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Emergency Department Trends and Outcomes: A Data-Driven Analysis

aut.relation.endpage509
aut.relation.journalStudies in Health Technology and Informatics
aut.relation.startpage505
aut.relation.volume329
dc.contributor.authorRasouli Panah, Hamidreza
dc.contributor.authorMadanian, Samaneh
dc.contributor.authorYu, Jian
dc.contributor.authorIjadi Maghsoodi, Abtin
dc.date.accessioned2025-08-14T22:28:47Z
dc.date.available2025-08-14T22:28:47Z
dc.date.issued2025-08
dc.description.abstractThis study analyzes hospital Emergency Department (ED) data from 2016 to 2024, examining trends in Waiting Times (WT), Lengths of Stay (LoS), and patient outcomes. WT and LoS increased after the pandemic, indicating operational issues, even though patient volumes remained consistent throughout the whole period. Longer delays were observed on weekends and throughout the colder months, according to temporal analysis. Younger age groups and NZ European/Pākehā and Māori populations dominated ED visits, with older patients experiencing higher mortality rates. Mortality analysis revealed an inverse relationship between WT and patient mortality, with extended LoS correlating with increased severity. The results emphasize the use of predictive analytics to enhance healthcare equity and optimize ED operations.
dc.identifier.citationStudies in Health Technology and Informatics, ISSN: 0926-9630 (Print); 0926-9630 (Online), IOS Press, 329, 505-509. doi: 10.3233/SHTI250891
dc.identifier.doi10.3233/SHTI250891
dc.identifier.issn0926-9630
dc.identifier.issn0926-9630
dc.identifier.urihttp://hdl.handle.net/10292/19679
dc.languageeng
dc.publisherIOS Press
dc.relation.urihttps://ebooks.iospress.nl/doi/10.3233/SHTI250891
dc.rights© 2025 The Authors. This article is published online with Open Access by IOS Press and distributed under the terms of the Creative Commons Attribution Non-Commercial License 4.0 (CC BY-NC 4.0).
dc.rights.accessrightsOpenAccess
dc.rights.urihttps://creativecommons.org/licenses/by-nc/4.0/deed.en_US
dc.subjectData Analysis
dc.subjectED Operations
dc.subjectEmergency Department
dc.subjectLength of Stay
dc.subjectWaiting Time
dc.subjectData Analysis
dc.subjectED Operations
dc.subjectEmergency Department
dc.subjectLength of Stay
dc.subjectWaiting Time
dc.subject4203 Health Services and Systems
dc.subject42 Health Sciences
dc.subjectEmergency Care
dc.subjectPatient Safety
dc.subjectClinical Research
dc.subjectHealth Services
dc.subjectGeneric health relevance
dc.subject3 Good Health and Well Being
dc.subject0807 Library and Information Studies
dc.subject1117 Public Health and Health Services
dc.subjectMedical Informatics
dc.subject4203 Health services and systems
dc.subject4601 Applied computing
dc.subject.meshAdolescent
dc.subject.meshAdult
dc.subject.meshAged
dc.subject.meshAged, 80 and over
dc.subject.meshCOVID-19
dc.subject.meshEmergency Service, Hospital
dc.subject.meshFemale
dc.subject.meshHospital Mortality
dc.subject.meshHumans
dc.subject.meshLength of Stay
dc.subject.meshMale
dc.subject.meshMiddle Aged
dc.subject.meshNew Zealand
dc.subject.meshWaiting Lists
dc.subject.meshYoung Adult
dc.subject.meshEmergency Service, Hospital
dc.subject.meshHumans
dc.subject.meshLength of Stay
dc.subject.meshAged
dc.subject.meshMiddle Aged
dc.subject.meshFemale
dc.subject.meshMale
dc.subject.meshNew Zealand
dc.subject.meshCOVID-19
dc.subject.meshAdult
dc.subject.meshHospital Mortality
dc.subject.meshWaiting Lists
dc.subject.meshAged, 80 and over
dc.subject.meshAdolescent
dc.subject.meshYoung Adult
dc.subject.meshEmergency Service, Hospital
dc.subject.meshHumans
dc.subject.meshLength of Stay
dc.subject.meshAged
dc.subject.meshMiddle Aged
dc.subject.meshFemale
dc.subject.meshMale
dc.subject.meshNew Zealand
dc.subject.meshCOVID-19
dc.subject.meshAdult
dc.subject.meshHospital Mortality
dc.subject.meshWaiting Lists
dc.subject.meshAged, 80 and over
dc.subject.meshAdolescent
dc.subject.meshYoung Adult
dc.titleEmergency Department Trends and Outcomes: A Data-Driven Analysis
dc.typeJournal Article
pubs.elements-id623358

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