Evidence map›Paper›PMID 37722603›Full record

ArticleApplied clinical informatics2024

Vanderbilt Electronic Health Record Voice Assistant Supports Clinicians.

Yaa A Kumah-Crystal, Christoph U Lehmann, Dan Albert, Tim Coffman, Hala Alaw, Sydney Roth, Alexandra Manoni, Peter Shave, Kevin B Johnson

Open access · bronzeAbstract read
In one paragraph

Article in Applied clinical informatics, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
1.1field-weighted citation impact, top 16% of its field
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

3 citing papers in PubMed, 3 citations in OpenAlex.

  1. Review
  2. Article
  3. Article
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 at 3 institutions in 1 country.

Yaa A Kumah-CrystalDepartment of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, Tennessee, United States.
Christoph U LehmannClinical Informatics Center, University of Texas Southwestern Medical Center, Dallas, Texas, United States.
Dan AlbertDepartment of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, Tennessee, United States.
Tim CoffmanDepartment of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, Tennessee, United States.
Hala AlawDepartment of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, Tennessee, United States.
Sydney RothDepartment of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, Tennessee, United States.
Alexandra ManoniDepartment of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, Tennessee, United States.
Peter ShaveDepartment of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, Tennessee, United States.
Kevin B JohnsonDepartment of Biomedical Informatics, University of Pennsylvania, Richards, Philadelphia, Pennsylvania, United States.
Vanderbilt University Medical Center · USThe University of Texas Southwestern Medical Center · USUniversity of Pennsylvania · US

Funding

UT Southwestern Center for Translational MedicineUL1TR003163 · NCATS · UT SOUTHWESTERN MEDICAL CENTER · PI TOTO, ROBERT DANIEL · 2021 to 2025
$39.3M
NCATS NIH HHS UL1 TR003163
6 · The paper itself

Abstract

backgroundElectronic health records (EHRs) present navigation challenges due to time-consuming searches across segmented data. Voice assistants can improve clinical workflows by allowing natural language queries and contextually aware navigation of the EHR.

objectivesTo develop a voice-mediated EHR assistant and interview providers to inform its future refinement.

methodsThe Vanderbilt EHR Voice Assistant (VEVA) was developed as a responsive web application and designed to accept voice inputs and execute the appropriate EHR commands. Fourteen providers from Vanderbilt Medical Center were recruited to participate in interactions with VEVA and to share their experience with the technology. The purpose was to evaluate VEVA's overall usability, gather qualitative feedback, and detail suggestions for enhancing its performance.

resultsVEVA's mean system usability scale score was 81 based on the 14 providers' evaluations, which was above the standard 50th percentile score of 68. For all five summaries evaluated (overview summary, A1C results, blood pressure, weight, and health maintenance), most providers offered a positive review of VEVA. Several providers suggested modifications to make the technology more useful in their practice, ranging from summarizing current medications to changing VEVA's speech rate. Eight of the providers (64%) reported they would be willing to use VEVA in its current form.

conclusionOur EHR voice assistant technology was deemed usable by most providers. With further improvements, voice assistant tools such as VEVA have the potential to improve workflows and serve as a useful adjunct tool in health care.

Indexed as

Electronic Health RecordsSoftwareLanguageTechnology

Identifiers

PMID37722603
PMCPMC10937093
OpenAlexW4386821824

What OpenQuestion holds

Textmetadata
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.