Skip to main navigation Skip to main content

CEEM : Clinical and Experimental Emergency Medicine

OPEN ACCESS
Search
Advanced Search
ABOUT
BROWSE ARTICLES
FOR CONTRIBUTORS

Articles

Page Path

Systematic Review
Emergency Medicine Practice and Administration | Public Health & Policy

Emergency department crowding in the modern era: a systematic review (2018–2025)

Jonathan J. Oskvarek1,2orcid, Andrew Leubitz3, Nishad Rahman4, Bryan Sure1, Jesse M. Pines1,5
Clinical and Experimental Emergency Medicine 2026;13(2):119-139.
Published online: January 14, 2026

1US Acute Care Solutions, Canton, OH, USA

2Department of Emergency Medicine, Summa Health System, Akron, OH, USA

3Adventist Shady Grove Medical Center, Rockville, MD, USA

4Department of Emergency Medicine, Sinai Hospital, Baltimore, MD, USA

5Department of Emergency Medicine, George Washington University, Washington, DC, USA

Correspondence to: Jonathan J. Oskvarek (oskvarekj@usacs.com)
• Received: August 2, 2025   • Revised: January 3, 2026   • Accepted: January 5, 2026

Copyright © 2026 The Korean Society of Emergency Medicine

This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/4.0/).

