A simulation model for predicting hospital occupancy for Covid-19 using archetype analysis
COVID-19 pandemic has sent millions of people to hospitals worldwide, exhausting on many occasions the capacity of healthcare systems to provide care patients required to survive. Although several epidemiological research works have contributed a variety of models and approaches to anticipate the pa...
| Main Authors: | , , , , , , , |
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| Format: | Article |
| Language: | English |
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Elsevier
2023-11-01
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| Series: | Healthcare Analytics |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2772442523000643 |
| _version_ | 1827917405674799104 |
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| author | Eduardo Redondo Vittorio Nicoletta Valérie Bélanger José P. Garcia-Sabater Paolo Landa Julien Maheut Juan A. Marin-Garcia Angel Ruiz |
| author_facet | Eduardo Redondo Vittorio Nicoletta Valérie Bélanger José P. Garcia-Sabater Paolo Landa Julien Maheut Juan A. Marin-Garcia Angel Ruiz |
| author_sort | Eduardo Redondo |
| collection | DOAJ |
| description | COVID-19 pandemic has sent millions of people to hospitals worldwide, exhausting on many occasions the capacity of healthcare systems to provide care patients required to survive. Although several epidemiological research works have contributed a variety of models and approaches to anticipate the pandemic spread, very few have tried to translate the output of these models into hospital service requirements, particularly in terms of bed occupancy, a key question for hospital managers. This paper proposes a tool for predicting the current and future occupancy associated with COVID-19 patients of a hospital to help managers make informed decisions to maximize the availability of hospitalization and intensive care unit (ICU) beds and ensure adequate access to services for confirmed COVID-19 patients. The proposed tool uses a discrete event simulation approach that uses archetypes (i.e., empirical models of trajectories) extracted from empirical analysis of actual patient trajectories. Archetypes can be fitted to trajectories observed in different regions or to the particularities of current and forthcoming variants using a rather small amount of data. Numerical experiments on realistic instances demonstrate the accuracy of the tool’s predictions and illustrate how it can support managers in their daily decisions concerning the system’s capacity and ensure patients the access the resources they require. |
| first_indexed | 2024-03-13T03:27:19Z |
| format | Article |
| id | doaj.art-60dc99c2711c47c3b39d8d5061fce643 |
| institution | Directory Open Access Journal |
| issn | 2772-4425 |
| language | English |
| last_indexed | 2024-03-13T03:27:19Z |
| publishDate | 2023-11-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Healthcare Analytics |
| spelling | doaj.art-60dc99c2711c47c3b39d8d5061fce6432023-06-25T04:44:22ZengElsevierHealthcare Analytics2772-44252023-11-013100197A simulation model for predicting hospital occupancy for Covid-19 using archetype analysisEduardo Redondo0Vittorio Nicoletta1Valérie Bélanger2José P. Garcia-Sabater3Paolo Landa4Julien Maheut5Juan A. Marin-Garcia6Angel Ruiz7Faculty of Business Administration, Université Laval, Quebec (Quebec), G1K 7P4, Canada; Interuniversity Research Center on Enterprise Networks, Logistics and Transportation (CIRRELT), CanadaFaculty of Business Administration, Université Laval, Quebec (Quebec), G1K 7P4, Canada; Interuniversity Research Center on Enterprise Networks, Logistics and Transportation (CIRRELT), CanadaInteruniversity Research Center on Enterprise Networks, Logistics and Transportation (CIRRELT), Canada; Department of Logistics and Operations Management, HEC Montréal, 3000 chemin de la Cote Sainte-Catherine, Montreal (Quebec), H3T 2A7, CanadaROGLE, Department of Organización de Empresas, Universitat Politècnica de València, Valencia s/n, 46021 Valencia, SpainFaculty of Business Administration, Université Laval, Quebec (Quebec), G1K 7P4, Canada; Interuniversity Research Center on Enterprise Networks, Logistics and Transportation (CIRRELT), CanadaROGLE, Department of Organización de Empresas, Universitat Politècnica de València, Valencia s/n, 46021 Valencia, SpainROGLE, Department of Organización de Empresas, Universitat Politècnica de València, Valencia s/n, 46021 Valencia, SpainFaculty of Business Administration, Université Laval, Quebec (Quebec), G1K 7P4, Canada; Interuniversity Research Center on Enterprise Networks, Logistics and Transportation (CIRRELT), Canada; Correspondence to: Pavillon Palasis-Prince, Université Laval, 2325 Rue de la Terrasse, Quebec City, QC, Canada G1V 0A6.COVID-19 pandemic has sent millions of people to hospitals worldwide, exhausting on many occasions the capacity of healthcare systems to provide care patients required to survive. Although several epidemiological research works have contributed a variety of models and approaches to anticipate the pandemic spread, very few have tried to translate the output of these models into hospital service requirements, particularly in terms of bed occupancy, a key question for hospital managers. This paper proposes a tool for predicting the current and future occupancy associated with COVID-19 patients of a hospital to help managers make informed decisions to maximize the availability of hospitalization and intensive care unit (ICU) beds and ensure adequate access to services for confirmed COVID-19 patients. The proposed tool uses a discrete event simulation approach that uses archetypes (i.e., empirical models of trajectories) extracted from empirical analysis of actual patient trajectories. Archetypes can be fitted to trajectories observed in different regions or to the particularities of current and forthcoming variants using a rather small amount of data. Numerical experiments on realistic instances demonstrate the accuracy of the tool’s predictions and illustrate how it can support managers in their daily decisions concerning the system’s capacity and ensure patients the access the resources they require.http://www.sciencedirect.com/science/article/pii/S2772442523000643Healthcare managementCOVID-19Bed managementDiscrete event simulationArchetype analysis |
| spellingShingle | Eduardo Redondo Vittorio Nicoletta Valérie Bélanger José P. Garcia-Sabater Paolo Landa Julien Maheut Juan A. Marin-Garcia Angel Ruiz A simulation model for predicting hospital occupancy for Covid-19 using archetype analysis Healthcare Analytics Healthcare management COVID-19 Bed management Discrete event simulation Archetype analysis |
| title | A simulation model for predicting hospital occupancy for Covid-19 using archetype analysis |
| title_full | A simulation model for predicting hospital occupancy for Covid-19 using archetype analysis |
| title_fullStr | A simulation model for predicting hospital occupancy for Covid-19 using archetype analysis |
| title_full_unstemmed | A simulation model for predicting hospital occupancy for Covid-19 using archetype analysis |
| title_short | A simulation model for predicting hospital occupancy for Covid-19 using archetype analysis |
| title_sort | simulation model for predicting hospital occupancy for covid 19 using archetype analysis |
| topic | Healthcare management COVID-19 Bed management Discrete event simulation Archetype analysis |
| url | http://www.sciencedirect.com/science/article/pii/S2772442523000643 |
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