Evidence map›Paper›PMID 40694347›Full record

ArticleJAMA network open2025

Informed Consent for Ambient Documentation Using Generative AI in Ambulatory Care.

Katharine Lawrence, Vasudev S Kuram, Defne L Levine, Sarah Sharif, Conner Polet, Kiran Malhotra, Kellie Owens

Abstract read
In one paragraph

Article in JAMA network open, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 26 papers.

0numbers the graph read from it
0cells of the map it votes in
26citing 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

26 citing papers in PubMed.

  1. Article
  2. Review
  3. Article
  4. Review
  5. Artificial Intelligence Scribes in Orthopaedic Surgery: A Narrative Review.Journal of the American Academy of Orthopaedic Surgeons. Global research & reviews · 2026
    Review
  6. Article
  7. Observational
  8. Article
  9. Article
  10. Review
  11. Review
  12. Article
  13. Review
  14. The Ethics of AI Scribes as Epistemic Agents.JMIR medical informatics · 2026
    Article
  15. Review
  16. Reintegrating the Human in Health: A Triadic Blueprint for Whole-Person Care in the Age of AI.International journal of environmental research and public health · 2026
    Article
  17. Review
  18. Article
  19. Article
  20. Promise and Caution: Mapping Opportunities for AI Decision Support in Emergency Medical Services.Proceedings. IEEE International Conference on Healthcare 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

7 authors.

Katharine LawrenceDepartment of Population Health, New York University Grossman School of Medicine, New York.
Vasudev S KuramDepartment of Health Informatics, Medical Center Information Technology, NYU Langone, New York, New York.
Defne L LevineDepartment of Population Health, New York University Grossman School of Medicine, New York.
Sarah SharifDepartment of Health Informatics, Medical Center Information Technology, NYU Langone, New York, New York.
Conner PoletDepartment of Health Informatics, Medical Center Information Technology, NYU Langone, New York, New York.
Kiran MalhotraDepartment of Health Informatics, Medical Center Information Technology, NYU Langone, New York, New York.
Kellie OwensDepartment of Population Health, New York University Grossman School of Medicine, New York.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Importance: Artificial intelligence (AI)-assisted ambient documentation technologies that use audio from clinic visits to generate documentation are being deployed across hospital systems to optimize clinical note writing and reduce administrative burden. However, little is known about optimal approaches to engage patients in informed consent for these tools. Objectives: To explore clinician and patient experiences with consent processes and examine the association between the use of ambient documentation tools and the patient-clinician relationship. Design, Setting, and Participants: This study was conducted from March 1 to December 31, 2024, in ambulatory practices across specialties in a large urban academic health center as part of an ongoing operational quality improvement initiative. Participants included clinicians and patients participating in an operational proof-of-concept exploration of ambient documentation technology. Main Outcomes and Measures: A pragmatic, sequential, inductive-deductive qualitative evaluation was conducted of informed consent contexts, processes, and challenges among ambulatory clinicians and patients exposed to ambient documentation technology. Evaluation included site visits, clinical observations, clinician interviews, and patient surveys to explore perceptions and challenges related to consent. Results: A total of 121 ambient documentation pilot users included 18 clinicians (mean [SD] years of practice, 18.6 [100]; 10 men [55.6%]) and 103 patients (mean [SD] age, 37 [12.5] years; 65 women [63.1%]). The most common consent approach was a verbal patient-clinician conversation prior to an individual encounter. Patients and clinicians had a spectrum of comfort with ambient technology; 77 patients (74.8%) reported being comfortable or very comfortable with their physician using ambient documentation. Patient trust, detail in the consent discussion, and intended tool use were associated with patient comfort and intent to consent. Technical understanding was associated with comfort with consent conversations: when provided basic information about the technology, 84 patients (81.6%) consented; this decreased to 57 patients (55.3%) when details about AI features, data storage, and corporate involvement were disclosed. Perceived benefits included reduced documentation burden, improved decision-making, and enhanced communication. Concerns included data security, legal liability, cognitive impacts, and equity. When asked about responsibility for medical errors linked to ambient documentation, 66 patients (64.1%) held physicians accountable; for data security breaches, 79 patients (76.7%) believed vendors should be responsible. Participants suggested a flexible consent model with digital touchpoints, education, nonclinical staff support, and opt-out options. Conclusions and Relevance: In this quality improvement study of 121 pilot users of AI-assisted ambient documentation technology, informed consent relied primarily on verbal conversations that varied based on time, knowledge, and the patient-clinician relationship. A flexible, multimodal approach-including education, time to discuss risks and benefits, digital resources, nonclinical staff involvement, and clear opt-out options-may improve consent processes and support broader acceptance of ambient documentation tools.

Indexed as

Ambulatory CareArtificial IntelligenceDocumentationInformed ConsentAdultFemaleHumansMaleMiddle AgedPhysician-Patient RelationsQualitative Research

Identifiers

PMID40694347
PMCPMC12284739

What OpenQuestion holds

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LicenceCC BY
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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.