Computational Decision Support for the COVID-19 Healthcare Coalition
Computing in science & engineering (Print)
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. I n the early months of 2020, the SARS-CoV-2 Coronavirus took the world by surprise, resulting in the COVID-19 pandemic that has caused significant loss of lives and challenged the sustainability of our health care systems. In mid-March, it became obvious that government and communities had to react immediately. Under the lead of the Mayo Clinic and The MITRE Corporation, the COVID-19 Healthcare Coalition (C19HCC) was established as a coordinated public-interest, private-sector response. The coalition brought healthcare organizations, technology firms, nonprofits, academia, and startups to support supply chains, inform coordinated social policies, and provide data-driven insights to protect people and preserve the healthcare delivery system. The coalition quickly reached more than 1000 member organizations, many of them working in computational fields. Although the efforts focused on the United States, we had several international partners who not only observed, but also contributed to the efforts. This article summarizes selected research results and lessons learned when highly diverse and heterogenous organizations bring their data and computational infrastructure together to provide computational decision support in a new problem domain with daily changing scientific insights, as is the case with COVID-19. Of particular interest for this journal is the work of the analytics working groups who had to obtain and align distributed and diverse data, orchestrate heterogenous modeling approaches, use machine learning (ML) and artificial intelligence (AI) methods to identify trends, apply simulations implementing latest research insights, and visualize the results using dashboards that allow decision makers in federal and state governments to understand the results, leading to actionable recommendations.