Predicting Hospitalisation In Older Patients Attended In The Emergency Department Using Classical And Artificial Intelligence Mathematical Strategies

Background: The ageing population poses a significant challenge for health and social care systems. Emergency Departments (EDs) frequently experience overcrowding due to the high volume of patients and the limited availability of hospital beds. From the perspective of bed management planners, knowin...

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Autores: Larrea Aguirre, Nere, García Gutiérrez, Susana, Miró, Óscar, Jacob, Javier, Llorens Soriano, Pere, Burillo Putze, Guillermo, Fernández Alonso, Cesáreo, Alquezar Arbé, Aitor, Aguiló, Sira, Montero Pérez, Francisco Javier, Noceda Bermejo, José, Maza Vera, María Teresa, García García, Ángel, Ezponda, Patxi, González del Castillo, Juan
Tipo de documento: artigo
Estado:Versão publicada
Data de publicação:2025
País:España
Recursos:Universidad de Barcelona
Repositório:Dipòsit Digital de la UB
OAI Identifier:oai:diposit.ub.edu:2445/222386
Acesso em linha:https://hdl.handle.net/2445/222386
Access Level:Acceso aberto
Palavra-chave:Persones grans
Intel·ligència artificial en medicina
Older people
Medical artificial intelligence
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spelling Predicting Hospitalisation In Older Patients Attended In The Emergency Department Using Classical And Artificial Intelligence Mathematical StrategiesLarrea Aguirre, NereGarcía Gutiérrez, SusanaMiró, ÓscarJacob, JavierLlorens Soriano, PereBurillo Putze, GuillermoFernández Alonso, CesáreoAlquezar Arbé, AitorAguiló, SiraMontero Pérez, Francisco JavierNoceda Bermejo, JoséMaza Vera, María TeresaGarcía García, ÁngelEzponda, PatxiGonzález del Castillo, JuanPersones gransIntel·ligència artificial en medicinaOlder peopleMedical artificial intelligenceBackground: The ageing population poses a significant challenge for health and social care systems. Emergency Departments (EDs) frequently experience overcrowding due to the high volume of patients and the limited availability of hospital beds. From the perspective of bed management planners, knowing the likelihood of a patient's admission at the earliest stage of care can be highly beneficial for effective resource planning. The goal of our study was to develop a prediction model to identify patients with a high probability of being admitted to the hospital. Methods: We included all patients aged 65 or older who were treated over the course of one week in 52 Spanish Emergency Departments. The data collected included socio-demographic characteristics, baseline functional status, comorbidities, vital signs, chronic treatments, and laboratory test results. The primary outcome variable was hospital admission. We applied several mathematical strategies to develop the most accurate model for identifying high-risk patients likely to require hospitalisation. Results: The most effective model was developed using a random forest algorithm, incorporating various variables available during patient care in the ED. The probability of admission was categorised into four risk groups: 2.19 %, 15.65 %, 25.09 %, and 57.08 %. The resulting model had a sensitivity of 0.88. Conclusion: We developed a high-sensitivity score for hospital admission in older patients treated in the ED to enhance the management of patient flow by bed planners. This score will help prevent ED overcrowding, which compromises patient safety and disrupts the healthcare system.Office of Academic Resources, Chulalongkorn University - DIGITAL COMMONS JOURNALS2025info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionapplication/pdfhttps://hdl.handle.net/2445/222386Articles publicats en revistes (Institut d'lnvestigació Biomèdica de Bellvitge (IDIBELL))reponame:Dipòsit Digital de la UBinstname:Universidad de BarcelonaInglésReproducció del document publicat a: https://doi.org/10.56808/2586-940X.1141Journal of Health Research, 2025, vol. 39, num. 3https://doi.org/10.56808/2586-940X.1141cc by (c) Larrea Aguirre, Nere et al, 2024http://creativecommons.org/licenses/by/3.0/es/info:eu-repo/semantics/openAccessoai:diposit.ub.edu:2445/2223862026-05-27T06:46:51Z
dc.title.none.fl_str_mv Predicting Hospitalisation In Older Patients Attended In The Emergency Department Using Classical And Artificial Intelligence Mathematical Strategies
title Predicting Hospitalisation In Older Patients Attended In The Emergency Department Using Classical And Artificial Intelligence Mathematical Strategies
spellingShingle Predicting Hospitalisation In Older Patients Attended In The Emergency Department Using Classical And Artificial Intelligence Mathematical Strategies
Larrea Aguirre, Nere
Persones grans
Intel·ligència artificial en medicina
Older people
Medical artificial intelligence
title_short Predicting Hospitalisation In Older Patients Attended In The Emergency Department Using Classical And Artificial Intelligence Mathematical Strategies
