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Emergency Department Capacity Planning: A Recurrent Neural Network and Simulation Approach
Emergency departments (EDs) play a vital role in the whole healthcare system as they are the first point of care in hospitals for urgent and critically ill patients. Therefore, effective management of hospital's ED is crucial in improving the quality of the healthcare service. The effectiveness...
Autores principales: | , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Hindawi
2019
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6881773/ https://www.ncbi.nlm.nih.gov/pubmed/31827585 http://dx.doi.org/10.1155/2019/4359719 |
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author | Nas, Serkan Koyuncu, Melik |
author_facet | Nas, Serkan Koyuncu, Melik |
author_sort | Nas, Serkan |
collection | PubMed |
description | Emergency departments (EDs) play a vital role in the whole healthcare system as they are the first point of care in hospitals for urgent and critically ill patients. Therefore, effective management of hospital's ED is crucial in improving the quality of the healthcare service. The effectiveness depends on how efficiently the hospital resources are used, particularly under budget constraints. Simulation modeling is one of the best methods to optimize resources and needs inputs such as patients' arrival time, patient's length of stay (LOS), and the route of patients in the ED. This study develops a simulation model to determine the optimum number of beds in an ED by minimizing the patients' LOS. The hospital data are analyzed, and patients' LOS and the route of patients in the ED are determined. To determine patients' arrival times, the features associated with patients' arrivals at ED are identified. Mean arrival rate is used as a feature in addition to climatic and temporal variables. The exhaustive feature-selection method has been used to determine the best subset of the features, and the mean arrival rate is determined as one of the most significant features. This study is executed using the one-year ED arrival data together with five-year (43.824 study hours) ED arrival data to improve the accuracy of predictions. Furthermore, ten different machine learning (ML) algorithms are used utilizing the same best subset of these features. After a tenfold cross-validation experiment, based on mean absolute percentage error (MAPE), the stateful long short-term memory (LSTM) model performed better than other models with an accuracy of 47%, followed by the decision tree and random forest methods. Using the simulation method, the LOS has been minimized by 7% and the number of beds at the ED has been optimized. |
format | Online Article Text |
id | pubmed-6881773 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2019 |
publisher | Hindawi |
record_format | MEDLINE/PubMed |
spelling | pubmed-68817732019-12-11 Emergency Department Capacity Planning: A Recurrent Neural Network and Simulation Approach Nas, Serkan Koyuncu, Melik Comput Math Methods Med Research Article Emergency departments (EDs) play a vital role in the whole healthcare system as they are the first point of care in hospitals for urgent and critically ill patients. Therefore, effective management of hospital's ED is crucial in improving the quality of the healthcare service. The effectiveness depends on how efficiently the hospital resources are used, particularly under budget constraints. Simulation modeling is one of the best methods to optimize resources and needs inputs such as patients' arrival time, patient's length of stay (LOS), and the route of patients in the ED. This study develops a simulation model to determine the optimum number of beds in an ED by minimizing the patients' LOS. The hospital data are analyzed, and patients' LOS and the route of patients in the ED are determined. To determine patients' arrival times, the features associated with patients' arrivals at ED are identified. Mean arrival rate is used as a feature in addition to climatic and temporal variables. The exhaustive feature-selection method has been used to determine the best subset of the features, and the mean arrival rate is determined as one of the most significant features. This study is executed using the one-year ED arrival data together with five-year (43.824 study hours) ED arrival data to improve the accuracy of predictions. Furthermore, ten different machine learning (ML) algorithms are used utilizing the same best subset of these features. After a tenfold cross-validation experiment, based on mean absolute percentage error (MAPE), the stateful long short-term memory (LSTM) model performed better than other models with an accuracy of 47%, followed by the decision tree and random forest methods. Using the simulation method, the LOS has been minimized by 7% and the number of beds at the ED has been optimized. Hindawi 2019-11-15 /pmc/articles/PMC6881773/ /pubmed/31827585 http://dx.doi.org/10.1155/2019/4359719 Text en Copyright © 2019 Serkan Nas and Melik Koyuncu. http://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Research Article Nas, Serkan Koyuncu, Melik Emergency Department Capacity Planning: A Recurrent Neural Network and Simulation Approach |
title | Emergency Department Capacity Planning: A Recurrent Neural Network and Simulation Approach |
title_full | Emergency Department Capacity Planning: A Recurrent Neural Network and Simulation Approach |
title_fullStr | Emergency Department Capacity Planning: A Recurrent Neural Network and Simulation Approach |
title_full_unstemmed | Emergency Department Capacity Planning: A Recurrent Neural Network and Simulation Approach |
title_short | Emergency Department Capacity Planning: A Recurrent Neural Network and Simulation Approach |
title_sort | emergency department capacity planning: a recurrent neural network and simulation approach |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6881773/ https://www.ncbi.nlm.nih.gov/pubmed/31827585 http://dx.doi.org/10.1155/2019/4359719 |
work_keys_str_mv | AT nasserkan emergencydepartmentcapacityplanningarecurrentneuralnetworkandsimulationapproach AT koyuncumelik emergencydepartmentcapacityplanningarecurrentneuralnetworkandsimulationapproach |