Cargando…
COVID-19: Recovery Models for Radiology Departments
The coronavirus disease 2019 (COVID-19) pandemic has greatly affected demand for imaging services, with marked reductions in demand for elective imaging and image-guided interventional procedures. To guide radiology planning and recovery from this unprecedented impact, three recovery models were dev...
Autores principales: | , , , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
American College of Radiology
2020
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7476574/ https://www.ncbi.nlm.nih.gov/pubmed/32979322 http://dx.doi.org/10.1016/j.jacr.2020.09.020 |
_version_ | 1783579727164866560 |
---|---|
author | Guitron, Steven Pianykh, Oleg S. Succi, Marc D. Lang, Min Brink, James |
author_facet | Guitron, Steven Pianykh, Oleg S. Succi, Marc D. Lang, Min Brink, James |
author_sort | Guitron, Steven |
collection | PubMed |
description | The coronavirus disease 2019 (COVID-19) pandemic has greatly affected demand for imaging services, with marked reductions in demand for elective imaging and image-guided interventional procedures. To guide radiology planning and recovery from this unprecedented impact, three recovery models were developed to predict imaging volume over the course of the COVID-19 pandemic: (1) a long-term volume model with three scenarios based on prior disease outbreaks and other historical analogues, to aid in long-term planning when the pandemic was just beginning; (2) a short-term volume model based on the supply-demand approach, leveraging increasingly available COVID-19 data points to predict examination volume on a week-to-week basis; and (3) a next-wave model to estimate the impact from future COVID-19 surges. The authors present these models as techniques that can be used at any stage in an unpredictable pandemic timeline. |
format | Online Article Text |
id | pubmed-7476574 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | American College of Radiology |
record_format | MEDLINE/PubMed |
spelling | pubmed-74765742020-09-08 COVID-19: Recovery Models for Radiology Departments Guitron, Steven Pianykh, Oleg S. Succi, Marc D. Lang, Min Brink, James J Am Coll Radiol Original Article The coronavirus disease 2019 (COVID-19) pandemic has greatly affected demand for imaging services, with marked reductions in demand for elective imaging and image-guided interventional procedures. To guide radiology planning and recovery from this unprecedented impact, three recovery models were developed to predict imaging volume over the course of the COVID-19 pandemic: (1) a long-term volume model with three scenarios based on prior disease outbreaks and other historical analogues, to aid in long-term planning when the pandemic was just beginning; (2) a short-term volume model based on the supply-demand approach, leveraging increasingly available COVID-19 data points to predict examination volume on a week-to-week basis; and (3) a next-wave model to estimate the impact from future COVID-19 surges. The authors present these models as techniques that can be used at any stage in an unpredictable pandemic timeline. American College of Radiology 2020-11 2020-09-07 /pmc/articles/PMC7476574/ /pubmed/32979322 http://dx.doi.org/10.1016/j.jacr.2020.09.020 Text en © 2020 American College of Radiology. Since January 2020 Elsevier has created a COVID-19 resource centre with free information in English and Mandarin on the novel coronavirus COVID-19. The COVID-19 resource centre is hosted on Elsevier Connect, the company's public news and information website. Elsevier hereby grants permission to make all its COVID-19-related research that is available on the COVID-19 resource centre - including this research content - immediately available in PubMed Central and other publicly funded repositories, such as the WHO COVID database with rights for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for free by Elsevier for as long as the COVID-19 resource centre remains active. |
spellingShingle | Original Article Guitron, Steven Pianykh, Oleg S. Succi, Marc D. Lang, Min Brink, James COVID-19: Recovery Models for Radiology Departments |
title | COVID-19: Recovery Models for Radiology Departments |
title_full | COVID-19: Recovery Models for Radiology Departments |
title_fullStr | COVID-19: Recovery Models for Radiology Departments |
title_full_unstemmed | COVID-19: Recovery Models for Radiology Departments |
title_short | COVID-19: Recovery Models for Radiology Departments |
title_sort | covid-19: recovery models for radiology departments |
topic | Original Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7476574/ https://www.ncbi.nlm.nih.gov/pubmed/32979322 http://dx.doi.org/10.1016/j.jacr.2020.09.020 |
work_keys_str_mv | AT guitronsteven covid19recoverymodelsforradiologydepartments AT pianykholegs covid19recoverymodelsforradiologydepartments AT succimarcd covid19recoverymodelsforradiologydepartments AT langmin covid19recoverymodelsforradiologydepartments AT brinkjames covid19recoverymodelsforradiologydepartments |