Assessing genetic counseling efficiency with natural language processing

Abstract

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 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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