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Discrete-Event Simulation Modeling of Critical Care Flow: New Hospital, Old Challenges
OBJECTIVES: As the demand for critical care beds rises each year, hospitals must be able to adapt. Delayed transfer of care reduces available critical care capacity and increases occupancy. The use of mathematic modeling within healthcare systems has the ability to aid planning of resources. Discret...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Lippincott Williams & Wilkins
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7491890/ https://www.ncbi.nlm.nih.gov/pubmed/32984824 http://dx.doi.org/10.1097/CCE.0000000000000174 |
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author | Williams, Elizabeth Szakmany, Tamas Spernaes, Izabela Muthuswamy, Babu Holborn, Penny |
author_facet | Williams, Elizabeth Szakmany, Tamas Spernaes, Izabela Muthuswamy, Babu Holborn, Penny |
author_sort | Williams, Elizabeth |
collection | PubMed |
description | OBJECTIVES: As the demand for critical care beds rises each year, hospitals must be able to adapt. Delayed transfer of care reduces available critical care capacity and increases occupancy. The use of mathematic modeling within healthcare systems has the ability to aid planning of resources. Discrete-event simulation models can determine the optimal number of critical care beds required and simulate different what-if scenarios. DESIGN: Complex discrete-event simulation model was developed using a warm-up period of 30 days and ran for 30 trials against a 2-year period with the mean calculated for the runs. A variety of different scenarios were investigated to determine the effects of increasing capacity, increasing demand, and reduction of proportion and length of delayed transfer of care out of the ICU. SETTING: Combined data from two ICUs in United Kingdom. PATIENTS: The model was developed using 1,728 patient records and was validated against an independent dataset of 2,650 patients. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: During model validation, the average bed utilization and admittance rate were equal to the real-world data. In the what-if scenarios, we found that increasing bed numbers from 23 to 28 keeping the arrival rate stable reduces the average occupancy rate to 70%. We found that the projected 4% yearly increase in admissions could overwhelm even the 28-bedded unit, without change in the delayed transfer of care episodes. Reduction in the proportion of patients experiencing delayed transfer of care had the biggest effect on occupancy rates, time spent at full capacity, and average bed utilization. CONCLUSIONS: Using discrete-event simulation of commonly available baseline patient flow and patient care data produces reproducible models. Reducing the proportion of patients with delayed transfer of care had a greater effect in reducing occupancy levels than simply increasing bed numbers even when demand is increased. |
format | Online Article Text |
id | pubmed-7491890 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | Lippincott Williams & Wilkins |
record_format | MEDLINE/PubMed |
spelling | pubmed-74918902020-09-24 Discrete-Event Simulation Modeling of Critical Care Flow: New Hospital, Old Challenges Williams, Elizabeth Szakmany, Tamas Spernaes, Izabela Muthuswamy, Babu Holborn, Penny Crit Care Explor Methodology OBJECTIVES: As the demand for critical care beds rises each year, hospitals must be able to adapt. Delayed transfer of care reduces available critical care capacity and increases occupancy. The use of mathematic modeling within healthcare systems has the ability to aid planning of resources. Discrete-event simulation models can determine the optimal number of critical care beds required and simulate different what-if scenarios. DESIGN: Complex discrete-event simulation model was developed using a warm-up period of 30 days and ran for 30 trials against a 2-year period with the mean calculated for the runs. A variety of different scenarios were investigated to determine the effects of increasing capacity, increasing demand, and reduction of proportion and length of delayed transfer of care out of the ICU. SETTING: Combined data from two ICUs in United Kingdom. PATIENTS: The model was developed using 1,728 patient records and was validated against an independent dataset of 2,650 patients. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: During model validation, the average bed utilization and admittance rate were equal to the real-world data. In the what-if scenarios, we found that increasing bed numbers from 23 to 28 keeping the arrival rate stable reduces the average occupancy rate to 70%. We found that the projected 4% yearly increase in admissions could overwhelm even the 28-bedded unit, without change in the delayed transfer of care episodes. Reduction in the proportion of patients experiencing delayed transfer of care had the biggest effect on occupancy rates, time spent at full capacity, and average bed utilization. CONCLUSIONS: Using discrete-event simulation of commonly available baseline patient flow and patient care data produces reproducible models. Reducing the proportion of patients with delayed transfer of care had a greater effect in reducing occupancy levels than simply increasing bed numbers even when demand is increased. Lippincott Williams & Wilkins 2020-09-14 /pmc/articles/PMC7491890/ /pubmed/32984824 http://dx.doi.org/10.1097/CCE.0000000000000174 Text en Copyright © 2020 The Authors. Published by Wolters Kluwer Health, Inc. on behalf of the Society of Critical Care Medicine. This is an open-access article distributed under the terms of the Creative Commons Attribution-Non Commercial-No Derivatives License 4.0 (CCBY-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 without permission from the journal. |
spellingShingle | Methodology Williams, Elizabeth Szakmany, Tamas Spernaes, Izabela Muthuswamy, Babu Holborn, Penny Discrete-Event Simulation Modeling of Critical Care Flow: New Hospital, Old Challenges |
title | Discrete-Event Simulation Modeling of Critical Care Flow: New Hospital, Old Challenges |
title_full | Discrete-Event Simulation Modeling of Critical Care Flow: New Hospital, Old Challenges |
title_fullStr | Discrete-Event Simulation Modeling of Critical Care Flow: New Hospital, Old Challenges |
title_full_unstemmed | Discrete-Event Simulation Modeling of Critical Care Flow: New Hospital, Old Challenges |
title_short | Discrete-Event Simulation Modeling of Critical Care Flow: New Hospital, Old Challenges |
title_sort | discrete-event simulation modeling of critical care flow: new hospital, old challenges |
topic | Methodology |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7491890/ https://www.ncbi.nlm.nih.gov/pubmed/32984824 http://dx.doi.org/10.1097/CCE.0000000000000174 |
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