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

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
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article

P714: Enhancing laboratory efficiency: Implementation of Revvity Transcribe AI for automated data entry in newborn screening

Madhuri Hegde, Ephrem Chin, Antti Mikkonen, Andrew Millerschoen et al.
Genetics in Medicine Open
Artificial Intelligence in Healthcare and Education
article

P714: Enhancing laboratory efficiency: Implementation of Revvity Transcribe AI for automated data entry in newborn screening

Madhuri Hegde, Ephrem Chin, Antti Mikkonen, Andrew Millerschoen, Chris Murphy, Jacob Decker, Ken Sredzienski, Justin Anderle, Maria Ralph
article en

Abstract

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.

Genetics in Medicine OpenVol. 3
Openalex Percentile: Top 97%
Artificial Intelligence in Healthcare and Education
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