Predicting waiting and treatment times in emergency departments using ordinal logistic regression models

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dc.authoridAtaman, Mustafa Gokalp / 0000-0003-4468-0020
dc.authoridsariyer, gorkem / 0000-0002-8290-2248
dc.authorscopusid57192943136
dc.authorscopusid57189867008
dc.authorwosidAtaman, Mustafa Gokalp/O-4644-2017
dc.authorwosidsariyer, gorkem/AAA-1524-2019
dc.contributor.authorAtaman, Mustafa Gökalp
dc.contributor.authorSarıyer, Görkem
dc.date.accessioned2022-02-15T16:57:20Z
dc.date.available2022-02-15T16:57:20Z
dc.date.issued2021
dc.departmentBakırçay Üniversitesien_US
dc.description.abstractBackground: Since providing timely care is the primary concern of emergency departments (EDs), long waiting times increase patient dissatisfaction and adverse outcomes. Especially in overcrowded ED environments, emergency care quality can be significantly improved by developing predictive models of patients' waiting and treatment times to use in ED operations planning. Methods: Retrospective data on 37,711 patients arriving at the ED of a large urban hospital were examined. Ordinal logistic regression models were proposed to identify factors causing increased waiting and treatment times and classify patients with longer waiting and treatment times. Results: According to the proposed ordinal logistic regression model for waiting time prediction, age, arrival mode, and ICD-10 encoded diagnoses are all significant predictors. The model had 52.247% accuracy. The model for treatment time showed that in addition to age, arrival mode, and diagnosis, triage level was also a significant predictor. The model had 66.365% accuracy. The model coefficients had negative signs in the corresponding models, indicating that waiting times are negatively related to treatment times. Conclusion: By predicting patients' waiting and treatment times, ED workloads can be assessed instantly. This enables ED personnel to be scheduled to better manage demand supply deficiencies, increase patient satisfaction by informing patients and relatives about expected waiting times, and evaluate performances to improve ED operations and emergency care quality. (c) 2021 Elsevier Inc. All rights reserved.en_US
dc.identifier.doi10.1016/j.ajem.2021.02.061
dc.identifier.endpage50en_US
dc.identifier.issn0735-6757
dc.identifier.issn1532-8171
dc.identifier.pmid33721589en_US
dc.identifier.scopus2-s2.0-85102288630en_US
dc.identifier.scopusqualityQ1en_US
dc.identifier.startpage45en_US
dc.identifier.urihttps://doi.org/10.1016/j.ajem.2021.02.061
dc.identifier.urihttps://hdl.handle.net/20.500.14034/111
dc.identifier.volume46en_US
dc.identifier.wosWOS:000681307200009en_US
dc.identifier.wosqualityQ1en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.indekslendigikaynakPubMeden_US
dc.language.isoenen_US
dc.publisherW B Saunders Co-Elsevier Incen_US
dc.relation.journalAmerican Journal Of Emergency Medicineen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectEmergency departmenten_US
dc.subjectWaiting timeen_US
dc.subjectTreatment timeen_US
dc.subjectICD-10en_US
dc.subjectTriageen_US
dc.subjectHospital Admissionsen_US
dc.subjectLengthen_US
dc.subjectVariablesen_US
dc.titlePredicting waiting and treatment times in emergency departments using ordinal logistic regression modelsen_US
dc.typeArticleen_US

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