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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...
Autores principales: | , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Cambridge University Press
2021
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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. |
format | Online Article Text |
id | pubmed-8129675 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Cambridge University Press |
record_format | MEDLINE/PubMed |
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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