title_full Predicting Hospitalisation In Older Patients Attended In The Emergency Department Using Classical And Artificial Intelligence Mathematical Strategies
title_fullStr Predicting Hospitalisation In Older Patients Attended In The Emergency Department Using Classical And Artificial Intelligence Mathematical Strategies
title_full_unstemmed Predicting Hospitalisation In Older Patients Attended In The Emergency Department Using Classical And Artificial Intelligence Mathematical Strategies
title_sort Predicting Hospitalisation In Older Patients Attended In The Emergency Department Using Classical And Artificial Intelligence Mathematical Strategies
dc.creator.none.fl_str_mv Larrea Aguirre, Nere
García Gutiérrez, Susana
Miró, Óscar
Jacob, Javier
Llorens Soriano, Pere
Burillo Putze, Guillermo
Fernández Alonso, Cesáreo
Alquezar Arbé, Aitor
Aguiló, Sira
Montero Pérez, Francisco Javier
Noceda Bermejo, José
Maza Vera, María Teresa
García García, Ángel
Ezponda, Patxi
González del Castillo, Juan
author Larrea Aguirre, Nere
author_facet Larrea Aguirre, Nere
García Gutiérrez, Susana
Miró, Óscar
Jacob, Javier
Llorens Soriano, Pere
Burillo Putze, Guillermo
Fernández Alonso, Cesáreo
Alquezar Arbé, Aitor
Aguiló, Sira
Montero Pérez, Francisco Javier
Noceda Bermejo, José
Maza Vera, María Teresa
García García, Ángel
Ezponda, Patxi
González del Castillo, Juan
author_role author
author2 García Gutiérrez, Susana
Miró, Óscar
Jacob, Javier
Llorens Soriano, Pere
Burillo Putze, Guillermo
Fernández Alonso, Cesáreo
Alquezar Arbé, Aitor
Aguiló, Sira
Montero Pérez, Francisco Javier
Noceda Bermejo, José
Maza Vera, María Teresa
García García, Ángel
Ezponda, Patxi
González del Castillo, Juan
author2_role author
author
author
author
author
author
author
author
author
author
author
author
author
author
dc.subject.none.fl_str_mv Persones grans
Intel·ligència artificial en medicina
Older people
Medical artificial intelligence
topic Persones grans
Intel·ligència artificial en medicina
Older people
Medical artificial intelligence
description Background: The ageing population poses a significant challenge for health and social care systems. Emergency Departments (EDs) frequently experience overcrowding due to the high volume of patients and the limited availability of hospital beds. From the perspective of bed management planners, knowing the likelihood of a patient's admission at the earliest stage of care can be highly beneficial for effective resource planning. The goal of our study was to develop a prediction model to identify patients with a high probability of being admitted to the hospital. Methods: We included all patients aged 65 or older who were treated over the course of one week in 52 Spanish Emergency Departments. The data collected included socio-demographic characteristics, baseline functional status, comorbidities, vital signs, chronic treatments, and laboratory test results. The primary outcome variable was hospital admission. We applied several mathematical strategies to develop the most accurate model for identifying high-risk patients likely to require hospitalisation. Results: The most effective model was developed using a random forest algorithm, incorporating various variables available during patient care in the ED. The probability of admission was categorised into four risk groups: 2.19 %, 15.65 %, 25.09 %, and 57.08 %. The resulting model had a sensitivity of 0.88. Conclusion: We developed a high-sensitivity score for hospital admission in older patients treated in the ED to enhance the management of patient flow by bed planners. This score will help prevent ED overcrowding, which compromises patient safety and disrupts the healthcare system.
publishDate 2025
dc.date.none.fl_str_mv 2025
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv https://hdl.handle.net/2445/222386
url https://hdl.handle.net/2445/222386
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.relation.none.fl_str_mv Reproducció del document publicat a: https://doi.org/10.56808/2586-940X.1141
Journal of Health Research, 2025, vol. 39, num. 3
https://doi.org/10.56808/2586-940X.1141
dc.rights.none.fl_str_mv cc by (c) Larrea Aguirre, Nere et al, 2024
http://creativecommons.org/licenses/by/3.0/es/
info:eu-repo/semantics/openAccess
rights_invalid_str_mv cc by (c) Larrea Aguirre, Nere et al, 2024
http://creativecommons.org/licenses/by/3.0/es/
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv Office of Academic Resources, Chulalongkorn University - DIGITAL COMMONS JOURNALS
publisher.none.fl_str_mv Office of Academic Resources, Chulalongkorn University - DIGITAL COMMONS JOURNALS
dc.source.none.fl_str_mv Articles publicats en revistes (Institut d'lnvestigació Biomèdica de Bellvitge (IDIBELL))
reponame:Dipòsit Digital de la UB
instname:Universidad de Barcelona
instname_str Universidad de Barcelona
reponame_str Dipòsit Digital de la UB
collection Dipòsit Digital de la UB
repository.name.fl_str_mv
repository.mail.fl_str_mv
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score 15.812429

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