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Currently submitted to: JMIR Medical Informatics

Date Submitted: Oct 9, 2025
Open Peer Review Period: Oct 16, 2025 - Dec 11, 2025
(currently open for review and needs more reviewers - can you help?)

Warning: This is an author submission that is not peer-reviewed or edited. Preprints - unless they show as "accepted" - should not be relied on to guide clinical practice or health-related behavior and should not be reported in news media as established information.

Impact of a Multilingual Ambient AI Scribe: A Real-World Time-Motion Study

  • Jonathan Yue En Tan; 
  • Iffat Bin Mohammad Rafi; 
  • Gerald Gui Ren Sng; 
  • Joshua Yi Min Tung; 
  • Daniel Yan Zheng Lim; 
  • Jasmine Chiat Ling Ong; 
  • Gek Hsiang Lim; 
  • Eileen Yi Lee Lew; 
  • Kuok Wei Chia; 
  • Mark Kok Hong Heng

ABSTRACT

Background:

Ambient AI scribes are increasingly used to reduce clinician burnout and cognitive load, though their impact on documentation time remains inconsistent across studies. Most existing real-world impact studies have been conducted in the United States and rely on electronic health record timestamps, which may not accurately reflect actual documentation time. No published studies assessed their effectiveness in multilingual clinical environments, where language diversity may influence outcomes.

Objective:

To quantify the impact of an ambient scribe technology on clinician efficiency and patient engagement in a multilingual outpatient setting using direct observational methods and patient surveys.

Methods:

We conducted a prospective, within-clinician quality improvement study at a large academic medical centre in Singapore from December 2024 to May 2025. Nine clinicians participated in matched observation sessions with and without an in-house ambient scribe tool. The ambient scribe tool supported multiple languages, enabling evaluation in a naturally multilingual clinical environment. Five trained observers used standardised time-motion methodology to capture documentation time, proportion of eye contact, and consultation duration across 169 consultations. Patient surveys were administered following consultations using the ambient scribe. Linear mixed-effects models were used to account for clustering within clinicians.

Results:

Ambient scribe use was associated with a 15.0% reduction in documentation time per consultation (5.3 to 4.5 minutes; P=.036), 10.6% increase in proportion of eye contact time (69.6% to 77.1%, P=.009), and no significant change in consultation duration (11.5 vs 10.9 minutes, P=.415). Effects were consistent across new and follow-up patients. Among consultations with the ambient scribe, 81% were conducted in English, 18% in Mandarin, and 1% in Malay. Patient acceptance was favourable: among 39 surveyed patients, 69% agreed their doctor focused on them more during the consultation, and none expressed discomfort with the technology.

Conclusions:

This first real-world evaluation of ambient scribe use in a multilingual Asian healthcare setting demonstrates that experienced users achieved reduced documentation time and improved patient engagement without affecting consultation duration. The technology was effective across multiple languages and well accepted by patients. These findings suggest ambient scribes should be viewed as quality-of-care interventions that shift clinician attention from clerical tasks to patient engagement, supporting broader implementation in diverse healthcare settings.


 Citation

Please cite as:

Tan JYE, Rafi IBM, Sng GGR, Tung JYM, Lim DYZ, Ong JCL, Lim GH, Lew EYL, Chia KW, Heng MKH

Impact of a Multilingual Ambient AI Scribe: A Real-World Time-Motion Study

JMIR Preprints. 09/10/2025:85580

DOI: 10.2196/preprints.85580

URL: https://preprints.jmir.org/preprint/85580

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