  • 2,617 Views
  • 325 Download
  • 1 Crossref
prev next

Full Article

Download PDF Download PDF
  • Objective
    This study systematically reviews the causes, effects, and potential solutions to emergency department (ED) crowding, with emphasis on challenges amplified by the COVID-19 pandemic.
  • Methods
    Following PRISMA guidelines, we searched MEDLINE, CINAHL, and the Web of Science for peer reviewed studies published from January 1, 2018, to January 31, 2025, that investigated ED crowding. Studies were included if they evaluated crowding causes, consequences, or interventions, using metrics such as ED length of stay, boarding, or left without being seen. Four reviewers independently screened titles, abstracts, and full texts. Study quality was assessed using the SIGN critical appraisal tools. This review was registered in PROSPERO (No. CRD420251117676).
  • Results
    Of 23,408 studies identified, 226 met inclusion criteria. Most studies were retrospective (83%) and of low (62%) or acceptable (35%) quality. Crowding was primarily driven by input (high patient volumes, limited primary care access), throughput (staffing shortages, laboratory and imaging delays), and output (boarding, late discharges) factors. Adverse effects included increased mortality, treatment delays, prolonged inpatient stays, higher rates of patients leaving without being seen, and reduced patient satisfaction. Effective strategies included provider-in-triage, nurse-initiated orders, and split-flow models. Output-focused interventions, such as active bed management and early discharge protocols, required system-wide coordination. The COVID-19 pandemic shifted patient volumes and led to innovative solutions such as drive-through clinics and repurposed spaces to alleviate surges.
  • Conclusion
    ED crowding is a persistent global issue with significant clinical and operational consequences. While promising interventions exist, high-quality evidence remains limited, underscoring the need for system-level and multifaceted solutions.
  • Keywords: Emergency department; Emergency department crowding; Emergency department length of stay; COVID-19; Length of stay
What is already known
Emergency department crowding is widely recognized as a multifactorial problem driven by input, throughput, and output constraints and is associated with adverse patient outcomes, delayed care, and system strain.
What is new in the current study
This systematic review synthesizes global evidence from 2018–2025, including pandemic-era data, demonstrating worsening crowding trends, expanding impacts on patients, providers, and hospital systems, and highlighting that while numerous operational interventions show promise, high-quality evidence for sustained, system-level solutions remains limited.
Timely assessment and treatment are central to emergency medicine. Emergency department (ED) crowding undermines this goal. Crowding stems from input, throughput, and output factors, such as increased patient arrivals, prolonged length of stay, inefficient service delivery, and a reduced ability to rapidly admit to inpatient units or transfer patients to other hospitals [6–35]. These challenges impact patient safety, care quality, and outcomes.
Previous reviews have provided foundational overviews of ED crowding by describing its multifactorial causes, broad consequences, and potential solutions [1,2]. However, the healthcare landscape has shifted considerably in recent years, particularly during and after the COVID-19 pandemic. At the same time, hospitals implemented novel operational interventions and digital solutions, including artificial intelligence (AI)-based tools and new crowding metrics.
Given the persistence of pandemic-related challenges and the evolving landscape of emergency care, a comprehensive and updated synthesis of the literature is warranted. This review aims to systematically evaluate the causes, consequences, and potential solutions to ED crowding from 2018 to early 2025, including the unique pressures and innovations that emerged during the COVID-19 pandemic and increasing exploration of digital solutions.
Definition of crowding
We used the American College of Emergency Physicians' definition of crowding: “crowding occurs when the identified need for emergency services exceeds available resources for patient care in the emergency department, hospital, or both” (Suppl. 1) [4]. Because there is no agreed upon definition of crowding, we included studies that empirically examined ED length of stay (LOS), rates of left without being seen (LWBS), ambulance diversion, access block/boarding, national disposition targets (e.g., Australia’s National Emergency Access Target [NEAT], the UK National Health Service [NHS] 4-hour target), the Emergency Department Work Index (EDWIN) score, the National Emergency Department Overcrowding Score (NEDOCS), or ED census, which is similar to the approach in a prior review [2].
Search strategy
Following the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines, we searched MEDLINE, CINAHL and Web of Science using the terms “emergency medical services,” “emergency medicine,” or “emergency” AND “crowding,” “crowded,” “overcrowding,” “overcrowded,” “diversion,” “divert,” “congestion,” “surge,” “capacity,” “crisis,” “crises,” “occupancy,” “boarding,” “nurse shortage,” “staff shortage,” “access block,” OR “hospital congestion” (Suppl. 1, 2). We included studies published between January 1, 2018, and January 31, 2025, that examined causes, effects, or solutions to crowding; provided a description of data collection and analysis; and focused on general or pediatric EDs in nondisaster settings, though COVID-19 studies were included. We included quantitative, qualitative, and mixed methods studies.
Study selection, assessment, and data extraction
Four authors reviewed each article title for inclusion (AL, BS, JJO, NR). Then two authors reviewed the abstracts and a third resolved disagreements. Four authors reviewed full text manuscripts and extracted data in pairs, capturing study design, setting, population, sample size, interventions, and key outcomes (AL, BS, JJO, NR). Quality was assessed using the Scottish Integrated Guidelines Network (SIGN) critical appraisal tools [5]. Disagreements in ratings were resolved by discussion until reviewers reached consensus. This systematic review was registered in PROSPERO (No. CRD420251117676).
The initial search identified 23,408 records, from which 300 full texts were retrieved, and 226 met the inclusion criteria (Fig. 1, Suppl. 3–7). Most studies (n=187, 83%) were retrospective, with 30 prospective and others using mixed methods or statistical modeling. Nearly all were quantitative (n=224, 99%), with most conducted in the United States (n=96, 42%) and Canada (n=19, 8%).
The majority of studies were of low (62%) or acceptable (35%) quality, with only eight studies (4%) rated as high quality. Common limitations included lack of a clear primary outcome and insufficient control of confounding factors, as retrospective and single-cohort studies cannot be rated higher than acceptable under SIGN guidelines.
Causes of ED crowding
While ED timeliness metrics improved from 2006–2016 [6], crowding has since worsened globally [7–14], further exacerbated by the COVID-19 pandemic. Crowding affects medical, surgical, and psychiatric patients [9,10,15] and can be attributed to input, throughput, and output factors (Table 1, Suppl. 3) [1–31].

