Evidence map›Paper›PMID 42416857›Full record

ArticleJAMIA open2026

Evaluating the impact of artificial intelligence scribes on clinical documentation in primary care: a simulation study.

LaShawn Murray, Emily Ha, Qianyue Wang, Isabelle Choon-Kon-Yune, Siying Luan, Vanessa Kishimoto, Onil Bhattacharyya, Payal Agarwal, Enid Montague

Abstract read
In one paragraph

Article in JAMIA open, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

9 authors.

LaShawn MurrayDepartment of Mechanical and Industrial Engineering, University of Toronto, Toronto, ON, M5S 3G8, Canada.ORCID https://orcid.org/0009-0003-9710-5545
Emily HaDalla Lana School of Public Health, University of Toronto, Toronto, ON, M5T 3M7, Canada.ORCID https://orcid.org/0000-0003-4639-7894
Qianyue WangDepartment of Statistical Sciences, University of Toronto, Toronto, ON, M5G 1Z5, Canada.ORCID https://orcid.org/0009-0004-4177-7382
Isabelle Choon-Kon-YuneWomen's College Hospital Institute for Health System Solutions and Virtual Care, Women's College Hospital, Toronto, ON, M5S 1B2, Canada.ORCID https://orcid.org/0009-0001-4357-3381
Siying LuanWomen's College Hospital Institute for Health System Solutions and Virtual Care, Women's College Hospital, Toronto, ON, M5S 1B2, Canada.ORCID https://orcid.org/0000-0002-6861-7010
Vanessa KishimotoWomen's College Hospital Institute for Health System Solutions and Virtual Care, Women's College Hospital, Toronto, ON, M5S 1B2, Canada.ORCID https://orcid.org/0000-0002-4516-1225
Onil BhattacharyyaWomen's College Hospital Institute for Health System Solutions and Virtual Care, Women's College Hospital, Toronto, ON, M5S 1B2, Canada.ORCID https://orcid.org/0000-0001-5219-7288
Payal AgarwalWomen's College Hospital Institute for Health System Solutions and Virtual Care, Women's College Hospital, Toronto, ON, M5S 1B2, Canada.ORCID https://orcid.org/0000-0001-5625-7532
Enid MontagueDepartment of Mechanical and Industrial Engineering, University of Toronto, Toronto, ON, M5S 3G8, Canada.ORCID https://orcid.org/0009-0005-7387-4248

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Objectives: To investigate the performance of artificial intelligence (AI) scribes and their impact on clinical documentation time. Materials and Methods: Artificial intelligence scribes were assessed using clinical simulations with 9 primary care physicians, each conducting 4 simulated encounters with standardized patients with and without the use of an AI scribe. Clinical simulations were audio and video recorded, and videos were coded for documentation behaviors using a designed coding scheme. Results: Clinical documentation accounts for 36.3% of the simulated clinical encounter without the use of the AI scribe, whereas primary care physicians spend 11.2% of the simulated encounter on clinical documentation when using an AI scribe. The use of AI scribes accounts for a 69.1% reduction in documentation time. Discussion: Clinical simulations offered a controlled and realistic environment to assess the impact of AI scribes on primary care physicians' workflows, demonstrating their potential to reduce documentation time. Conclusion: Artificial intelligence scribes demonstrate potential to alleviate the burden of clinical documentation, addressing a critical factor contributing to physician burnout. Further research is needed to understand the impact of AI scribes in real-life clinical settings across complex patient scenarios.

Indexed as

artificial intelligenceclinical documentationdigital scribeselectronic health recordprimary care

Identifiers

PMID42416857
PMCPMC13341013

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

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.