Evidence map›Paper›PMID 41211696›Full record

ArticleJournal of the American Medical Informatics Association : JAMIA2026

Assessing genetic counseling efficiency with natural language processing.

Michelle H Nguyen, Carolyn D Applegate, Brittney Murray, Ayah Zirikly, Crystal Tichnell, Catherine Gordon, Lisa R Yanek, Cynthia A James, Casey Overby Taylor

Abstract read
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In one paragraph

Article in Journal of the American Medical Informatics Association : JAMIA, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. 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.

Michelle H NguyenInstitute for Computational Medicine, Johns Hopkins Whiting School of Engineering, Baltimore, MD 21218, United States.ORCID 0000-0002-7689-646X
Carolyn D ApplegateDepartment of Genetic Medicine, The Johns Hopkins University School of Medicine, Baltimore, MD 21205, United States.
Brittney MurrayDivision of Cardiology, Department of Medicine, The Johns Hopkins University School of Medicine, Baltimore, MD 21205, United States.ORCID 0000-0001-7870-8584
Ayah ZiriklyCenter for Language and Speech Processing, Johns Hopkins Whiting School of Engineering, Baltimore, MD 21218, United States.
Crystal TichnellDivision of Cardiology, Department of Medicine, The Johns Hopkins University School of Medicine, Baltimore, MD 21205, United States.ORCID 0000-0003-3465-294X
Catherine GordonDivision of Cardiology, Department of Medicine, The Johns Hopkins University School of Medicine, Baltimore, MD 21205, United States.
Lisa R YanekDepartment of Medicine, Division of General Internal Medicine, The Johns Hopkins University School of Medicine, Baltimore, MD 21205, United States.
Cynthia A JamesDivision of Cardiology, Department of Medicine, The Johns Hopkins University School of Medicine, Baltimore, MD 21205, United States.ORCID 0000-0003-0854-0418
Casey Overby TaylorInstitute for Computational Medicine, Johns Hopkins Whiting School of Engineering, Baltimore, MD 21218, United States.ORCID 0000-0001-9302-5968

Funding

Clinical Decision Support for Unsolicited Genomic ResultsR35HG010714 · NHGRI · JOHNS HOPKINS UNIVERSITY · PI TAYLOR, CASEY OVERBY · 2020 to 2024
$2.8M
Randomized clinical trial of the sequence of genetic counseling and testing to optimize efficiency, patient empowerment and engagement, and medical adherence for diverse genetic testing indicationsR01HG011902 · NHGRI · JOHNS HOPKINS UNIVERSITY · PI JAMES, CYNTHIA A · 2021 to 2024
$1.3M
National Human Genome Research Institute of the National Institutes of Health R01HG011902National Human Genome Research Institute of the National Institutes of Health R35HG010714NHGRI NIH HHSNHGRI NIH HHS R01 HG011902NHGRI NIH HHS R35 HG010714NIH HHS R01HG011902NIH HHS R35HG010714
6 · The paper itself

Abstract

objectiveTo build natural language processing (NLP) strategies to characterize measures of genetic counseling (GC) efficiency and classify measures according to phase of GC (pre- or post-genetic testing). MATERIALS AND

methodsThis study selected and annotated 800 GC notes from 7 clinical specialties in a large academic medical center for NLP model development and validation. The NLP approaches extracted GC efficiency measures, including direct and indirect time and GC phase. The models were then applied to 24 102 GC notes collected from January 2016 through December 2023.

resultsNLP approaches performed well (F1 scores of 0.95 and 0.90 for direct time in GC and GC phase classification, respectively). Our findings showed median direct time in GC of 50 minutes, with significant differences in direct time distributions observed across clinical specialties, time periods (2016-2019 or 2020-2023), delivery modes (in person or telehealth), and GC phase. DISCUSSION: As referrals to GC increase, there is increasing pressure to improve efficiency. Our NLP strategy was used to generate and summarize real-world evidence of GC time for 7 clinical specialties. These approaches enable future research on the impact of interventions intended to improve GC efficiency.

conclusionThis work demonstrated the practical value of NLP to provide a useful and scalable strategy to generate real world evidence of GC efficiency. Principles presented in this work may also be valuable for health services research in other practice areas.

Indexed as

Genetic CounselingNatural Language ProcessingElectronic Health RecordsHumansclinician efficiencygenetic counselingnatural language processing

Identifiers

PMID41211696
PMCPMC12743353

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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.