• Assessing genetic counseling e...

*Result*: Assessing genetic counseling efficiency with natural language processing.

Title:
Assessing genetic counseling efficiency with natural language processing.
Authors:
Nguyen MH; Institute for Computational Medicine, Johns Hopkins Whiting School of Engineering, Baltimore, MD 21218, United States.; Department of Biomedical Engineering, The Johns Hopkins University School of Medicine, Baltimore, MD 21205, United States., Applegate CD; Department of Genetic Medicine, The Johns Hopkins University School of Medicine, Baltimore, MD 21205, United States., Murray B; Division of Cardiology, Department of Medicine, The Johns Hopkins University School of Medicine, Baltimore, MD 21205, United States., Zirikly A; Center for Language and Speech Processing, Johns Hopkins Whiting School of Engineering, Baltimore, MD 21218, United States., Tichnell C; Division of Cardiology, Department of Medicine, The Johns Hopkins University School of Medicine, Baltimore, MD 21205, United States., Gordon C; Division of Cardiology, Department of Medicine, The Johns Hopkins University School of Medicine, Baltimore, MD 21205, United States., Yanek LR; Department of Medicine, Division of General Internal Medicine, The Johns Hopkins University School of Medicine, Baltimore, MD 21205, United States., James CA; Division of Cardiology, Department of Medicine, The Johns Hopkins University School of Medicine, Baltimore, MD 21205, United States., Taylor CO; Institute for Computational Medicine, Johns Hopkins Whiting School of Engineering, Baltimore, MD 21218, United States.; Department of Biomedical Engineering, The Johns Hopkins University School of Medicine, Baltimore, MD 21205, United States.; Department of Medicine, Division of General Internal Medicine, The Johns Hopkins University School of Medicine, Baltimore, MD 21205, United States.
Source:
Journal of the American Medical Informatics Association : JAMIA [J Am Med Inform Assoc] 2026 Feb 01; Vol. 33 (2), pp. 295-303.
Publication Type:
Journal Article
Language:
English
Journal Info:
Publisher: Oxford University Press Country of Publication: England NLM ID: 9430800 Publication Model: Print Cited Medium: Internet ISSN: 1527-974X (Electronic) Linking ISSN: 10675027 NLM ISO Abbreviation: J Am Med Inform Assoc Subsets: MEDLINE
Imprint Name(s):
Publication: 2015- : Oxford : Oxford University Press
Original Publication: Philadelphia, PA : Hanley & Belfus, c1993-
MeSH Terms:
Natural Language Processing* , Genetic Counseling*, Humans ; Electronic Health Records
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Grant Information:
R01 HG011902 United States HG NHGRI NIH HHS; R35 HG010714 United States HG NHGRI NIH HHS; R35HG010714 National Human Genome Research Institute of the National Institutes of Health; R01HG011902 National Human Genome Research Institute of the National Institutes of Health; United States HG NHGRI NIH HHS; R01HG011902 United States NH NIH HHS; R35HG010714 United States NH NIH HHS
Contributed Indexing:
Keywords: clinician efficiency; genetic counseling; natural language processing
Entry Date(s):
Date Created: 20251110 Date Completed: 20260127 Latest Revision: 20260202
Update Code:
20260202
PubMed Central ID:
PMC12743353
DOI:
10.1093/jamia/ocaf190
PMID:
41211696
Database:
MEDLINE

*Further Information*

*Objective: To 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 Methods: This 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.
Results: NLP 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.
Conclusion: This 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.
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