Development and validation of an advanced data analytics model to support strategic point-of-care testing utilization decisions in the emergency department

Aims: This study was carried out to address potential uncertainties about how point-of-care testing (POCT) improves patients’ outcomes in emergency department (ED). The main aim was to develop and validate a model based on advanced data analytics to evaluate POCT’s impact in patients’ outcomes and E...

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Autores: León Justel, Antonio, Jiménez Barragán, Marta, Navarro Bustos, Carmen, Martin Pérez, Salomon, Garrido Castilla, José M., Morales Barroso, Isabel M., Oltra Hostalet, Fernando, Fernández Gallardo, María F., Diaz Luque, Ana, Eugenio Pizarro, Antonia, Luque Cid, Antonio, Sánchez Mora, Catalina
Tipo de recurso: artículo
Fecha de publicación:2025
País:España
Institución:Universidad Loyola Andalucía
Repositorio:Brújula
OAI Identifier:oai:repositorio.uloyola.es:20.500.12412/6780
Acceso en línea:https://hdl.handle.net/20.500.12412/6780
Access Level:acceso abierto
Palabra clave:Overcrowding, point-of-care, emergency department, simulation, advanced data analytics model
Overcrowding
Point-of-care
Emergency department
Simulation
Advanced data analytics model
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spelling Development and validation of an advanced data analytics model to support strategic point-of-care testing utilization decisions in the emergency departmentLeón Justel, AntonioJiménez Barragán, MartaNavarro Bustos, CarmenMartin Pérez, SalomonGarrido Castilla, José M.Morales Barroso, Isabel M.Oltra Hostalet, FernandoFernández Gallardo, María F.Diaz Luque, AnaEugenio Pizarro, AntoniaLuque Cid, AntonioSánchez Mora, CatalinaOvercrowding, point-of-care, emergency department, simulation, advanced data analytics modelOvercrowdingPoint-of-careEmergency departmentSimulationAdvanced data analytics modelAims: This study was carried out to address potential uncertainties about how point-of-care testing (POCT) improves patients’ outcomes in emergency department (ED). The main aim was to develop and validate a model based on advanced data analytics to evaluate POCT’s impact in patients’ outcomes and ED patients’ flow. Materials and methods: We built a discrete event model simulation (DEMS) to represent work flow of a Spanish ED. Historical data from ED, published evidence and expert estimates were used to support the model. Different scenarios of progressive utilization of POCT in patients’ care triaged as Emergency Severity Index (ESI) level 3 were compared to standard-of-care (SoC) in terms of time-to-first medical intervention (TFMI), time-to-disposition decision (TDD), total length of stay (LoS) and patient workflow. Results: In POCT maximum utilization scenario (60% of ESI-3 patients), time savings reached 27.44, 14.58 and 13.96 min of TFMI, 55.77, 13.64 and 13.97 min of TDD and 89.60, 18.55 and 13.98 min of LoS (ESI-3, 4 and 5 patients, respectively). Statistically significant reductions were found for all time outcomes in every POCT scenario for ESI-3, 4 and 5 patients. Internal valid ation didn’t show differences between model results and real data. Limitations: Simplifications were made due to theoretical nature of computer-simulation mod els. Some input data and assumptions regarding individual process times were derived from interviews. Theoretical distributions were assumed; other activities outside the ED were consid ered as a disruption to the system; finally, findings reflect experience of a single ED. Conclusions: Advanced data analytics has become a useful tool in analyzing lots of processes. Our study showed that advanced data analytics has become an exceptional tool in clinical laboratories and exemplifies how POCT incorporation in ED for care of ESI-3 patients reduces physicians’ workRoche Diagnostics2025info:eu-repo/semantics/articlehttps://hdl.handle.net/20.500.12412/6780reponame:Brújulainstname:Universidad Loyola AndalucíaIngléshttps://doi.org/10.1080/13696998.2025.2508659http://creativecommons.org/licenses/by-nc-nd/4.0/info:eu-repo/semantics/openAccessoai:repositorio.uloyola.es:20.500.12412/67802026-06-24T12:48:37Z
dc.title.none.fl_str_mv Development and validation of an advanced data analytics model to support strategic point-of-care testing utilization decisions in the emergency department
title Development and validation of an advanced data analytics model to support strategic point-of-care testing utilization decisions in the emergency department
spellingShingle Development and validation of an advanced data analytics model to support strategic point-of-care testing utilization decisions in the emergency department
León Justel, Antonio
Overcrowding, point-of-care, emergency department, simulation, advanced data analytics model
Overcrowding
Point-of-care
Emergency department
Simulation
Advanced data analytics model
