Evidence map›Paper›PMID 41507920›Full record

ArticleBMC health services research2026

Performance, acceptability, and impact of ambient listening scribe technology in an outpatient context: a mixed methods trial evaluation.

Salim Memon, Adam Brand, Bianca Taylor, Adelaide Michael, Rachael Smithson

Abstract read
In one paragraph

Article in BMC health services research, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing 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

4 citing papers in PubMed.

  1. Article
  2. Effect of use of ambient listening technology on patient-reported communication and satisfaction ratings.Journal of the American Medical Informatics Association : JAMIA · 2026
    Article
  3. Review
  4. 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

5 authors.

Salim MemonGeneral Medicine, Gold Coast Hospital and Health Service, Gold Coast, Australia.
Adam BrandEmergency Department, Gold Coast Hospital and Health Service, Gold Coast, Australia.
Bianca TaylorDigital Transformation and Research, Gold Coast Hospital and Health Service, Gold Coast, Australia.
Adelaide MichaelHealth Systems Research Group, Gold Coast Hospital and Health Service, Gold Coast, Australia.
Rachael SmithsonHealth Systems Research Group, Gold Coast Hospital and Health Service, Gold Coast, Australia. rachael.smithson@health.qld.gov.au.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundIn 2024, Gold Coast Hospital and Health Service outpatient division initiated a 16-week trial of artificial intelligence (AI)-enabled ambient listening scribe technology. The objective of this pilot study was to evaluate scribe technology performance (according to dimensions of quality, utility and reliability) and its impact on the experience of outpatient clinicians and patients receiving care.

methodsA mixed method research design combined analysis of data from patient and staff surveys, staff interviews, scribe outputs and electronic medical records across a breadth of outpatient specialties.

resultsBy and large, ambient listening technology was associated with positive patient and staff experience. On average, 58% of scribe outputs were accepted without modification into the electronic outpatient note. There was limited evidence of bias in outputs, however there was some evidence of hallucination or incorrect outputs.

conclusionsQualitative and quantitative data were internally consistent and demonstrated that ambient listening technology can (1) produce an accurate summary of outpatient appointments, (2) enhance clinical note quality and (3) improve both clinician and patient experience. CLINICAL TRIAL NUMBER: Not applicable.

Indexed as

Ambulatory CareArtificial IntelligenceElectronic Health RecordsOutpatientsAdultFemaleHumansMalePatient SatisfactionPilot ProjectsAmbient listeningArtificial intelligenceDigital innovationMachine learningMedical transcriptionScribe technology

Identifiers

PMID41507920
PMCPMC12882620

What OpenQuestion holds

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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.