Input factors

The volume of new ED arrivals contributes to ED crowding [16]. Both excessive low- and high-acuity presentations have been linked to crowding [17,18]. Two studies linked low-acuity visits to poor primary care access [19,20]. Others found increased visits outside of operating hours of nonemergency healthcare facilities and when neighboring hospitals permanently closed [21,22]. The inability to access specialists outside the hospital also drives ED visits [17,18]. One study found that Medicaid expansion increased ED visits, wait times, and LWBS [23]. Seasonal variations in illnesses such as influenza have also been linked to ED visits [24].

Throughput factors

Internal ED operations affect ED throughput from arrival to disposition. Staffing shortages can prevent optimal ED flow [25,26]. A retrospective study using a regression model found that higher numbers of senior residents but not junior staff were associated with reduced crowding [25]. Nurse and ancillary staff shortages also reduce throughput and delay patient disposition [26,27].
Delays in obtaining and processing laboratory tests (both time to test and lab turnaround time) and medical imaging contribute to ED crowding [28–31]. These delays affect lower-acuity visits more than higher acuity [29]. Awaiting consultation also prolongs LOS [31].
Physician workflow plays a role in throughput. In interviews, Swedish ED physicians noted their concern for “criticism, reprimand, and gossip or feelings of guilt” on time to assess a patient [32].

Output factors

Three studies examined the causes of ED boarding or access block, where patients spend extended periods in the ED prior to transfer to an inpatient bed. Late discharges from inpatient units were a main contributor to ED boarding in one Canadian study [33]. A root-cause analysis in a Dutch ED showed that ED boarding was driven by factors outside the control of the ED, such as inpatient bed occupancy [34]. Boarding also affects pediatric and psychiatric patients with over half of pediatric patients waiting more than 12 hours in a US tertiary ED for psychiatric placement [35].
Effects of ED crowding
Numerous studies have studied effects of ED crowding on patients (Table 2, Suppl. 4) [36–43,45–92].

Increased mortality

Although four retrospective studies found no association between boarding and in-hospital mortality [36–39], several others found associations between boarding and in-ED [40], in-hospital [41–48], 7-day mortality [49–51], 10-day mortality [52,53], and 30-day mortality [51,54–56]. One retrospective cohort study in New Zealand found that access block of 10% was associated with greater 7-day mortality [49]. An analysis of US death records showed that alleviation of ED occupancy by 10% was associated with decreases of 24% and 17% in 30-day and 6-month mortality rates, respectively, relative to the baseline rates of 1.1% and 2.6%, respectively [55]. Also, crowding has been associated with worse outcomes particularly for older patients [57,58].

Delayed assessment and care

Two retrospective studies in England and one in Australia found that unexpected increases in ED volume resulted in longer wait times [59–61]. When a hospital had 100% bed occupancy, the proportion of patients with ED LOS >4 hours was 9% higher (95% confidence interval [CI], 7.5%–11.1%) than with 85% occupancy [60]. Waiting times were longer during periods of crowding in an Australian retrospective study, but not for high-acuity patients [62]. Time to initial physician assessment was longer with increased crowding in a retrospective Canadian study [63].
Once patients are assessed, delays from crowding continue. A German prospective study of patients with suspected acute coronary syndrome (but not ST-segment elevation myocardial infarction [STEMI]) found a link between increased lab turnaround times and longer ED LOS with increased crowding [64]. Similarly, a retrospective study in Ethiopia found increased lab turnaround time with crowding [65]. Although care for asthma patients was prolonged in a retrospective Canadian study [66], crowding did not delay stroke care in two prospective studies [67,68]. A retrospective study found that each 10% increase in ED occupancy rate was associated with a 4-minute increase in the door-to-antibiotic time [69]. Also, when the ED was overcrowded, 46% of patients received antibiotics within a 3-hour window compared to 63% when it was not [69].
As ED occupancy and LOS increases, more makeshift care areas are used, including hallway beds. ED visits assigned to hallway beds had longer LOS than roomed visits of comparable acuity in a US retrospective study [70].

Increased inpatient LOS

Patients boarded in the ED had longer inpatient LOS in retrospective studies from Canada [71], France [41,72], the United States [46,73], for pediatric patients [73], adult medical patients [41,46,72], and adult psychiatric patients [71].

