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Using Open Data and Algorithms to Motivate Equitable Community Health Care - Lessons Learned & Application to COVID-19 Response
While increasing amounts of open, public, and digital data have the potential to transform health and healthcare response, the systems that have generated these large volumes of data are deeply and historically unequal. In the field of digital health, actors from the local (e.g. NYC Health Data) to global (e.g. the World Health Organization’s Commission on Social Determinants of Health) have worked to identify health disparities. Organizations working at the intersection of data science and community health must take a multifaceted approach that balances health domain and data science expertise to create tools and systems that motivate the use of health equity data and create algorithms that directly fight the inequalities presented. In this session, DataKind and partners will showcase the analytical frameworks and data science solutions they’ve created to support health equity practices in community health, and discuss how those solutions have been applied during the COVID-19 pandemic.

Join speakers representing the New York City, the United States, and the global health communities from the Arnhold Institute for Global Health, the CONVINCE Consortium, Dimagi, Living Goods & Medic Mobile for this engaging panel conversation moderated by DataKind.

Mar 11, 2021 09:00 AM in Eastern Time (US and Canada)

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