Evidence map›Paper›PMID 41911388›Full record

ArticleJournal of the American Medical Informatics Association : JAMIA2026

Impact of announced wait time information on emergency department overcrowding mitigation: a simulation study.

Chengye Zou, Yiran Zhang, Huiyin Ouyang, Zhankun Sun

Abstract read
In one paragraph

Article in Journal of the American Medical Informatics Association : JAMIA, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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1 · What the graph read from it

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2 · The registry

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3 · Its place in the literature

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4 · The record

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5 · Who and what money

Authors and funding

4 authors.

Chengye ZouDepartment of Supply Chain and Information Management, The Hang Seng University of Hong Kong, Hong Kong, China.ORCID 0000-0002-9326-7980
Yiran ZhangFaculty of Business and Economics, The University of Hong Kong, Hong Kong, China.
Huiyin OuyangFaculty of Business and Economics, The University of Hong Kong, Hong Kong, China.ORCID 0000-0002-0020-1860
Zhankun SunDepartment of Decision Analytics and Operations, College of Business, City University of Hong Kong, Hong Kong, China.ORCID 0000-0002-1576-3372

Funding

HKU Education Consulting SZRI2023-TBRF-03Research Grants Council of the Hong Kong Special Administrative Region C7162-20GResearch Grants Council of the Hong Kong Special Administrative Region CityU 11501425Research Grants Council of the Hong Kong Special Administrative Region CityU 11508921Research Grants Council of the Hong Kong Special Administrative Region GRF17501022Theme-based Research FundTheme-based Research Fund provided by HKU Education Consulting (Shenzhen) Co., Ltd. SZRI2023-TBRF-03
6 · The paper itself

Abstract

objectiveDespite 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. MATERIALS AND

methodsA 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).

resultsAccurate 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.

conclusionsImplementing 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.

Indexed as

CrowdingEmergency Service, HospitalWaiting ListsComputer SimulationEmergency Room VisitsHong KongHumansTime Factorsemergency department operationshealth information systemssimulationwait time prediction

Identifiers

PMID41911388
PMCPMC13197183

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.