Evidence map›Paper›PMID 41501649›Full record

ArticleBMC emergency medicine2026

Construction of an index system for assessing emergency department overcrowding in Chinese tertiary hospitals: a Delphi study.

Zhen Ren, Nengyuan Xu, Yilan Yang, Shu Li, Hua Zhang, Lijun Wang, Yessai Negati Mu, Wei Chong, Ping Zhou, Longfei Pan and 10 more

Abstract read
In one paragraph

Article in BMC emergency medicine, 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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4 · The record

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5 · Who and what money

Authors and funding

20 authors.

Zhen Ren *Department of Emergency Medicine, Peking University Third Hospital, Beijing, 100191, China.
Nengyuan Xu *Department of Emergency Medicine, Peking University Third Hospital, Beijing, 100191, China.
Yilan YangDepartment of Emergency Medicine, Peking University Third Hospital, Beijing, 100191, China.
Shu LiDepartment of Emergency Medicine, Peking University Third Hospital, Beijing, 100191, China.
Hua ZhangResearch Centre of Clinical Epidemiology, Peking University Third Hospital, Beijing, 100191, China.
Lijun WangDepartment of Emergency Medicine, Tianjin Medical University General Hospital, Tianjin, 300052, China.
Yessai Negati MuDepartment of Emergency Medicine, People's Hospital of Xinjiang Uygur Autonomous Region, Urumqi, Xinjiang, 830001, China.
Wei ChongDepartment of Emergency Medicine, The First Hospital of China Medical University, Shenyang, Liaoning, 110122, China.
Ping ZhouDepartment of Emergency Medicine, Sichuan Academy of Medical Sciences & Sichuan Provincial People's Hospital, Chengdu, Sichuan, 610072, China.
Longfei PanDepartment of Emergency Medicine, The Second Affiliated Hospital of Xi'an Jiaotong University, Xi'an, Shaanxi, 710004, China.
Guoxing WangDepartment of Emergency Medicine, Beijing Friendship Hospital, Capital Medical University, Beijing, 100052, China.
Xiaojing LiDepartment of Emergency Medicine, Peking University First Hospital, Beijing, 100034, China.
Yan LiDepartment of Emergency Medicine, Second Hospital of Shanxi Medical University, Taiyuan, Shanxi, 030001, China.
Wencao LiuDepartment of Emergency Medicine, Shanxi Provincial People's Hospital, Taiyuan, Shanxi, 030000, China.
Hongxuan LiuDepartment of Emergency Medicine, Shanxi Bethune Hospital, Taiyuan, Shanxi, 030032, China.
Bin XuDepartment of Emergency Medicine, Beijing Tiantan Hospital, Capital Medical University, Beijing, 100070, China.
Yinzi JinDepartment of Global Health, School of Public Health, Peking University, Beijing, 100191, China.
Li MaDepartment of Emergency Medicine, Peking University Third Hospital, Beijing, 100191, China.
Guilong FengDepartment of Emergency Medicine, First Hospital of Shanxi Medical University, Taiyuan, 030001, China. fgl2008friend@163.com.
Qingbian MaDepartment of Emergency Medicine, Peking University Third Hospital, Beijing, 100191, China. maqingbian@126.com.

Funding

National Natural Science Foundation of China 7217040327The special fund of the National Clinical Key Specialty Construction Program, P. R. China (2022) 301-2305
6 · The paper itself

Abstract

backgroundEmergency department crowding (EDC) is a global public health crisis associated with adverse patient- and physician-related events. Currently, a reliable EDC assessment tool is lacking in China, as existing international models cannot be directly adapted owing to differences in healthcare systems, data accessibility constraints, and national policy contexts. The issue is multifactorial, data are difficult to obtain, and EDC varies regionally and temporally. Therefore, we developed a crowding assessment index system for tertiary hospital emergency departments in China, aiming to support the development of quantitative models suited to local conditions and formulation of related policies.

methodsThis study used two rounds of Delphi surveys involving a multidisciplinary expert panel from China, with expertise in emergency care crowding research and management. Experts rated 96 presumptive assessment indicators. The index system’s reliability was assessed by evaluating the experts’ enthusiasm, degree of authority, and degree of consistency and coordination in their opinions. The core EDC indicators were screened and optimised based on the boundary value method, with decision rules including coefficient of variation < 0.25 and full-score ratio ≥ 50%, referenced from prior Delphi studies. The final assessment system was established after modifying the indicators per the experts’ opinions. Data were summarised using descriptive statistics.

resultsAll 16 invited and eligible panellists participated (response rate, 100% in both rounds); the authority coefficient was 0.85. Most were aged > 40 years (14/16 [88%]), and the sex distribution was equal (eight men, eight women). Panellists achieved consensus on 3 primary, 8 secondary, and 56 tertiary indicators for EDC assessment. The three primary indicators included the emergency department ‘input-process-output’ phases. The input indicators included patient (e.g., age) and temporal (e.g., day or night) characteristics. The process indicators covered resource requirements (e.g., intravenous infusions), resource supply (e.g., doctor–patient ratio), and process efficiency (e.g., waiting time). The output indicators included patient survival outcomes, hospitalisation outcomes, and hospitalisation supply (e.g., bed occupancy rate).

conclusionsIn this study, Chinese experts reached consensus on an EDC assessment index system. These criteria provide a basis for developing quantitative EDC prediction tools and inform future research and policy development.

Indexed as

CrowdingEmergency Service, HospitalTertiary Care CentersChinaDelphi TechniqueEast Asian PeopleFemaleHumansMaleReproducibility of ResultsAssessmentCrowdingDelphi methodEmergency departmentIndex system

Identifiers

PMID41501649
PMCPMC12870493

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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.