Increased risk of admission/readmission

Findings on the effect of crowding on admission were mixed. Studies from Canada [63,74,75], Korea [76], Taiwan [77], and the United States [78] found increased admission with increased ED occupancy and crowding, even in all triage acuity groups [63,76]. However, two studies from England and the United States demonstrated lower admissions with ED and inpatient occupancy [79,80]. One study with pediatric patients found no increased risk of 7- or 14-day revisit or readmission with measures of crowding [74]. Another Canadian retrospective study found an association between ED wait times and 72-hour returns [81].

Patient elopement

Longer wait times and crowding have been associated with increased rates of LWBS and left without triage (LWOT). The odds of LWBS in a US retrospective study increased with the number of waiting room patients and the door-to-clinician time [82]. A retrospective Canadian study found that ED crowding was associated with longer time to initial assessment [63]. This in turn was associated with increased odds of left without completion of care. LWOT rates were associated with ED crowding in an English retrospective study [59]. Among pediatric patients in a US study, ED crowding was the most important factor in predicting the likelihood of LWBS [83]. A Canadian retrospective study found higher rates of LWBS amongst lower-acuity patients when the ED is experiencing increased crowding [66].

Poorer patient experiences

A worse patient experience was associated with boarding in two retrospective studies [84,85]. One prospective study from China showed boarding was associated with increased patient anxiety [86]. Another survey found that increased crowding was associated with poor perceptions of care among patients with acute coronary syndrome [87].

Staff effects

One Swedish study using qualitative interviews with ED nurses found that nurses experienced “frustration and disappointment at not being able to achieve the required standard of care” during times of crowding and that ED crowding contributed to negative psychological experiences for nurses [44]. Crowding was also found to negatively affect resident education. One study found that increased crowding increased the likelihood of residents not meeting their ultrasounds quotas [88] and another found crowding was associated with decreased resident patients per hour [89].

System and financial effects

Crowding led to transfer boarding, especially for mental health patients, in rural EDs as detailed by one study from the United States using qualitative interviews [90]. For the patient, ED boarding was associated with a higher cost of hospitalization. It also can affect hospital finances. A simulation model using Australian ED data showed the median cost attributable to lost bed capacity was AUD $202.99 per patient [91]. Also, an analysis of a large registry of ED operational data found higher boarding was associated with decreased profit margin and hospitals with the highest boarding were associated with the highest debt leverage [92].
Solutions
The potential interventions to ED crowding target input, throughput and output factors (Table 3, Suppl. 5) [93–167,203].

Decreasing input

Several studies examined the effect of alternative care areas on ED patient input. A paramedic-based assessment and treat-in-place program in the United States reduced transports of low-acuity patients to the ED [93]. However, a paramedic-facing ED crowding status dashboard did not affect perceived crowding [94]. A virtual model of a Singaporean ED showed that co-location of primary care services could reduce ED LOS [95]. Triaging of patients to non-ED settings such as primary care clinics reduced ED visits, LWBS, costs for patients in Belgium [96], Finland [97], Canada [98], France [99], and Italy [100] with minimal revisits to the ED.
One study found that patients expressed a willingness to accept a primary care appointment instead of an ED visit [101]. Also, use of initial telehealth screening for pediatric patients prior to an ED visit resulted in only 17% of those patients being referred to the ED for further in-person assessment [102]. A similar study in adult ED patients reduced ED visits by patients who called by 57% [103]. Student run clinics have additionally been explored as a viable option to decrease ED visits [104]. In contrast to these studies, a retrospective analysis of ED visits in Texas found that freestanding emergency departments did not alleviate congestion in hospital-based EDs [105].

