Assessing genetic counseling efficiency with natural language processing.

ABSTRACT:

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

SUBMITTER: Nguyen MH 

PROVIDER: S-EPMC12743353 | biostudies-literature | 2026 Feb

REPOSITORIES: biostudies-literature