Evidence map›Paper›PMID 42114818›Full record

ArticleApplied clinical informatics2026

Ambient Artificial Intelligence Scribes in Pediatric Hematology-Oncology: Early Implementation of DAX Copilot.

Molly Talman, Lamia Alam, Dara Mize, Yaa Kumah-Crystal, Carolynn Nall, Allison B McCoy, Adam Wright, Laura Zahn, Jennifer Andrews

Abstract read
In one paragraph

Article in Applied clinical informatics, 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.

Molly TalmanDepartment of Pediatrics, Vanderbilt University Medical Center, Nashville, Tennessee, United States.ORCID 0000-0002-8505-8329
Lamia AlamDepartment of Biomedical Informatics, Vanderbilt University Medical Center, Vanderbilt University, Nashville, Tennessee, United States.
Dara MizeDepartment of Biomedical Informatics, Vanderbilt University Medical Center, Vanderbilt University, Nashville, Tennessee, United States.
Yaa Kumah-CrystalDepartment of Pediatrics, Vanderbilt University Medical Center, Nashville, Tennessee, United States.
Carolynn NallDepartment of Biomedical Informatics, Vanderbilt University Medical Center, Vanderbilt University, Nashville, Tennessee, United States.
Allison B McCoyDepartment of Biomedical Informatics, Vanderbilt University Medical Center, Vanderbilt University, Nashville, Tennessee, United States.
Adam WrightDepartment of Biomedical Informatics, Vanderbilt University Medical Center, Vanderbilt University, Nashville, Tennessee, United States.
Laura ZahnDepartment of Biomedical Informatics, Vanderbilt University Medical Center, Vanderbilt University, Nashville, Tennessee, United States.
Jennifer AndrewsDepartment of Pediatrics, Vanderbilt University Medical Center, Nashville, Tennessee, United States.

Funding

Vanderbilt Clinical Oncology Research Career Development ProgramK12CA090625 · NCI · VANDERBILT UNIVERSITY MEDICAL CENTER · PI Debra L. Friedman, Paula Jill Hurley · 2001 to 2026
$16.9M
VOLT (Vanderbilt Oncology Training Program)T32CA217834 · NCI · VANDERBILT UNIVERSITY MEDICAL CENTER · PI Debra L. Friedman, Douglas B Johnson · 2018 to 2026
$2.7M
NCI NIH HHS K12 CA090625NCI NIH HHS T32 CA217834
6 · The paper itself

Abstract

Background: Ambient artificial intelligence scribes are increasingly integrated into electronic health records to reduce documentation burden, but limited data describe their performance in high-complexity pediatric subspecialties. Objectives: This study aimed to describe the early implementation of Dragon Ambient Experience (DAX) Copilot in a pediatric hematology-oncology division and evaluate patterns of use, documentation time, and workflow effect among providers who adopted the tool. Methods: We reviewed outpatient encounters from January to July 2025 and identified those using DAX. Analyses focused on providers who used DAX more than twice, given low overall adoption. Documentation time (minutes spent in active editing sessions) and note-closure timeliness were compared between DAX and non-DAX encounters. Implementation processes, barriers, and accuracy concerns were qualitatively summarized. Results: Of 11,544 outpatient encounters, 427 (3.7%) involved DAX; 10 of 29 providers used DAX at least once, while 6 (20.7%) used it more than twice. Among repeat users, for shorter encounters (≤40 minutes), median documentation time was lower for DAX versus non-DAX (11 [interquartile range, (IQR): 13] vs. 24 [IQR: 30] minutes), corresponding to an approximately 30% lower documentation time (95% confidence interval [CI], 19.9-38.4%; Conclusion: For repeat users, DAX reduced active documentation time in shorter visits but showed limited benefit for longer encounters and did not improve the timeliness of note closure. Adoption remained modest, underscoring the importance of workflow fit and usability considerations in AI-scribe deployment.

Indexed as

Artificial IntelligenceDocumentationElectronic Health RecordsHematologyMedical OncologyPediatricsChildHumans

Identifiers

PMID42114818
PMCPMC13192280

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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.