Evidence map›Paper›PMID 41915701›Full record

Observational studyJMIR medical informatics2026

Impact of an Ambient AI Scribe Among Clinicians and Patients: Real-World Prospective Observational 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 readObservational Study
In one paragraph

Observational study in JMIR medical informatics, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.

0numbers the graph read from it
0cells of the map it votes in
7citing 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

7 citing papers in PubMed.

  1. Article
  2. Article
  3. Review
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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

10 authors.

Jonathan Yue En Tan *Department of Future Health System, Singapore General Hospital, 1 Hospital Blvd, SingHealth Tower, Singapore, Singapore.ORCID 0009-0005-3263-2103
Iffat Bin Mohammad Rafi *Department of Future Health System, Singapore General Hospital, 1 Hospital Blvd, SingHealth Tower, Singapore, Singapore.ORCID 0009-0005-2679-4448
Gerald Gui Ren SngData Science and Artificial Intelligence Laboratory, Singapore General Hospital, Singapore, Singapore.ORCID 0000-0002-8795-7491
Joshua Yi Min TungData Science and Artificial Intelligence Laboratory, Singapore General Hospital, Singapore, Singapore.ORCID 0000-0003-4991-9790
Daniel Yan Zheng LimData Science and Artificial Intelligence Laboratory, Singapore General Hospital, Singapore, Singapore.ORCID 0000-0002-9715-6970
Jasmine Chiat Ling OngDivision of Pharmacy, Singapore General Hospital, Singapore, Singapore.ORCID 0000-0001-6916-5960
Gek Hsiang LimHealth Services Research Unit, Singapore General Hospital, Singapore, Singapore.ORCID 0000-0002-8298-4618
Eileen Yi Lee LewOffice of Insights and Analytics, SingHealth, Singapore, Singapore.ORCID 0009-0002-1561-4913
Kuok Wei ChiaDepartment of Future Health System, Singapore General Hospital, 1 Hospital Blvd, SingHealth Tower, Singapore, Singapore.ORCID 0000-0002-7174-7422
Mark Kok Hong HengOffice of Insights and Analytics, SingHealth, Singapore, Singapore.ORCID 0009-0002-4483-6662

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Ambient artificial intelligence scribes are increasingly used to reduce clinician burnout and cognitive load, although 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 time stamps, which may not accurately reflect actual documentation time. Objective: This study aimed to quantify the impact of ambient scribe technology on clinician efficiency and patient engagement in an outpatient setting using direct observational methods and patient surveys. Methods: We conducted a prospective, within-clinician quality improvement study at a large academic medical center in Singapore from December 2024 to May 2025. Nine clinicians participated in matched observation sessions with and without an in-house ambient scribe tool. Five trained observers used standardized 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 (mean 5.3, SD 2.7 minutes to 4.5, SD 2.4 minutes; P=.04); a 10.6% increase in proportion of eye contact time (mean 69.6%, SD 18.6% to 77.1%, SD 17.7%; P=.009); and no significant change in consultation duration (mean 11.5, SD 6.9 minutes vs 10.9, SD 5.6 minutes; P=.42) or total cycle time per patient (mean 13.7, SD 7.7 minutes vs 13.2, SD 6.3 minutes; P=.57), where total cycle time includes preclerking, consultation, and postconsultation documentation. Effects were consistent across new and follow-up patients. Patient acceptance was favorable: of 39 surveyed patients, 27 (69.2%) agreed that their physician focused on them more during the consultation, and none expressed discomfort with the technology. Conclusions: This evaluation of ambient scribe use in an Asian health care setting demonstrates that experienced users achieved reduced documentation time and improved patient engagement without affecting consultation duration or total time per patient. The technology was also well accepted by patients. Taken together, these findings suggest that ambient scribes reallocate clinician effort toward patient interaction rather than enabling faster patient turnover, supporting their implementation across diverse health care settings.

Indexed as

Artificial IntelligenceDocumentationElectronic Health RecordsTime and Motion StudiesFemaleHumansMaleProspective StudiesSingaporeSurveys and QuestionnairesAIambient intelligenceambient scribeartificial intelligenceclinical documentationeye contactgenerative artificial intelligencephysician-patient interactiontime-motion study

Identifiers

PMID41915701
PMCPMC13037756

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.