title_short Development and validation of an advanced data analytics model to support strategic point-of-care testing utilization decisions in the emergency department
title_full Development and validation of an advanced data analytics model to support strategic point-of-care testing utilization decisions in the emergency department
title_fullStr Development and validation of an advanced data analytics model to support strategic point-of-care testing utilization decisions in the emergency department
title_full_unstemmed Development and validation of an advanced data analytics model to support strategic point-of-care testing utilization decisions in the emergency department
title_sort Development and validation of an advanced data analytics model to support strategic point-of-care testing utilization decisions in the emergency department
dc.creator.none.fl_str_mv León Justel, Antonio
Jiménez Barragán, Marta
Navarro Bustos, Carmen
Martin Pérez, Salomon
Garrido Castilla, José M.
Morales Barroso, Isabel M.
Oltra Hostalet, Fernando
Fernández Gallardo, María F.
Diaz Luque, Ana
Eugenio Pizarro, Antonia
Luque Cid, Antonio
Sánchez Mora, Catalina
author León Justel, Antonio
author_facet León Justel, Antonio
Jiménez Barragán, Marta
Navarro Bustos, Carmen
Martin Pérez, Salomon
Garrido Castilla, José M.
Morales Barroso, Isabel M.
Oltra Hostalet, Fernando
Fernández Gallardo, María F.
Diaz Luque, Ana
Eugenio Pizarro, Antonia
Luque Cid, Antonio
Sánchez Mora, Catalina
author_role author
author2 Jiménez Barragán, Marta
Navarro Bustos, Carmen
Martin Pérez, Salomon
Garrido Castilla, José M.
Morales Barroso, Isabel M.
Oltra Hostalet, Fernando
Fernández Gallardo, María F.
Diaz Luque, Ana
Eugenio Pizarro, Antonia
Luque Cid, Antonio
Sánchez Mora, Catalina
author2_role author
author
author
author
author
author
author
author
author
author
author
dc.subject.none.fl_str_mv Overcrowding, point-of-care, emergency department, simulation, advanced data analytics model
Overcrowding
Point-of-care
Emergency department
Simulation
Advanced data analytics model
topic Overcrowding, point-of-care, emergency department, simulation, advanced data analytics model
Overcrowding
Point-of-care
Emergency department
Simulation
Advanced data analytics model
description Aims: This study was carried out to address potential uncertainties about how point-of-care testing (POCT) improves patients’ outcomes in emergency department (ED). The main aim was to develop and validate a model based on advanced data analytics to evaluate POCT’s impact in patients’ outcomes and ED patients’ flow. Materials and methods: We built a discrete event model simulation (DEMS) to represent work flow of a Spanish ED. Historical data from ED, published evidence and expert estimates were used to support the model. Different scenarios of progressive utilization of POCT in patients’ care triaged as Emergency Severity Index (ESI) level 3 were compared to standard-of-care (SoC) in terms of time-to-first medical intervention (TFMI), time-to-disposition decision (TDD), total length of stay (LoS) and patient workflow. Results: In POCT maximum utilization scenario (60% of ESI-3 patients), time savings reached 27.44, 14.58 and 13.96 min of TFMI, 55.77, 13.64 and 13.97 min of TDD and 89.60, 18.55 and 13.98 min of LoS (ESI-3, 4 and 5 patients, respectively). Statistically significant reductions were found for all time outcomes in every POCT scenario for ESI-3, 4 and 5 patients. Internal valid ation didn’t show differences between model results and real data. Limitations: Simplifications were made due to theoretical nature of computer-simulation mod els. Some input data and assumptions regarding individual process times were derived from interviews. Theoretical distributions were assumed; other activities outside the ED were consid ered as a disruption to the system; finally, findings reflect experience of a single ED. Conclusions: Advanced data analytics has become a useful tool in analyzing lots of processes. Our study showed that advanced data analytics has become an exceptional tool in clinical laboratories and exemplifies how POCT incorporation in ED for care of ESI-3 patients reduces physicians’ work
publishDate 2025
dc.date.none.fl_str_mv 2025
dc.type.none.fl_str_mv info:eu-repo/semantics/article
format article
dc.identifier.none.fl_str_mv https://hdl.handle.net/20.500.12412/6780
url https://hdl.handle.net/20.500.12412/6780
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.relation.none.fl_str_mv https://doi.org/10.1080/13696998.2025.2508659
dc.rights.none.fl_str_mv http://creativecommons.org/licenses/by-nc-nd/4.0/
info:eu-repo/semantics/openAccess
rights_invalid_str_mv http://creativecommons.org/licenses/by-nc-nd/4.0/
eu_rights_str_mv openAccess
dc.source.none.fl_str_mv reponame:Brújula
instname:Universidad Loyola Andalucía
instname_str Universidad Loyola Andalucía
reponame_str Brújula
collection Brújula
repository.name.fl_str_mv
repository.mail.fl_str_mv
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score 15.811543

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