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Petri Nets Validation of Markovian Models of Emergency Department Arrivals
Modeling of hospital’s Emergency Departments (ED) is vital for optimisation of health services offered to patients that shows up at an ED requiring treatments with different level of emergency. In this paper we present a modeling study whose contribution is twofold: first, based on a dataset relativ...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7324247/ http://dx.doi.org/10.1007/978-3-030-51831-8_11 |
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author | Ballarini, Paolo Duma, Davide Horváth, Andras Aringhieri, Roberto |
author_facet | Ballarini, Paolo Duma, Davide Horváth, Andras Aringhieri, Roberto |
author_sort | Ballarini, Paolo |
collection | PubMed |
description | Modeling of hospital’s Emergency Departments (ED) is vital for optimisation of health services offered to patients that shows up at an ED requiring treatments with different level of emergency. In this paper we present a modeling study whose contribution is twofold: first, based on a dataset relative to the ED of an Italian hospital, we derive different kinds of Markovian models capable to reproduce, at different extents, the statistical character of dataset arrivals; second, we validate the derived arrivals model by interfacing it with a Petri net model of the services an ED patient undergoes. The empirical assessment of a few key performance indicators allowed us to validate some of the derived arrival process model, thus confirming that they can be used for predicting the performance of an ED. |
format | Online Article Text |
id | pubmed-7324247 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
record_format | MEDLINE/PubMed |
spelling | pubmed-73242472020-06-30 Petri Nets Validation of Markovian Models of Emergency Department Arrivals Ballarini, Paolo Duma, Davide Horváth, Andras Aringhieri, Roberto Application and Theory of Petri Nets and Concurrency Article Modeling of hospital’s Emergency Departments (ED) is vital for optimisation of health services offered to patients that shows up at an ED requiring treatments with different level of emergency. In this paper we present a modeling study whose contribution is twofold: first, based on a dataset relative to the ED of an Italian hospital, we derive different kinds of Markovian models capable to reproduce, at different extents, the statistical character of dataset arrivals; second, we validate the derived arrivals model by interfacing it with a Petri net model of the services an ED patient undergoes. The empirical assessment of a few key performance indicators allowed us to validate some of the derived arrival process model, thus confirming that they can be used for predicting the performance of an ED. 2020-06-02 /pmc/articles/PMC7324247/ http://dx.doi.org/10.1007/978-3-030-51831-8_11 Text en © Springer Nature Switzerland AG 2020 This article is made available via the PMC Open Access Subset for unrestricted research re-use and secondary analysis in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the World Health Organization (WHO) declaration of COVID-19 as a global pandemic. |
spellingShingle | Article Ballarini, Paolo Duma, Davide Horváth, Andras Aringhieri, Roberto Petri Nets Validation of Markovian Models of Emergency Department Arrivals |
title | Petri Nets Validation of Markovian Models of Emergency Department Arrivals |
title_full | Petri Nets Validation of Markovian Models of Emergency Department Arrivals |
title_fullStr | Petri Nets Validation of Markovian Models of Emergency Department Arrivals |
title_full_unstemmed | Petri Nets Validation of Markovian Models of Emergency Department Arrivals |
title_short | Petri Nets Validation of Markovian Models of Emergency Department Arrivals |
title_sort | petri nets validation of markovian models of emergency department arrivals |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7324247/ http://dx.doi.org/10.1007/978-3-030-51831-8_11 |
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