Improving throughput

1) Provider in triage

Implementation of teleprovider in triage improved provider in triage (PIT) efficiency [106] and decreased LWBS [107]. A Rapid Medical Evaluation (RME) protocol with a PIT to assess patients and begin workups in the waiting room led to a decrease in ED LOS by 90 minutes in one retrospective review [108] and by 22 minutes in another [109]. A prospective study also found a shorter door-to-disposition time for Emergency Severity Index (ESI) level 5 patients after RME implementation [110].
Other studies also found decreased door-to-clinician time, door-to-discharge, ED LOS, and LWBS after PIT implementation [108,109,111–114]. A similar program using a physician patient flow coordinator in triage at a Swiss ED decreased time to first medical provider, but did not improve patient flow or LWBS [115].

2) Early order initiation

Two retrospective studies found that nurse-initiated orders at triage sped care [116,117]. Nurse-initiated medication administration for acetaminophen, nonsteroidal anti-inflammatory drugs (NSAIDs), and ondansetron decreased time to medication administration by 7.5 minutes in a US pediatric ED [116]. Another study in an Italian ED found that performing blood sampling during nurse triage decreased ED LOS by 24 minutes [117]. One prospective US trial in which emergency medical services (EMS) personnel drew blood for laboratory tests on patients with chest pain resulted in a 72.5-minute mean reduction in LOS [118].

3) Streaming, split flow, fast track, vertical care

Triaging patients and continuing their care in nontraditional environments within the ED has been studied in various configurations. A prospective cohort study found that a vertical flow model where patients are triaged and then sent to a vertical care area (i.e., placed in chairs outside the main treatment area or sent back to the waiting room) decreased ED LOS after adjusting for acuity, census, patient age, and admission rate [119]. Similarly, a retrospective study of Stanford’s vertical flow model for ESI 3 patients found a decrease in the total LOS for ESI 3 patients from 384 minutes to 270 minutes [120]. Fast tracks are a similar concept specifically for lower-acuity patients. A study of a pediatric fast track for ESI 4 and 5 patients found a decrease in LOS of 36% for those patients [121]. A simulation model using data from a single institution found that combining split flow by an intake physician and two internal waiting areas resulted in the shortest LOS and highest bed utilization rate [122].

4) Improved turnaround time for laboratory and radiology

A study in a Spanish ED found that a new patient flow model for ESI 3 patients based on a point-of-care testing (POCT) protocol reduced LOS, time to disposition decision, and lab turnaround time without increased readmissions [123]. A French prospective trial found a decreased time to result for laboratory tests with implementation of an extended panel of POCTs, but no change in LOS [124]. An intervention using Lean methodology improved computed tomography (CT) turnaround time from 5.9 to 4.7 hours, despite a 13.8% increase in the number of CTs ordered [125].

5) Electronic notifications

A retrospective trial of push-text notifications to patients on the completion status of tests and directions to ED care areas in an Israeli ED found that the cost of the system was not worth the insignificant effects on patient flow [126]. Another study in Jordan found that electronic health record notifications to clinicians were associated with improved consultation times [127].

6) Increased/improved staffing

Improving staffing or staffing allocation was investigated by two studies. One retrospective analysis found a waterfall schedule in which physician shifts overlap and physicians sometimes change location in the ED during their shifts led to improvements in the door-to-doctor time, LWBS rate, and elopement rate [128]. A simulation study in a Brazilian ED found that rebalancing the allocation of doctors between acuity areas could reduce waiting times [129].

7) Observation units

A retrospective trial in a United Arab Emirates facility found that implementing an inpatient acute medical unit, where healthcare professionals expedite multidisciplinary assessment, decreased ED waiting time and the number of patients boarding in the ED by decreasing total hospital LOS and increasing early discharges [130]. A retrospective study in a Korean ED found that an emergency short stay ward had no effect on ED boarding time, but did decrease intensive care unit (ICU) admissions [131].

8) Process improvement strategies

Lean methodologies use structured observation and targeted solutions to improve efficiency, commonly by eliminating waste in processes [132,133], and have been used to reduce wait times [134,135], throughput times [133], and improve patient satisfaction [132]. Another process improvement approach that included electronic health record (EHR) alterations that better captured the time of ED admission and sent automatic notifications to the admitting team with ED contact information, and provider education resulted in decreased decision-to-admit time [136].

