Contact PD/PI: Wright, Rosalind J In the past decade, Mount Sinai has invested significantly in the integrative translational initiatives needed to advance precision medicine. This included investments in genomics, high-performance computing, clinical informatics, smart technologies, and computational biology. In concert with institutional strategic goals and increasing community concerns around environmental toxins and social justice, ConduITS, the Mount Sinai Institute for Translational Science, has evolved its approach to precision medicine to include a precision public health framework integrating genomics with key public health domains such as environmental health, social determinants, and big data team science to more effectively address health equity challenges. ConduITS will harness unique strengths in research informatics, digital health, genomics, exposomics (the study of all health relevant environment), and data science to transform engagement and the science of translation to accelerate discoveries into better health outcomes across the lifespan matched to the needs of the diverse populations we serve. We will augment ConduITS’ role in transforming the local, regional, and national translational research enterprise by developing and sharing innovative educational programs, incentivizing transdisciplinary team science in emerging data science fields (e.g., exposomics) and entrepreneurship, as well as training a diversified workforce on emerging clinical and bioinformatics learning health system approaches that leverage big data. Our aims cut across the domains serving as organizing principles for our CTSA hub including: 1) Workforce Development. Evolve learning opportunities promoting transdisciplinary clinical data science and entrepreneurial activities; 2) Diversity. Advance justice, equity, diversity, inclusion, and an antiracist mindset as core tenets of socially responsible research and training; 3) Collaboration and Engagement. Engage diverse stakeholders in all phases of translational research through accelerator models constituted to address priority community concerns and apply an environmental justice, life course, and data science framework to drive community-centered solutions: 4) Informatics. Leverage data science to better coordinate health care delivery with translational research by integrating secure electronic health records (EHRs), patient reported outcomes, clinical trials management systems, and institutional biobanks and data repositories, enabling clinical data science research; 5) Methods and Processes. Innovate and streamline research administrative oversight and processes to minimize roadblocks, ensure quality and integrity; monitor outcomes and garner feedback from stakeholders to facilitate and enhance participation in clinical translational research supported by ConduITS, regional CTSA hubs, and the CTSA national network; and 6) Integration. Incorporate translational research across the lifespan, focusing on perinatal and pediatric research, geriatric populations, and populations impacted by health disparities; accelerating inclusion of environment in life course precision medicine via our innovative Exposomics optional function. . Project Summary/Abstract Page 309 Contact PD/PI: Wright, Rosalind J ConduITS will harness unique strengths in translational research informatics, digital health, and data science to transform research, engagement, and the science of translation to accelerate discoveries into better health outcomes matched to diverse population needs across the lifespan. We will augment ConduITS’ role in transforming the local, regional, and national translational research enterprise to improve health through development and sharing of innovative educational programs, participating in multi-site trials including centralized regulatory oversight, subcontracting and data-sharing, incentivizing transdisciplinary team science in emerging scientific fields (e.g. precision medicine, public health, and exposomics), entrepreneurship, and in training a diversified workforce emphasizing emerging clinical and bioinformatics learning health system approaches and related big data initiatives.

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