Using Data to Save Lives: The Rockefeller Foundation and Partners Launch $100 Million Precision Public Health Initiative

Wednesday, September 25, 2019

Using Data to Save Lives: The Rockefeller Foundation and Partners Launch $100 Million Precision Public Health Initiative

NEW YORK, 25 September 2019 – The Rockefeller Foundation and leading global health partners announced today a $100 million Precision Public Health initiative to empower community health systems and frontline health workers with the latest data science innovations, including more accurate and precise decision-making tools based on large, integrated datasets, predictive analytics, artificial intelligence, and machine learning. The initiative aims to prevent 6 million deaths in 10 countries by 2030, by enabling frontline health workers with simple, inexpensive data analytic tools.

This initiative will address the reality that data science and new innovations are not reaching the people who need them most. The Precision Public Health initiative will leverage technologies that are transforming health in wealthy countries to dramatically reduce preventable maternal and child deaths. Partners and collaborators for this initiative include UNICEF; the World Health Organization; The Global Fund; the Global Financing Facility supported by The World Bank Group; and Gavi, the Vaccine Alliance.

“We have an unprecedented opportunity to leverage advances in data science and technology that have enriched the lives of society’s most privileged, and transform health for those left behind around the world,” said Dr. Rajiv J. Shah, President of The Rockefeller Foundation. “Working together, we can close the health inequity gap by driving innovation and investment to save millions of lives.”

The initiative will build upon a number of similar efforts on a smaller scale that have already shown encouraging results in applying data science to better deploy life-saving health tools. These include developing real-time risk maps to direct frontline health workers to areas of greatest need, and analyzing non-health data like climate patterns or social media trends to predict and better address health emergencies weeks in advance...

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