9) Other interventions

Assignment of an ED nurse as a flow coordinator for transfers and EMS arrivals increased the number of patients seen and decreased LWBS by 45% over 3 years [137]. A program to deploy critical care nurses to the ED to care for boarding critically ill patients was not associated with changes in mortality despite an increase in illness severity, hospital congestion, and ED boarding time [138]. A Canadian prospective study found that earlier prediction of admission by emergency physicians could result in reductions of stretcher time [139]. A model to predict prolonged ED LOS was developed and a simulation using it showed that ED crowding could be reduced by 12% [140].
For psychiatric ED patients, a mixed-intervention quality improvement study found that implementing clinical pathways, social worker consults, shared EHR documentation, and improved team communication was associated with a reduced median LOS from 4.2 to 3.5 hours [141]. Finally, a multivariate logit model using data from the US National Hospital Ambulatory Medical Care Survey (NHAMCS) from 2007–2015 found decreased odds of prolonged wait time with adopting bedside registration, electronic dashboards, check-in kiosks, physician-based triage, and a full capacity protocol [142]. The study also found decreased odds of patient LWBS with using wireless devices, bedside registration, and pooled nursing.

Increase output

1) Nurse handoff protocols

An electronic nurse to nurse handoff protocol decreased ED to inpatient room time from 84 to 49 minutes [143].

2) Inpatient hallway boarding

A retrospective study in Israel found that boarding patients in inpatient hallways increased ED bed capacity [144]. Interviews with hospital leaders revealed that boarding ED patients in inpatient halls (also known as full capacity protocol) was more likely to be successful when there was close collaboration with inpatient nurses and when leaders reached a consensus on when the protocol would be triggered [145].

3) Active bed management

Three studies examined the use of active bed management to improve crowding. One study used an emergency transfer coordination center, which reduced the ED LOS of transferred, critically ill patients [146]. The two other studies used a bed calculator to predict the number of staffed beds better than human estimation [147] and a bed information system to reduce administrative staff burden [148], which decreased weekly LOS.

4) Admission to another hospital

Patients can be admitted to another hospital to reduce ED boarding and crowding. In a prospective study at a US ED, transferring patients from an academic ED to a community hospital medical floor reduced boarding times, ED LOS, and hospital LOS [149]. In another study, 50 patients were transferred from one academic ED to another academic hospital to level patient loads during the COVID-19 pandemic [150]. This saved 432 bed days at the home academic medical center.

5) Reducing bed downtime

Implementation of a quality improvement metric on bed downtime and education to providers around the metric decreased mean bed downtime from 254 to 129 minutes [151]. Implementation of an ED flow nurse who notified staff of patient bed assignments in real time, called report, and then facilitated transport of the patient led to a decreased transport time from 104 to 84 minutes [152].

6) Modified admission protocols

A pilot project to discharge patients with coordinated outpatient services instead of admission resulted in 30 patients being enrolled and only 13% returning within 30 days [153]. Boarding time was reduced by 8.2 hours. Streamlining the admission process in a Korean hospital between the ED attending and an internal medicine team decreased both LOS of admitted patients and time to admit order [154].

7) Early discharges

Deploying a multidisciplinary rounding format reduced LOS by 0.83 days and decreased ED boarding by 8.83 hours per month [155]. Other interventions to facilitate earlier inpatient discharges in Brazil, Israel, and Saudi Arabia showed this can reduce ED crowding [156–158], but one of these studies also showed that in the process, staff dissatisfaction increased [156] and another showed no improvement in ED boarding [159].

8) Discharge lounge

Discharge lounges are areas where discharged patients can await completion of discharge and transport so inpatient beds can be more rapidly reused. Increased discharge lounge use in a medical-surgical unit reduced discharge turnaround time and hospital crowding [160].

