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Appl Clin Inform 2026; 17(01): 82-88
DOI: 10.1055/a-2807-4098
Research Article

The Effect of Ambient Listening Technology on the Patient Experience

Authors

  • Kajal N. Patel

    1   Department of Medicine, Emory University School of Medicine, Atlanta, Georgia, United States

  • Mary M. Stout

    1   Department of Medicine, Emory University School of Medicine, Atlanta, Georgia, United States

  • Mary A. Solis

    1   Department of Medicine, Emory University School of Medicine, Atlanta, Georgia, United States

  • Nasrin Riyahi

    1   Department of Medicine, Emory University School of Medicine, Atlanta, Georgia, United States

  • Christopher Holland

    2   Emory Digital, Emory Healthcare, Atlanta, Georgia, United States

  • Pamela D. Vohra-Khullar

    1   Department of Medicine, Emory University School of Medicine, Atlanta, Georgia, United States

  • Miranda A. Moore

    1   Department of Medicine, Emory University School of Medicine, Atlanta, Georgia, United States
    3   Department of Family and Preventive Medicine, Emory University School of Medicine, Atlanta, Georgia, United States

  • Reema H. Dbouk 

    1   Department of Medicine, Emory University School of Medicine, Atlanta, Georgia, United States

Funding Information This study is supported by the Emory Academic Internal Medicine Center with donations from the O. Wayne Rollins Foundation; the sponsor did not play a role in study design, data collection, interpretation, or manuscript preparation.
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Abstract

Background

Ambient listening tools utilize generative artificial intelligence (AI) to create clinical notes from real-time conversations between clinicians and patients during an encounter. One of the potential benefits of ambient listening tools is an improvement in reported patient experience.

Objective

This study aimed to compare the patient experience of an outpatient visit during which an ambient listening tool is used with a standard visit and to quantify any perceived improvements in care.

Methods

Patients completed a targeted survey following outpatient clinic visits across all departments at a large academic institution. We conducted ordered logistic regression analyses to examine the association between ambient scribe use and patient satisfaction across six survey domains: provider communication, provider attention, perceived time spent with the provider, overall interaction with the provider, understanding of health information, and quality of the after-visit summary.

Results

Our analysis included 8,120 patients who submitted a survey following their outpatient visit in February to April 2025. Patients whose provider used an ambient scribe had higher odds of reporting satisfaction with the perceived duration of time spent with the provider (OR = 1.13, 95% CI: 1.01–1.26, p = 0.033).

Conclusion

In this observational study, use of an ambient AI scribe was associated with a small improvement in one patient-reported satisfaction domain of perceived attention from the provider and no detectable differences across other domains assessing patient experience. These findings suggest that, in early real-world implementation, ambient AI documentation tools may be acceptable to patients and do not appear to adversely affect perceived visit quality.

Keywords

generative artificial intelligence - ambient listening technology - ambient scribe - AI scribe - patient satisfaction

Protection of Human and Animal Subjects

This study was conducted according to the guidelines in the Declaration of Helsinki and was deemed to not be human subject research by the Emory University Institutional Review Board on January 15, 2024.


Data Availability Statement

Deidentified data analyzed during the current study is available from the corresponding author upon reasonable request.


Contributors' Statement

K.N.P.: conceptualization, writing—original draft, writing—review and editing; M.M.S.: conceptualization, writing—original draft, writing—review and editing; M.A.S.: conceptualization, writing—original draft, writing—review and editing; N.R.: data curation, formal analysis, writing—original draft, writing—review and editing; C.H.: conceptualization, investigation, methodology, writing—original draft, writing—review and editing; P.D.V.-K.: conceptualization, writing—original draft, writing—review and editing; M.A.M.: data curation, formal analysis, methodology, writing—original draft, writing—review and editing; R.H.D.: conceptualization, funding acquisition, methodology, supervision, writing—original draft, writing—review and editing.




Publication History

Received: 17 September 2025

Accepted: 05 February 2026

Article published online:
27 February 2026

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