| Literature DB >> 33673920 |
Benjamin D Pollock1, Rickey E Carter2, Sean C Dowdy3, Shannon M Dunlay4, Elizabeth B Habermann5, Daryl J Kor6, Andrew H Limper7, Hongfang Liu8, Pablo Moreno Franco9, Matthew R Neville10, Katherine H Noe11, John D Poe12, Priya Sampathkumar13, Curtis B Storlie5, Henry H Ting12, Nilay D Shah5.
Abstract
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.Entities:
Year: 2020 PMID: 33673920 DOI: 10.1016/j.mayocp.2020.12.019
Source DB: PubMed Journal: Mayo Clin Proc ISSN: 0025-6196 Impact factor: 7.616