Enhanced family history-based algorithms increase the identification of individuals meeting criteria for genetic testing of hereditary cancer syndromes but would not reduce disparities on their own

Bradshaw, Richard L.; Kawamoto, Kensaku; Bather, Jemar R.; Goodman, Melody S.; Kohlmann, Wendy K.; Chavez-Yenter, Daniel; Volkmar, Molly; Monahan, Rachel; Kaphingst, Kimberly A.; Del Fiol, Guilherme

doi:10.1016/j.jbi.2023.104568

Journal of Biomedical Informatics

2024

DOI: 10.1016/j.jbi.2023.104568

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Enhanced family history-based algorithms increase the identification of individuals meeting criteria for genetic testing of hereditary cancer syndromes but would not reduce disparities on their own

Richard L. Bradshaw,

Kensaku Kawamoto,

Jemar R. Bather

et al.

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Cited by 2 publications

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Fairness and inclusion methods for biomedical informatics research

Visweswaran,

Luo,

Peleg

2024

Journal of Biomedical Informatics

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Fairness and inclusion methods for biomedical informatics research

Visweswaran,

Luo,

Peleg

2024

Journal of Biomedical Informatics

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Identification of Individuals With Hereditary Cancer Risk Through Multiple Data Sources: A Population-Based Method Using the GARDE Platform and The Utah Population Database

Del Fiol,

Madsen,

Bradshaw

et al. 2024

JCO Clin Cancer Inform

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PURPOSE The GARDE platform uses family history reported in the electronic health record (EHR) to systematically identify eligible patients for genetic testing for hereditary cancer syndromes. The goal of this study was to evaluate the change in effectiveness of GARDE to identify eligible individuals when more comprehensive family history data are provided, thus quantifying the impact of underdocumentation. METHODS A cohort of 133,764 patients at the University of Utah Health was analyzed with GARDE comparing identification rates using EHR data versus EHR plus data from a statewide population database, the Utah Population Database (UPDB). RESULTS Compared with EHR alone, EHR + UPDB increased the rate of individuals eligible for genetic testing from 4.1% to 9.2%. In the 44,692 individuals with the most comprehensive family history, eligibility more than quadrupled from 4.6% (EHR alone) to 19.3% (EHR + UPDB). The increase was significant across all demographics, but disparities still remained for historically marginalized minorities (9.2%-13.9% in non-White races compared with 19.7% in White races). CONCLUSION Augmenting EHR data with family history data from the UPDB substantially improved the detection of individuals eligible for genetic testing of hereditary cancer syndromes in all subgroups. This underscores the importance of improving methods for acquiring family history, in person or in silico. However, these increases did not ameliorate disparities. Continuous disparities are unlikely to be explained by incomplete family history alone and may also be because susceptibility genes, risk variants, and screening guidelines were discovered and developed largely in White races. Addressing disparities will require intentional data collection of family history in historically marginalized minorities and the promotion of genetic and risk assessment studies in more diverse populations to ensure equity and health care.

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Enhanced family history-based algorithms increase the identification of individuals meeting criteria for genetic testing of hereditary cancer syndromes but would not reduce disparities on their own

Cited by 2 publications

References 19 publications

Fairness and inclusion methods for biomedical informatics research

Fairness and inclusion methods for biomedical informatics research

Identification of Individuals With Hereditary Cancer Risk Through Multiple Data Sources: A Population-Based Method Using the GARDE Platform and The Utah Population Database

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