{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,8,15]],"date-time":"2025-08-15T01:03:39Z","timestamp":1755219819161,"version":"3.43.0"},"reference-count":0,"publisher":"IOS Press","isbn-type":[{"type":"electronic","value":"9781643686080"}],"license":[{"start":{"date-parts":[[2025,8,7]],"date-time":"2025-08-07T00:00:00Z","timestamp":1754524800000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2025,8,7]]},"abstract":"<jats:p>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\/P\u0101keh\u0101 and M\u0101ori 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.<\/jats:p>","DOI":"10.3233\/shti250891","type":"book-chapter","created":{"date-parts":[[2025,8,7]],"date-time":"2025-08-07T11:34:19Z","timestamp":1754566459000},"source":"Crossref","is-referenced-by-count":0,"title":["Emergency Department Trends and Outcomes: A Data-Driven Analysis"],"prefix":"10.3233","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-2038-9167","authenticated-orcid":false,"given":"Hamidreza","family":"Rasouli Panah","sequence":"first","affiliation":[{"name":"Department of Data Science and Artificial Intelligence, AUT, Auckland, New Zealand"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Samaneh","family":"Madanian","sequence":"additional","affiliation":[{"name":"Department of Data Science and Artificial Intelligence, AUT, Auckland, New Zealand"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jian","family":"Yu","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Software Engineering, AUT, Auckland, New Zealand"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Abtin","family":"Ijadi Maghsoodi","sequence":"additional","affiliation":[{"name":"Division of Health, Engineering, Computing and Science, University of Waikato, Hamilton, New Zealand"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"7437","container-title":["Studies in Health Technology and Informatics","MEDINFO 2025 \u2014 Healthcare Smart \u00d7 Medicine Deep"],"original-title":[],"link":[{"URL":"https:\/\/ebooks.iospress.nl\/pdf\/doi\/10.3233\/SHTI250891","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,8,7]],"date-time":"2025-08-07T11:34:19Z","timestamp":1754566459000},"score":1,"resource":{"primary":{"URL":"https:\/\/ebooks.iospress.nl\/doi\/10.3233\/SHTI250891"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,8,7]]},"ISBN":["9781643686080"],"references-count":0,"URL":"https:\/\/doi.org\/10.3233\/shti250891","relation":{},"ISSN":["0926-9630","1879-8365"],"issn-type":[{"type":"print","value":"0926-9630"},{"type":"electronic","value":"1879-8365"}],"subject":[],"published":{"date-parts":[[2025,8,7]]}}}