Evidence map›Paper›PMID 41308194›Full record

ArticleJMIR medical informatics2025

Patient Attitudes Toward Ambient Voice Technology: Preimplementation Patient Survey in an Academic Medical Center.

Gary Leiserowitz, Jeff Mansfield, Scott MacDonald, Melissa Jost

Abstract read
In one paragraph

Article in JMIR medical informatics, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.

0numbers the graph read from it
0cells of the map it votes in
11citing 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

11 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
  4. Article
  5. Article
  6. Review
  7. Effect of use of ambient listening technology on patient-reported communication and satisfaction ratings.Journal of the American Medical Informatics Association : JAMIA · 2026
    Article
  8. Observational
  9. Review
  10. Article
  11. AI Scribes: Are We Measuring What Matters?JMIR medical informatics · 2026
    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

4 authors.

Gary LeiserowitzUniversity of California Davis Medical Center, Sacramento, CA, United States.ORCID 0000-0002-3016-4490
Jeff MansfieldUniversity of California Davis Medical Center, Sacramento, CA, United States.ORCID 0009-0001-1706-5906
Scott MacDonaldUniversity of California Davis Medical Center, Sacramento, CA, United States.ORCID 0000-0003-0797-656X
Melissa JostUniversity of California Davis Medical Center, Sacramento, CA, United States.ORCID 0000-0002-9322-6230

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundMany institutions are in various stages of deploying an artificial intelligence (AI) scribe system for clinic electronic health record (EHR) documentation. In anticipation of the University of California, Davis Health's deployment of an AI scribe program, we surveyed current patients about their perceptions of this technology to inform a patient-centered implementation.

objectiveWe assessed patient perceptions about current clinician EHR documentation practices before implementation of the AI scribe program, and preconceptions regarding the AI scribe's introduction.

methodsWe conducted a descriptive preimplementation survey as a quality improvement study. A convenience sample of 9171 patients (aged ≥18 years) who had a clinic visit within the previous year, was recruited via an email postvisit survey. Patient-identified demographics (age, gender, and race and ethnicity) were collected. The survey included rating scales on questions related to the patient perception of the AI scribe program, plus open-ended comments. Data were collated to analyze patient perceptions of including AI Scribe technology in a clinician visit.

resultsIn total, 1893 patients completed the survey (20% response rate), with partial responses from another 549. Sixty-three percent (n=1205) of the respondents were female, and most were 51 years and older (87%, n=1649). Most patients identified themselves as White (69%, n=1312), multirace (8%, n=154), Latinx (7%, n=130), and Black (2%, n=42). The respondents were not representative of the overall clinic populations and skewed more toward being female, ages 50 years and older, and White in comparison. Patients reacted to the current EHR documentation system, with 71% (n=1349) feeling heard or sometimes heard, but 23% (n=416) expressed frustrations that their physician focused too much on typing into the computer. When asked about their anticipated response to the use of an AI scribe, 48% (n=904) were favorable, 33% (n=630) were neutral, and 19% (n=359) were unfavorable. Younger patients (ages 18-30 years) expressed more skepticism than those aged 51 years and older. Further, 42% (655/1567) of positive comments received indicated this technology could improve human interaction during their visits. Comments supported that the use of an AI scribe would enhance patient experience by allowing the clinician to focus on the patient. However, when asked about concerns regarding the AI scribe, 39% (515/1330) and 15% (203/1330) of comments expressed concerns about documentation accuracy and privacy, respectively. Providing previsit patient education and obtaining permission were viewed as very important.

conclusionsThis patient survey showed that respondents are generally open to the use of an AI scribe program for EHR documentation to allow the clinician to focus on the patient during the actual encounter rather than the computer. Providing patient education and obtaining consent before using AI are important components to gain patient trust. Caution about the results is appropriate, given the low response rate and nonrepresentative profile.

Indexed as

Artificial IntelligenceElectronic Health RecordsPatientsAcademic Medical CentersAdolescentAdultAgedFemaleHumansMaleMiddle AgedSurveys and QuestionnairesYoung AdultAIAI scribeambient voice technologyartificial intelligenceEHRelectronic health recordshealth information privacypatient perceptions and attitudespreimplementation patient surveyscribe program

Identifiers

PMID41308194
PMCPMC12699246

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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.