Evidence map›Paper›PMID 41916739›Full record

ArticleBMJ open quality2026

Improving ward round documentation using the

Amna Qamar, Mairead Kelly, Robert Maweni, Ayeshah Abdul-Hamid

Abstract read
In one paragraph

Article in BMJ open quality, 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. Review
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

4 authors.

Amna QamarOxford University Hospitals NHS Foundation Trust, Oxford, England, UK amna.qamar3@nhs.net.ORCID 0000-0002-6372-1468
Mairead KellyOxford University Hospitals NHS Foundation Trust, Oxford, England, UK.
Robert MaweniOxford University Hospitals NHS Foundation Trust, Oxford, England, UK.
Ayeshah Abdul-HamidOxford University Hospitals NHS Foundation Trust, Oxford, England, UK.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

introductionAccurate and timely documentation during surgical ward rounds is critical for ensuring patient safety, effective multidisciplinary communication and continuity of care. In high-demand surgical settings, resident doctors often experience delays in completing documentation due to competing clinical priorities. This quality improvement project aimed to assess whether an artificial intelligence (AI) transcription tool,

methodsBaseline data on time taken to complete conventional ward round documentation were collected over a 4-day period. The

resultsThe implementation of

conclusionsUsing AI tools can not only improve timeliness of clinical records but also free resident doctors from scribing duties, allowing greater participation in patient care and enhancing educational opportunities. This intervention demonstrated the potential of AI-assisted documentation to improve workflow efficiency and patient flow while supporting resident doctor training and reducing administrative burden in a surgical setting.

Indexed as

Artificial IntelligenceDocumentationTeaching RoundsHumansQuality ImprovementArtificial IntelligenceElectronic Health RecordsInformation technology

Identifiers

PMID41916739
PMCPMC13052744

What OpenQuestion holds

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LicenceCC BY-NC
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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.