Evidence map›Paper›PMID 42258767›Full record

ArticleJMIR medical education2026

Re-Centering Clinical Documentation in the Age of AI Scribes: Four Aims of the Patient Chart Note.

Chwen-Yuen Angie Chen, Tiffany I Leung, Rika Bajra

Abstract read
In one paragraph

Article in JMIR medical education, 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

3 authors.

Chwen-Yuen Angie Chen *Division of Primary Care Population Health, Department of Medicine, Stanford University, 211 Quarry Road, 4th Floor, Palo Alto, CA, 94304, United States, 1 650-498-9000, 1 888 755 4489.ORCID 0000-0002-7207-598X
Tiffany I Leung *JMIR Publications, Toronto, ON, Canada.ORCID 0000-0002-6007-4023
Rika BajraDivision of Primary Care Population Health, Department of Medicine, Stanford University, 211 Quarry Road, 4th Floor, Palo Alto, CA, 94304, United States, 1 650-498-9000, 1 888 755 4489.ORCID 0000-0002-1378-2561

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Unlabelled: Clinical documentation is a foundational skill in medicine, developed during training and required in everyday practice. Historically, the chart note functioned as a clinician-centered cognitive tool for reasoning, teaching, and communication but has evolved into a multipurpose document shaped by administrative, regulatory, and financial demands, and is increasingly experienced as burdensome. The electronic health record, intended to improve efficiency, has introduced additional complexity and workflow strain, contributing to clinician burnout. Ambient artificial intelligence (AI) scribe technologies are rapidly being adopted to address these challenges, yet their implementation has outpaced evidence regarding their impact on learning, cognition, and clinical reasoning. We raise questions regarding the underexplored consequences of AI-assisted documentation, particularly cognitive off-loading and the potential for de-skilling, echoing historical concerns surrounding earlier cognitive technologies that externalized thought. We propose a practical framework that re-centers clinical documentation around four core aims: supporting clinical reasoning ("note to self"), facilitating communication ("note to others"), meeting medicolegal and billing requirements, and enhancing patient education in the era of open notes. Incorporating this framework into training may promote more intentional documentation practices before routine reliance on AI. We advocate for reframing the chart note to support clinician development and preserve its role in high-quality, patient-centered care.

Indexed as

Artificial IntelligenceDocumentationElectronic Health RecordsHumansambient scribeartificial intelligenceclinical trainingmedical documentationmedical education

Identifiers

PMID42258767
PMCPMC13245841

What OpenQuestion holds

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LicenceCC BY
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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.