective, focusing on how these systems interact with existing clinical work practices and organisational processes. We searched Embase, PubMed and Scopus to identify relevant literature and included 27 articles. We used a sociotechnical lens to synthesise research and map core themes against implementation domains from the Non-Adoption, Abandonment, Scale-Up, Spread and Sustainability (NASSS) framework. We identified few empirical studies examining the risks of ambient scribes, and even fewer investigating their implementation and adoption in practice. Existing research has focused predominantly on their potential to reduce administrative burden and improve efficiency. However, ambient scribes do more than automate note-taking. They actively shape clinical workflows and patient-clinician interactions and change the nature and function of clinical documentation. This suggests that there has been a de facto acceptance by policymakers and practitioners of the value proposition without critically examining the underlying assumptions and broader consequences. Ambient scribes must be understood as complex interventions rather than just productivity tools. There is therefore an urgent need for contextual validation and longitudinal evaluation.

Indexed as

Artificial intelligenceBMJ Health InformaticsDelivery of Health CareImplementation ScienceMedical Informatics

Identifiers

PMID42877726
PMCPMC13663716

What OpenQuestion holds

Textmetadata
Read underepoch 390

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.