Computational decision support for the covid-19 healthcare coalition

Abstract

The COVID-19 Healthcare Coalition was established as a private sector-led response to the COVID-19 pandemic. Its purpose was to bring together healthcare organizations, technology firms, nonprofits, academia, and startups to preserve the healthcare delivery system and help protect U.S. populations by providing data-driven, real-time insights that improve outcomes. This required the coalition to obtain, align, and orchestrate many heterogeneous data sources and present this data on dashboards in a format that was understandable and useful to decision makers. To do this, the coalition employed an ensemble approach to analysis, combining machine learning algorithms together with theory-based simulations, allowing prognosis to provide computational decision support rooted in science and engineering.