Applying data science methodologies with artificial intelligence variant reinterpretation to map and estimate genetic disorder prevalence utilizing clinical data.

Data science methodologies can be utilized to ascertain and analyze clinical genetic data that is often unstructured and rarely used outside of patient encounters. Genetic variants from all genetic testing resulting to a large pediatric healthcare system for a 5-year period were obtained and reinterpreted utilizing the previously validated Franklin© Artificial Intelligence (AI). Using PowerBI©, the data were further matched to patients in the electronic healthcare record to associate with demographic data to generate a variant data table and mapped by ZIP codes. Three thousand and sixty-five variants were identified and 98% were matched to patients with geographic data. Franklin© changed the interpretation for 24% of variants. One hundred and fifty-six clinically actionable variant reinterpretations were made. A total of 739 Mendelian genetic disorders were identified with disorder prevalence estimation. Mapping of variants demonstrated hot-spots for pathogenic genetic variation such as PEX6-associated Zellweger Spectrum Disorder. Seven patients were identified with Bardet-Biedl syndrome and seven patients with Rett syndrome amenable to newly FDA-approved therapeutics. Utilizing readily available software we developed a database and Exploratory Data Analysis (EDA) methodology enabling us to systematically reinterpret variants, estimate variant prevalence, identify conditions amenable to new treatments, and localize geographies enriched for pathogenic variants.

Survey of Physician Well-Being and Health Behaviors at an Academic Medical Center.

PURPOSE: The study aims were to ascertain, among attending and house staff at a single academic medical center, the prevalence of and risk factors for psychiatric symptoms and disorders and for personal health behaviors. METHODS: A self-administered, anonymous 72-item survey of physicians was conducted in February 2003. RESULTS: Response rate was 37.6%. The prevalence of current depressive symptoms was 29%. AUDIT scores consistent with high likelihood of harmful alcohol consumption were prevalent in 6%. Almost 5% acknowledged use of sedatives or hypnotics without a prescription in the prior 12 months. Characteristics independently associated with current depressive symptoms included: living alone, full time salaried faculty status, not having a primary care physician, female sex, and age < 50 years. Factors associated with high risk of harmful alcohol consumption included: male sex, house staff status, and not being exclusively heterosexual. CONCLUSIONS: The prevalence of recent depressive symptoms among responding physicians was nearly 30%. Interventions to engage physicians in primary care relationships and social support to confidentially disclose potentially stigmatizing characteristics may facilitate earlier case finding of those at risk for depression, suicide, and substance abuse.