9) Maximum time-related boarding rules

Five studies evaluated the impact of maximum boarding rules and admission, such as the NHS 4-hour ED LOS rule for admission. One found that delayed transfers of care (when an admitted patient is ready to depart from the ED but has not done so yet) were not associated with breaches of the NHS 4-hour rule [161]. A study of the effect of Western Australia’s 4-hour rule found no conclusive benefit to mortality or patient flow [162]. In contrast, Australian ED staff noted in a survey that it improved access to care while harming education and training [163].
A study of a 24-hour maximum boarding protocol in a Korean hospital showed ED LOS exceeding 24 hours fell from 7.6% to 4.0% [164]. In the United States, the proportion of ED psychiatric patients with prolonged LOS increased after implementation of a state law requiring patients committed through the criminal justice system be transferred to a state psychiatric hospital within 48 hours [165].

10) Other interventions

Active management of boarded ED patients by a hospitalist team led to decreased hospital LOS [166]. Another intervention with an intermediary nurse that took sign out of ED admitted patients and started their admission process before handing them over to the floor also reduced hospital LOS [167].
AI and machine learning
Machine learning and AI models to predict and address crowding have been evaluated in several studies, predicting flow and patient disposition (Table 4, Suppl. 6) [147,168–200]. One Chinese study collected videos of different ED areas and used AI to calculate congestion [168]. Data from a French ED was used to build a queuing model that accurately predicted when the ED would be most crowded based on the number of patients and medical professionals present [169]. A home-grown complexity score at a Canadian ED predicted LWBS and time to initial assessment [170].
Other models in France, the United States, and Canada can detect strain situations when a large number of patients are arriving [171], and predict ED occupancy 4 hours ahead [172], wait times [173,174], and mortality [175]. Several models to predict patient disposition have also been evaluated. One identified low-severity patients who would be ideal candidates for a fast track [176]. Others predicted admission and inpatient demand with generally high levels of reliability [147,177–182]. However, one model overpredicted the need for admission [180].
Measures for assessing ED overcrowding
Several studies evaluated ED crowding metrics or proposed novel measures (Table 4, Suppl. 6) [142,164–197]. Several studies found the NEDOCS had good discriminatory power for crowding perception [183], though another study noted discrepancies with staff perceptions [184–186]. Two prospective Italian studies reported low correlation between NEDOCS and EDWIN [187,188]. However, retrospective US studies found significant correlations between ED occupancy, NEDOCS, EDWIN, and staff evaluations [189,190]. ED occupancy was as effective as complex measures like NEDOCS, EDWIN, and International Crowding Metric in Emergency Departments (ICMED) for describing crowding [191]. The modified Skåne Emergency Department Assessment of Patient Load (mSEAL) model performed comparably to NEDOCS, simplified ICMED, and occupancy rate [192]. Australian ED directors noted that no single metric fully captures crowding [193].
New models include calculating ambulances needed to address overcapacity [194], a polynomial regression for arrival patterns [195], and metrics for unnecessary waits [196] and healthcare professional capacity using a staircase model, in which productivity decreases in a stepwise fashion throughout a clinician’s shift [197].
Additional research highlighted system-wide impacts: an Irish study linked ED LOS to bed supply rather than ED demand and performance [198], while an Australian study attributed ED LOS largely to boarding, proposing “lost bed capacity” as a better metric [199]. Conversely, a US study identified patient age, arrival mode, diagnosis, and triage level as predictors of wait and treatment times [200].
COVID-19

Causes: shifts in ED utilization patterns

A new area of research examined the changes in ED utilization patterns during COVID-19 and the resulting effects on ED crowding as well as potential solutions (Table 5, Suppl. 7) [201–230]. During the early COVID-19 pandemic, ED visits decreased, as shown in several retrospective studies [201–216]. Two Italian studies (February–May 2020) reported decreases across all patient types, with the largest decline in low-acuity visits [203,204]. ED radiology volumes dropped 35% across five US health systems in 2020 compared to 2019 [208]. Southern California and Colorado EDs saw decreased in-person visits and increased telehealth visits [206,212]. Later, ED visits gradually rose but remained below pre-pandemic levels [207,216].

