Evidence map›Paper›PMID 42897388›Full record

ArticleJournal of general internal medicine2026

The Ambulatory Patient Experience with Ambient Artificial Intelligence Scribes: A Qualitative Study.

Oliver T Nguyen, Majid Afshar, Michael A Jaeb, Mary Ryan Baumann, Felice Resnik, Anne Gravel Sullivan, Graham Wills, Jason Dambach, Leigh A Mrotek, Mariah Quinn and 11 more

Abstract read
PubMed Publisher
In one paragraph

Article in Journal of general internal medicine, 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

21 authors.

Oliver T NguyenDepartment of Industrial and Systems Engineering, University of Wisconsin at Madison, Madison, WI, USA. fnguyen2@wisc.edu.ORCID http://orcid.org/0000-0003-2852-7106
Majid AfsharInstitute for Clinical and Translational Research, University of Wisconsin at Madison, Madison, WI, USA.
Michael A JaebDepartment of Family Medicine and Community Health, University of Wisconsin at Madison, Madison, WI, USA.
Mary Ryan BaumannInstitute for Clinical and Translational Research, University of Wisconsin at Madison, Madison, WI, USA.
Felice ResnikInstitute for Clinical and Translational Research, University of Wisconsin at Madison, Madison, WI, USA.
Anne Gravel SullivanInstitute for Clinical and Translational Research, University of Wisconsin at Madison, Madison, WI, USA.
Graham WillsUW Health, Madison, WI, USA.
Jason DambachDepartment of Medicine, University of Wisconsin at Madison, Madison, WI, USA.
Leigh A MrotekInstitute for Clinical and Translational Research, University of Wisconsin at Madison, Madison, WI, USA.
Mariah QuinnDepartment of Medicine, University of Wisconsin at Madison, Madison, WI, USA.
Kirsten AbramsonDepartment of Medicine, University of Wisconsin at Madison, Madison, WI, USA.
Peter KleinschmidtDepartment of Medicine, University of Wisconsin at Madison, Madison, WI, USA.
Thomas B BrazeltonDepartment of Pediatrics, University of Wisconsin at Madison, Madison, WI, USA.
Margaret A LeafUW Health, Madison, WI, USA.
Heidt TwedtDepartment of Medicine, University of Wisconsin at Madison, Madison, WI, USA.
Brian PattersonBerbeeWalsh Department of Emergency Medicine, University of Wisconsin at Madison, Madison, WI, USA.
Frank LiaoUW Health, Madison, WI, USA.
Stacy RasmussenUW Health, Madison, WI, USA.
Elizabeth S BurnsideInstitute for Clinical and Translational Research, University of Wisconsin at Madison, Madison, WI, USA.
Douglas A WiegmannDepartment of Industrial and Systems Engineering, University of Wisconsin at Madison, Madison, WI, USA.
Joel E GordonDepartment of Family Medicine and Community Health, University of Wisconsin at Madison, Madison, WI, USA.

Funding

University of Wisconsin Institute for Clinical and Translational ResearchUL1TR002373 · NCATS · UNIVERSITY OF WISCONSIN-MADISON · PI ELIZABETH S BURNSIDE, Allan R. Brasier · 2017 to 2026
$75.9M
NCATS NIH HHS UL1TR002373
6 · The paper itself

Abstract

objectiveThe effect of ambient artificial intelligence (AI) scribes on patient experience remains understudied and has been largely limited to closed-ended surveys. Qualitative studies are needed to more fully explore how ambient AI scribes influence patient experience. The aim of this study was to explore how ambient AI scribes influence the patient experience and to identify factors that improve patients' comfort with these tools. MATERIALS AND

methodsWe conducted a qualitative study using a semi-structured interview guide that captured key patient experience domains from a literature review. We recruited patients from numerous ambulatory specialties (e.g., dermatology, primary care) whose clinicians were participating in a pragmatic randomized clinical trial of an ambient AI scribe implementation at an academic medical center. We deductively and inductively drew patterns in patients' reported experiences and the factors that contributed to patients' comfort with these tools.

resultsTwenty patients were included in our sample before thematic saturation occurred. Patients reported improvements in patient experience through increased respect for patients' preferences (e.g., fewer distractions), improved thoroughness of visit notes, and increased time spent answering patients' questions. Factors influencing comfort included demographic factors (e.g., exposure to similar technologies), lack of saliency of the device, lack of privacy-related concerns, positive pre-existing patient-clinician relationships, and anticipated benefits to care.

conclusionAmbient AI scribes may improve the patient experience through improved patient-clinician verbal, non-verbal, and written communication. These findings contribute to our understanding how these tools shape the patient experience. CLINICAL TRIAL NUMBER: Not applicable.

Indexed as

electronic health recordsgenerative artificial intelligencenatural language processingpatient satisfactionprofessional-patient relations

Identifiers

PMID42897388

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.