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Deployment of an Interdisciplinary Predictive Analytics Task Force to Inform Hospital Operational Decision-Making During the COVID-19 Pandemic

2020; Elsevier BV; Volume: 96; Issue: 3 Linguagem: Inglês

10.1016/j.mayocp.2020.12.019

1942-5546

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, Nilay D. Shah, Kimberly K. Amrami, Robert Domnick, Ethan P. Heinzen, Karen Helfinstine, Ajay Jayakumar, Patrick W. Johnson, Camille Knepper, David Marcelletti, Mindy Mickelson, Ricardo L. Rojas, Mark St. George, Aaron J. Tande, Kelli Walvatne, Phichet Wutthisirisart,

COVID-19 epidemiological studies

In 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.