P714: Enhancing laboratory efficiency: Implementation of Revvity Transcribe AI for automated data entry in newborn screening
20 years of experience.A majority of the respondents reported interpreting more than 100 variants per year (71.6%), with 26.8% reporting reinterpreting more than 26 variants per year.80.5% of responders reported that insufficient data was frequently the cause for classifying variants as VUS.Overall, 78.4% of respondents reported engaging with functional evidence and most (77.4%)evaluating functional data for variant classification in the clinical setting.More than half of the respondents reported functional data was rarely or never available for their variants of interest (67.4%).A majority (90.5%) of respondents considered insufficient quality metrics or confidence in the accuracy of functional data to be a barrier to using functional evidence.When asked about options of interventions that could improve use of functional evidence, almost half of the respondents noted access to primary functional data (42.6%), and standardized interpretation of functional data through ClinVar (45.8%) would significantly improve their use.Conclusion: Overall, the results of this study provided insights on the functional evidence usage for individuals involved in variant interpretation.It also showed a demand for a robust database that provides more accessible information on the quality and accuracy of functional evidence to promote use of functional evidence in variant interpretation.
Authors
- Madhuri Hegde (ORCID: https://orcid.org/0000-0002-4500-0368)
- Ephrem Chin
- Antti Mikkonen
- Andrew Millerschoen
- Chris Murphy
- Jacob Decker
- Ken Sredzienski
- Justin Anderle
- Maria Ralph
Publication Details
- Journal
- Genetics in Medicine Open
- Published
- 2025-01-01
- DOI
- https://doi.org/10.1016/j.gimo.2025.103083
- Primary Topic
- Artificial Intelligence in Healthcare and Education
- Type
- article
- Field-Weighted Citation Impact
- 0.00