Evidence map›Paper›PMID 42870111›Full record

ArticleWorld journal of urology2026

Ambient artificial intelligence scribes in urology: a pilot evaluation of user experience and clinical efficiency gains.

Aleksandra M Golos, Sharath Reddy, Mursal Gardezi, Thomas V Martin, Marianne Casilla-Lennon

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Article in World journal of urology, 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

5 authors.

Aleksandra M GolosDepartment of Urology, Yale School of Medicine, 333 Cedar St, New Haven, CT, USA.ORCID http://orcid.org/0000-0001-5803-4164
Sharath ReddyDepartment of Urology, Yale School of Medicine, 333 Cedar St, New Haven, CT, USA.ORCID http://orcid.org/0000-0002-3291-0565
Mursal GardeziDepartment of Urology, Yale School of Medicine, 333 Cedar St, New Haven, CT, USA.ORCID http://orcid.org/0000-0003-2188-3167
Thomas V MartinDepartment of Urology, Yale School of Medicine, 333 Cedar St, New Haven, CT, USA.
Marianne Casilla-LennonDepartment of Urology, Yale School of Medicine, 333 Cedar St, New Haven, CT, USA. marianne.casilla-lennon@yale.edu.ORCID http://orcid.org/0000-0003-1512-6186

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

purposeAmbient artificial intelligence (AI) scribes aim to reduce documentation burden, yet evidence in urology is limited. We evaluated the pilot rollout of an AI scribe system in our academic urology department, focusing on user experience and electronic health record-based efficiency metrics.

methodsAI scribes were introduced into outpatient urology clinics in June 2024. We developed a user experience questionnaire to assess navigation, functionality, workload reduction, and clinical impact at 1 and 11 months of implementation. Clinical efficiency was evaluated using changes in Epic Signal metrics from pre- to post-rollout. Descriptive statistics were stratified by AI scribe adoption status and documentation method.

resultsOf 41 eligible clinicians, seven initially enrolled in the AI scribe pilot. Two clinicians discontinued use, and three additional clinicians adopted the technology, resulting in eight persistent AI scribe users (19.5%). Median utilization in office visit notes was 90%. User experience improved across all domains, most notably in workload reduction (3.1 to 4.7). Users reported reduced after-hours charting and improved patient interaction; limitations included lack of pre-charting capabilities and insufficient adaptation to urology-specific documentation needs. AI scribe users demonstrated consistent within-clinician reductions in time in notes per day (-28.7%; 14.0 min), time in notes per appointment (-21.9%; 0.9 min), and time outside scheduled hours (-28.6%; 13.4 min). Inconsistent background trends were observed among non-users.

conclusionAI scribe users reported positive experience with the technology and demonstrated reductions in documentation burden. Findings are exploratory and support larger, controlled evaluations of note quality and workflow impact.

Indexed as

Artificial IntelligenceDocumentationElectronic Health RecordsUrologyHumansPilot ProjectsWorkloadAmbient listeningArtificial intelligence scribeClinical documentationElectronic health recordPhysician burnout

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Registered trials

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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.