Emergency Department Trends and Outcomes: A Data-Driven Analysis
| aut.relation.endpage | 509 | |
| aut.relation.journal | Studies in Health Technology and Informatics | |
| aut.relation.startpage | 505 | |
| aut.relation.volume | 329 | |
| dc.contributor.author | Rasouli Panah, Hamidreza | |
| dc.contributor.author | Madanian, Samaneh | |
| dc.contributor.author | Yu, Jian | |
| dc.contributor.author | Ijadi Maghsoodi, Abtin | |
| dc.date.accessioned | 2025-08-14T22:28:47Z | |
| dc.date.available | 2025-08-14T22:28:47Z | |
| dc.date.issued | 2025-08 | |
| dc.description.abstract | This 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.citation | Studies in Health Technology and Informatics, ISSN: 0926-9630 (Print); 0926-9630 (Online), IOS Press, 329, 505-509. doi: 10.3233/SHTI250891 | |
| dc.identifier.doi | 10.3233/SHTI250891 | |
| dc.identifier.issn | 0926-9630 | |
| dc.identifier.issn | 0926-9630 | |
| dc.identifier.uri | http://hdl.handle.net/10292/19679 | |
| dc.language | eng | |
| dc.publisher | IOS Press | |
| dc.relation.uri | https://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.accessrights | OpenAccess | |
| dc.rights.uri | https://creativecommons.org/licenses/by-nc/4.0/deed.en_US | |
| dc.subject | Data Analysis | |
| dc.subject | ED Operations | |
| dc.subject | Emergency Department | |
| dc.subject | Length of Stay | |
| dc.subject | Waiting Time | |
| dc.subject | Data Analysis | |
| dc.subject | ED Operations | |
| dc.subject | Emergency Department | |
| dc.subject | Length of Stay | |
| dc.subject | Waiting Time | |
| dc.subject | 4203 Health Services and Systems | |
| dc.subject | 42 Health Sciences | |
| dc.subject | Emergency Care | |
| dc.subject | Patient Safety | |
| dc.subject | Clinical Research | |
| dc.subject | Health Services | |
| dc.subject | Generic health relevance | |
| dc.subject | 3 Good Health and Well Being | |
| dc.subject | 0807 Library and Information Studies | |
| dc.subject | 1117 Public Health and Health Services | |
| dc.subject | Medical Informatics | |
| dc.subject | 4203 Health services and systems | |
| dc.subject | 4601 Applied computing | |
| dc.subject.mesh | Adolescent | |
| dc.subject.mesh | Adult | |
| dc.subject.mesh | Aged | |
| dc.subject.mesh | Aged, 80 and over | |
| dc.subject.mesh | COVID-19 | |
| dc.subject.mesh | Emergency Service, Hospital | |
| dc.subject.mesh | Female | |
| dc.subject.mesh | Hospital Mortality | |
| dc.subject.mesh | Humans | |
| dc.subject.mesh | Length of Stay | |
| dc.subject.mesh | Male | |
| dc.subject.mesh | Middle Aged | |
| dc.subject.mesh | New Zealand | |
| dc.subject.mesh | Waiting Lists | |
| dc.subject.mesh | Young Adult | |
| dc.subject.mesh | Emergency Service, Hospital | |
| dc.subject.mesh | Humans | |
| dc.subject.mesh | Length of Stay | |
| dc.subject.mesh | Aged | |
| dc.subject.mesh | Middle Aged | |
| dc.subject.mesh | Female | |
| dc.subject.mesh | Male | |
| dc.subject.mesh | New Zealand | |
| dc.subject.mesh | COVID-19 | |
| dc.subject.mesh | Adult | |
| dc.subject.mesh | Hospital Mortality | |
| dc.subject.mesh | Waiting Lists | |
| dc.subject.mesh | Aged, 80 and over | |
| dc.subject.mesh | Adolescent | |
| dc.subject.mesh | Young Adult | |
| dc.subject.mesh | Emergency Service, Hospital | |
| dc.subject.mesh | Humans | |
| dc.subject.mesh | Length of Stay | |
| dc.subject.mesh | Aged | |
| dc.subject.mesh | Middle Aged | |
| dc.subject.mesh | Female | |
| dc.subject.mesh | Male | |
| dc.subject.mesh | New Zealand | |
| dc.subject.mesh | COVID-19 | |
| dc.subject.mesh | Adult | |
| dc.subject.mesh | Hospital Mortality | |
| dc.subject.mesh | Waiting Lists | |
| dc.subject.mesh | Aged, 80 and over | |
| dc.subject.mesh | Adolescent | |
| dc.subject.mesh | Young Adult | |
| dc.title | Emergency Department Trends and Outcomes: A Data-Driven Analysis | |
| dc.type | Journal Article | |
| pubs.elements-id | 623358 |
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