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Simulation to Predict Effect of Citywide Events on Emergency Department Operations
Medical emergency preparedness has been an issue of medical relevance since the advent of hospital care. Studies have simulated emergency department (ED) overcrowding but not yet characterized effects of large-scale, planned events that drastically alter a city’s demography, such as in Philadelphia,...
Autores principales: | , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Wolters Kluwer Health
2017
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6132789/ https://www.ncbi.nlm.nih.gov/pubmed/30229148 http://dx.doi.org/10.1097/pq9.0000000000000008 |
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author | Ferraro, Nicole M. Day, Theodore Eugene |
author_facet | Ferraro, Nicole M. Day, Theodore Eugene |
author_sort | Ferraro, Nicole M. |
collection | PubMed |
description | Medical emergency preparedness has been an issue of medical relevance since the advent of hospital care. Studies have simulated emergency department (ED) overcrowding but not yet characterized effects of large-scale, planned events that drastically alter a city’s demography, such as in Philadelphia, Pennsylvania during the 2015 World Meeting of Families. A discrete event simulation of the ED at the Children’s Hospital of Philadelphia was designed and validated using past data. The model was used to predict the patient length of stay (LOS) and number of admitted patients if the arrival stream to the ED were to change by 50% from typical arrivals in either direction. We compared the model’s estimations with data produced during the papal visit that had 39.65% fewer patient arrivals. For validation, the simulated mean LOS was 226.1 ± 173.3 minutes (mean ± SD) for all patients and 352.1 ± 170.3 minutes for admitted patients. Real-world mean LOSs for the fiscal year 2014 were 230.6 ± 134.8 for all patients and 345.0 ± 147.7 for admitted patients. For the estimation of the World Meeting of Families, the simulation accurately estimated the LOS of both patients overall and admitted patients within 10%. These results show that it is possible to use simulations to project the patient flow effects in EDs in case of large-scale events. Providing efficient care is essential to emergency operations, and projections of demand are crucial for targeting appropriate changes during large-scale events. Analysis of validated computer simulations allows for evidence-based decision making in a complex clinical environment. |
format | Online Article Text |
id | pubmed-6132789 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2017 |
publisher | Wolters Kluwer Health |
record_format | MEDLINE/PubMed |
spelling | pubmed-61327892018-09-18 Simulation to Predict Effect of Citywide Events on Emergency Department Operations Ferraro, Nicole M. Day, Theodore Eugene Pediatr Qual Saf Individual QI Projects from Single Institutions Medical emergency preparedness has been an issue of medical relevance since the advent of hospital care. Studies have simulated emergency department (ED) overcrowding but not yet characterized effects of large-scale, planned events that drastically alter a city’s demography, such as in Philadelphia, Pennsylvania during the 2015 World Meeting of Families. A discrete event simulation of the ED at the Children’s Hospital of Philadelphia was designed and validated using past data. The model was used to predict the patient length of stay (LOS) and number of admitted patients if the arrival stream to the ED were to change by 50% from typical arrivals in either direction. We compared the model’s estimations with data produced during the papal visit that had 39.65% fewer patient arrivals. For validation, the simulated mean LOS was 226.1 ± 173.3 minutes (mean ± SD) for all patients and 352.1 ± 170.3 minutes for admitted patients. Real-world mean LOSs for the fiscal year 2014 were 230.6 ± 134.8 for all patients and 345.0 ± 147.7 for admitted patients. For the estimation of the World Meeting of Families, the simulation accurately estimated the LOS of both patients overall and admitted patients within 10%. These results show that it is possible to use simulations to project the patient flow effects in EDs in case of large-scale events. Providing efficient care is essential to emergency operations, and projections of demand are crucial for targeting appropriate changes during large-scale events. Analysis of validated computer simulations allows for evidence-based decision making in a complex clinical environment. Wolters Kluwer Health 2017-01-09 /pmc/articles/PMC6132789/ /pubmed/30229148 http://dx.doi.org/10.1097/pq9.0000000000000008 Text en Copyright © 2017 the Author(s). Published by Wolters Kluwer Health, Inc. All rights reserved. This is an open-access article distributed under the terms of the Creative Commons Attribution-Non Commercial-No Derivatives License 4.0 (CC-BY-NC-ND) (http://creativecommons.org/licenses/by-nc-nd/4.0/) , where it is permissible to download and share the work provided it is properly cited. The work cannot be changed in any way or used commercially. |
spellingShingle | Individual QI Projects from Single Institutions Ferraro, Nicole M. Day, Theodore Eugene Simulation to Predict Effect of Citywide Events on Emergency Department Operations |
title | Simulation to Predict Effect of Citywide Events on Emergency Department Operations |
title_full | Simulation to Predict Effect of Citywide Events on Emergency Department Operations |
title_fullStr | Simulation to Predict Effect of Citywide Events on Emergency Department Operations |
title_full_unstemmed | Simulation to Predict Effect of Citywide Events on Emergency Department Operations |
title_short | Simulation to Predict Effect of Citywide Events on Emergency Department Operations |
title_sort | simulation to predict effect of citywide events on emergency department operations |
topic | Individual QI Projects from Single Institutions |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6132789/ https://www.ncbi.nlm.nih.gov/pubmed/30229148 http://dx.doi.org/10.1097/pq9.0000000000000008 |
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