Evidence map›Paper›PMID 41757203›Full record

ArticlemedRxiv : the preprint server for health sciences2026

Randomized Trial Protocol: Epic Generative AI Chart Summarization Tool to Reduce Ambulatory Provider Cognitive Task Load.

Aaron T Chin, Nina Zhu, Thomas Kingsley, Pallavi Mynampati, Yan Phipps, Artem Romanov, Sitaram Vangala, Maxwell Weng, Lauren E Wisk, Hawkin Woo and 2 more

Abstract readPreprint
PubMed Full text at PMC Publisher
In one paragraph

Article in medRxiv : the preprint server for health sciences, 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

12 authors.

Aaron T ChinUCLA Health Information Technology, UCLA Health, University of California, Los Angeles, Los Angeles, CA, United States.
Nina ZhuUCLA Health Information Technology, UCLA Health, University of California, Los Angeles, Los Angeles, CA, United States.
Thomas KingsleyUCLA Health Information Technology, UCLA Health, University of California, Los Angeles, Los Angeles, CA, United States.
Pallavi MynampatiUCLA Health Information Technology, UCLA Health, University of California, Los Angeles, Los Angeles, CA, United States.
Yan PhippsUCLA Health Information Technology, UCLA Health, University of California, Los Angeles, Los Angeles, CA, United States.
Artem RomanovDivision of General Internal Medicine and Health Services Research, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, United States.
Sitaram VangalaDivision of General Internal Medicine and Health Services Research, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, United States.
Maxwell WengDavid Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, United States.
Lauren E WiskDivision of General Internal Medicine and Health Services Research, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, United States.
Hawkin WooUCLA Health Information Technology, UCLA Health, University of California, Los Angeles, Los Angeles, CA, United States.
John N MafiDivision of General Internal Medicine and Health Services Research, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, United States.
Paul J LukacUCLA Health Information Technology, UCLA Health, University of California, Los Angeles, Los Angeles, CA, United States.

Funding

Biomedical Data Science Training Program for Precision Health EquityT15LM013976 · NLM · UNIVERSITY OF CALIFORNIA LOS ANGELES · PI ALEX BUI · 2022 to 2026
$1.9M
Leveraging Electronic Health Records to Measure and Reduce Harmful, Low-Value Care Among Older AdultsK76AG064392 · NIA · UNIVERSITY OF CALIFORNIA LOS ANGELES · PI MAFI, JOHN · 2020 to 2024
$1.2M
NIA NIH HHS K76 AG064392NLM NIH HHS T15 LM013976
6 · The paper itself

Abstract

Background: EHR documentation and chart review contribute to clinician workload and burnout. To alleviate pre-charting burden, Epic has released a new generative AI chart summarizer tool, which has become widely adopted; however, its impact has not been examined in randomized trials. Objective: To evaluate whether access to an Epic generative AI chart summarization tool reduces cognitive task load among ambulatory providers compared with usual care. Methods: Two-arm, parallel-group randomized controlled trial among ambulatory clinicians across multiple specialties. Clinicians will be randomized 1:1 to tool access versus usual care for 90 days. The primary outcome is change in a 4-item physician task load (PTL) adapted for the pre-charting task. Exploratory outcomes include EHR-derived time metrics (Caboodle and Signal), professional fulfillment/burnout (PFI), usability (SUS), clinician satisfaction, aggregated patient experience item from CG-CAHPS, and reported safety related metrics. Ethics and Dissemination: Analyses will use clinician-level survey responses and aggregated EHR metrics; no patient-level protected health information will be included in the analytic dataset. Results will be disseminated via preprint and peer-reviewed publication.

Indexed as

chart reviewclinician burdenEHRgenerative AIrandomized trialtask load

Identifiers

PMID41757203
PMCPMC12934868

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
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.