• Mayo Clinic COVID-19 Predictive Analytics Task Force, Benjamin D Pollock, Rickey E Carter, Sean C Dowdy, Shannon M Dunlay, Elizabeth B Habermann, Daryl J Kor, Andrew H Limper, Hongfang Liu, Pablo Moreno Franco, Matthew R Neville, Katherine H Noe, John D Poe, Priya Sampathkumar, Curtis B Storlie, Henry H Ting, and Nilay D Shah.
• Department of Quality, Experience, and Affordability, Mayo Clinic, Rochester, MN; Department of Health Sciences Research, Mayo Clinic, Jacksonville, FL; Department of Neurology, Mayo Clinic, Phoenix, AZ. Electronic address: Pollock.Benjamin@Mayo.Edu.
• Mayo Clin. Proc. 2021 Mar 1; 96 (3): 690698690-698.

AbstractIn March 2020, our institution developed an interdisciplinary predictive analytics task force to provide coronavirus disease 2019 (COVID-19) hospital census forecasting to help clinical leaders understand the potential impacts on hospital operations. As the situation unfolded into a pandemic, our task force provided predictive insights through a structured set of visualizations and key messages that have helped the practice to anticipate and react to changing operational needs and opportunities. The framework shared here for the deployment of a COVID-19 predictive analytics task force could be adapted for effective implementation at other institutions to provide evidence-based messaging for operational decision-making. For hospitals without such a structure, immediate consideration may be warranted in light of the devastating COVID-19 third-wave which has arrived for winter 2020-2021.Copyright © 2021 Mayo Foundation for Medical Education and Research. Published by Elsevier Inc. All rights reserved.

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