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The Power of Modeling in Emergency Preparedness for COVID-19: A Moonshot Moment for Hospitals

Before coronavirus disease 2019 (COVID-19), few hospitals had fully tested emergency surge plans. Uncertainty in the timing and degree of surge complicates planning efforts, putting hospitals at risk of being overwhelmed. Many lack access to hospital-specific, data-driven projections of future patie...

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Autores principales: Safavi, Kyan C., Prestipino, Ann L., Zenteno Langle, Ana Cecilia, Copenhaver, Martin, Hu, Michael, Daily, Bethany, Koehler, Allison, Biddinger, Paul D., Dunn, Peter F.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Cambridge University Press 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8129675/
https://www.ncbi.nlm.nih.gov/pubmed/33588971
http://dx.doi.org/10.1017/dmp.2021.51
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author Safavi, Kyan C.
Prestipino, Ann L.
Zenteno Langle, Ana Cecilia
Copenhaver, Martin
Hu, Michael
Daily, Bethany
Koehler, Allison
Biddinger, Paul D.
Dunn, Peter F.
author_facet Safavi, Kyan C.
Prestipino, Ann L.
Zenteno Langle, Ana Cecilia
Copenhaver, Martin
Hu, Michael
Daily, Bethany
Koehler, Allison
Biddinger, Paul D.
Dunn, Peter F.
author_sort Safavi, Kyan C.
collection PubMed
description Before coronavirus disease 2019 (COVID-19), few hospitals had fully tested emergency surge plans. Uncertainty in the timing and degree of surge complicates planning efforts, putting hospitals at risk of being overwhelmed. Many lack access to hospital-specific, data-driven projections of future patient demand to guide operational planning. Our hospital experienced one of the largest surges in New England. We developed statistical models to project hospitalizations during the first wave of the pandemic. We describe how we used these models to meet key planning objectives. To build the models successfully, we emphasize the criticality of having a team that combines data scientists with frontline operational and clinical leadership. While modeling was a cornerstone of our response, models currently available to most hospitals are built outside of their institution and are difficult to translate to their environment for operational planning. Creating data-driven, hospital-specific, and operationally relevant surge targets and activation triggers should be a major objective of all health systems.
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spelling pubmed-81296752021-05-18 The Power of Modeling in Emergency Preparedness for COVID-19: A Moonshot Moment for Hospitals Safavi, Kyan C. Prestipino, Ann L. Zenteno Langle, Ana Cecilia Copenhaver, Martin Hu, Michael Daily, Bethany Koehler, Allison Biddinger, Paul D. Dunn, Peter F. Disaster Med Public Health Prep Report from the Field Before coronavirus disease 2019 (COVID-19), few hospitals had fully tested emergency surge plans. Uncertainty in the timing and degree of surge complicates planning efforts, putting hospitals at risk of being overwhelmed. Many lack access to hospital-specific, data-driven projections of future patient demand to guide operational planning. Our hospital experienced one of the largest surges in New England. We developed statistical models to project hospitalizations during the first wave of the pandemic. We describe how we used these models to meet key planning objectives. To build the models successfully, we emphasize the criticality of having a team that combines data scientists with frontline operational and clinical leadership. While modeling was a cornerstone of our response, models currently available to most hospitals are built outside of their institution and are difficult to translate to their environment for operational planning. Creating data-driven, hospital-specific, and operationally relevant surge targets and activation triggers should be a major objective of all health systems. Cambridge University Press 2021-02-16 /pmc/articles/PMC8129675/ /pubmed/33588971 http://dx.doi.org/10.1017/dmp.2021.51 Text en © Society for Disaster Medicine and Public Health, Inc. 2021 https://creativecommons.org/licenses/by/4.0/This is an Open Access article, distributed under the terms of the Creative Commons Attribution licence (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted re-use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Report from the Field
Safavi, Kyan C.
Prestipino, Ann L.
Zenteno Langle, Ana Cecilia
Copenhaver, Martin
Hu, Michael
Daily, Bethany
Koehler, Allison
Biddinger, Paul D.
Dunn, Peter F.
The Power of Modeling in Emergency Preparedness for COVID-19: A Moonshot Moment for Hospitals
title The Power of Modeling in Emergency Preparedness for COVID-19: A Moonshot Moment for Hospitals
title_full The Power of Modeling in Emergency Preparedness for COVID-19: A Moonshot Moment for Hospitals
title_fullStr The Power of Modeling in Emergency Preparedness for COVID-19: A Moonshot Moment for Hospitals
title_full_unstemmed The Power of Modeling in Emergency Preparedness for COVID-19: A Moonshot Moment for Hospitals
title_short The Power of Modeling in Emergency Preparedness for COVID-19: A Moonshot Moment for Hospitals
title_sort power of modeling in emergency preparedness for covid-19: a moonshot moment for hospitals
topic Report from the Field
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8129675/
https://www.ncbi.nlm.nih.gov/pubmed/33588971
http://dx.doi.org/10.1017/dmp.2021.51
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