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

Pollock, Benjamin D / Carter, Rickey E / Dowdy, Sean C / Dunlay, Shannon M / Habermann, Elizabeth B / Kor, Daryl J / Limper, Andrew H / Liu, Hongfang / Franco, Pablo Moreno / Neville, Matthew R / Noe, Katherine H / Poe, John D / Sampathkumar, Priya / Storlie, Curtis B / Ting, Henry H / Shah, Nilay D

Mayo Clinic proceedings

2020  Volume 96, Issue 3, Page(s) 690–698

Abstract: ... The framework shared here for the deployment of a COVID-19 predictive analytics task force could be adapted ... In March 2020, our institution developed an interdisciplinary predictive analytics task force ... our task force provided predictive insights through a structured set of visualizations and key messages that have ...

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.
MeSH term(s) COVID-19/epidemiology ; COVID-19/therapy ; Decision Making ; Disease Management ; Forecasting ; Hospitals/statistics & numerical data ; Humans ; Intensive Care Units/statistics & numerical data ; Pandemics ; SARS-CoV-2
Language English
Publishing date 2020-12-30
Publishing country England
Document type Journal Article ; Review
ZDB-ID 124027-4
ISSN 1942-5546 ; 0025-6196
ISSN (online) 1942-5546
ISSN 0025-6196
DOI 10.1016/j.mayocp.2020.12.019
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