{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,22]],"date-time":"2026-05-22T23:06:19Z","timestamp":1779491179080,"version":"3.53.1"},"reference-count":52,"publisher":"Oxford University Press (OUP)","issue":"6","license":[{"start":{"date-parts":[[2026,3,30]],"date-time":"2026-03-30T00:00:00Z","timestamp":1774828800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"funder":[{"name":"Research Grants Council of the Hong Kong Special Administrative Region","award":["C7162-20G"],"award-info":[{"award-number":["C7162-20G"]}]},{"name":"Research Grants Council of the Hong Kong Special Administrative Region","award":["CityU 11501425"],"award-info":[{"award-number":["CityU 11501425"]}]},{"name":"Research Grants Council of the Hong Kong Special Administrative Region","award":["CityU 11508921"],"award-info":[{"award-number":["CityU 11508921"]}]},{"name":"Research Grants Council of the Hong Kong Special Administrative Region","award":["GRF17501022"],"award-info":[{"award-number":["GRF17501022"]}]},{"name":"Theme-based Research Fund"},{"name":"HKU Education Consulting","award":["SZRI2023-TBRF-03"],"award-info":[{"award-number":["SZRI2023-TBRF-03"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2026,6,1]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Objective<\/jats:title>\n                    <jats:p>Despite widespread implementation of predicted patient wait time information systems in hospital emergency departments (EDs), the relationship between quality of announced wait time information and ED overcrowding mitigation remains unclear. This study investigates how prediction accuracy, update frequency, and patient adoption rates affect ED overcrowding level.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Materials and Methods<\/jats:title>\n                    <jats:p>A data-calibrated simulation model was developed using patient visit records from three metropolitan EDs in Hong Kong. We systematically varied patient adoption rates and evaluated seven wait time prediction methods across four update frequencies. Key performance metrics included the mean and standard deviation of patient wait times and percentage of patients left without being seen (LWBS rate).<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>Accurate prediction methods combined with frequent updates significantly reduced the mean and standard deviation of patient wait times and LWBS rate as patient adoption rate increased. Conversely, inaccurate prediction methods exhibited a U-shaped performance curve. Specifically, when the patient adoption rate was sufficiently high, these methods significantly increased the mean and standard deviation of wait times and LWBS rate, compared to the case with no predicted wait time.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>Implementing information systems to display predicted patient wait times requires carefully balancing prediction accuracy, update frequency, and patient adoption. Accurate and timely updates can help redistribute patient load across hospital networks and improve efficiency, while poor accuracy or infrequent updates risk worsening ED congestion, especially when patient adoption rate is high. Our study calls for immediate attention from ED managers to carefully evaluate the impact of announced wait time system before wide implementation.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1093\/jamia\/ocag036","type":"journal-article","created":{"date-parts":[[2026,3,30]],"date-time":"2026-03-30T18:18:15Z","timestamp":1774894695000},"page":"1112-1120","source":"Crossref","is-referenced-by-count":0,"title":["Impact of announced wait time information on emergency department overcrowding mitigation: a simulation study"],"prefix":"10.1093","volume":"33","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-9326-7980","authenticated-orcid":false,"given":"Chengye","family":"Zou","sequence":"first","affiliation":[{"name":"Department of Supply Chain and Information Management, The Hang Seng University of Hong Kong , Hong Kong,","place":["China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yiran","family":"Zhang","sequence":"additional","affiliation":[{"name":"Faculty of Business and Economics, The University of Hong Kong , Hong Kong,","place":["China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0020-1860","authenticated-orcid":false,"given":"Huiyin","family":"Ouyang","sequence":"additional","affiliation":[{"name":"Faculty of Business and Economics, The University of Hong Kong , Hong Kong,","place":["China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1576-3372","authenticated-orcid":false,"given":"Zhankun","family":"Sun","sequence":"additional","affiliation":[{"name":"Department of Decision Analytics and Operations, College of Business, City University of Hong Kong , Hong Kong,","place":["China"]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"286","published-online":{"date-parts":[[2026,3,30]]},"reference":[{"key":"2026052218580865700_ocag036-B1","doi-asserted-by":"crossref","first-page":"326-e1","DOI":"10.1016\/j.ajem.2021.10.023","article-title":"Delayed care in myocardial infarction and ischemic stroke patients during the COVID-19 pandemic","volume":"54","author":"Clodfelder","year":"2022","journal-title":"Am J Emerg Med."},{"key":"2026052218580865700_ocag036-B2","doi-asserted-by":"crossref","first-page":"839","DOI":"10.1016\/j.jemermed.2013.08.133","article-title":"Association of emergency department and hospital characteristics with elopements and length of stay","volume":"46","author":"Handel","year":"2014","journal-title":"J Emerg Med"},{"key":"2026052218580865700_ocag036-B3","first-page":"1","article-title":"Challenges and perceived impacts of ambulance diversions during emergency department overcrowding: a multi-stakeholder study","volume":"297","author":"Rao","year":"2024","journal-title":"Prehosp Emerg Care"},{"key":"2026052218580865700_ocag036-B4","doi-asserted-by":"crossref","first-page":"185","DOI":"10.5811\/westjem.2022.10.58045","article-title":"Impact of emergency department crowding on discharged patient experience","volume":"24","author":"Berlyand","year":"2022","journal-title":"West J Emerg Med."},{"key":"2026052218580865700_ocag036-B5","doi-asserted-by":"crossref","first-page":"247","DOI":"10.1016\/j.auec.2021.12.001","article-title":"A multidisciplinary, cross-sectional survey of burnout and wellbeing in emergency department staff during COVID-19","volume":"25","author":"Dixon","year":"2022","journal-title":"Australas Emerg Care."},{"key":"2026052218580865700_ocag036-B6","author":"NHS Digital"},{"key":"2026052218580865700_ocag036-B7","author":"NSW Health"},{"key":"2026052218580865700_ocag036-B8","author":"Inova"},{"key":"2026052218580865700_ocag036-B9","author":"Alberta Health Services. 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