Effects

Despite fewer visits, most studies reported increased LOS, attributed partly to mandatory COVID-19 testing before admission [217]. Retrospective studies in China [218], the Netherlands [219], Italy [203,204], and the United States [220,221] found increased LOS, though two studies noted shorter median LOS early in the pandemic [201,202]. A US study found that when hospital occupancy exceeded 85%, boarding exceeded the 4-hour Joint Commission standard in 88.9% of hospital-months [220]. Similarly, a Swedish study reported a 0.94 correlation between ED occupancy and LOS during COVID-19 [222]. Hospital strain was significant. A retrospective study (March 2020–April 2021) found 63% of US hospitals issued at least one alert for ED/ICU overcrowding or ventilator shortages [223]. Higher crowding continued late into the pandemic [73].

Solutions

Strategies to address crowding included drive-through clinics adjacent to EDs for low-acuity patients, resulting in shorter LOS and minimal ED referrals [224]. Forward triage tents in a New York ED screened low-risk COVID-19 patients, with 21% referred to the ED and 12.7% admitted [225]. Vanderbilt repurposed a parking garage, treating 55% of COVID-positive patients in the first month [226]. In Italy, an alternative care site staffed by nurses tested, imaged, and evaluated patients; 29 of 392 returned to the ED within 15 days, with 13 admitted [227]. A Dutch ED converted its observation unit into treatment space, avoiding external treatment sites [228]. Observation and admission protocols also helped [229,230]. A COVID-19 Accelerated Care Pathway, including observation and post-discharge automated text monitoring, reduced hospital LOS by 2.2 days in one US hospital [229].
In this systematic review, we identified 226 articles on the causes, effects and potential solutions to ED crowding in a wide range of ED settings. Most were of low or acceptable quality, emphasizing the need for high-quality research on solutions using diverse populations.
Causes
ED crowding stems from poor primary care access, high-acuity presentations, and systemic inefficiencies. Compared to the 2018 review by Morley et al. [2], there has been considerable examination of the causes of crowding. Low- and high-acuity visits, poor primary care access, non-ED facility closing hours, and Medicaid expansion contribute to crowding [17–21]. The ED remains a 24/7 safety net for all patients. Disposition delays due to lab and radiology turnaround times disproportionately affect lower-acuity patients [29]. However, hospital boarding, largely uncontrollable by the ED, is the primary driver of crowding [34].
Effects
ED crowding has significant negative outcomes, including increased mortality, delayed care, longer inpatient stays, higher elopement rates, and reduced satisfaction. Studies found mixed results on crowding and mortality: some linked boarding to higher in-ED mortality [40], in-hospital mortality [41–43,45–48], 7-day mortality [49–51], 10-day mortality [52,53], and 30-day mortality [51,54–56]. One study showed that access block of 10% was associated with greater 7-day mortality [49] while another showed that alleviation of ED occupancy by 10% was associated with a decrease of 24% and 17% in 30-day and 6-month mortality rates [55]. Thus, small reductions in boarding may have an outsize impact on patient outcomes. Staff effects were underreported. Although provider attrition was found in an earlier review by Hoot and Aronsky [1], a more recent review by Morley et al. [2] did not find this negative effect. Financial costs remain complex to study, involving revenue loss, payer mix, and system costs.
Solutions
Interventions addressed input, throughput, and output. PIT [106–115], nurse-initiated orders [116,117], and split-flow models [119–122] improve throughput. Output interventions, often external to the ED, include inpatient hallway boarding [144,145], active bed management [146–148], early discharges [155–159,203], and interfacility admissions [149,150]. Maximum boarding rules showed mixed results, highlighting the need for more research. Further research on maximum boarding rules and other legislative efforts to combat boarding is needed. Likewise, the effect of potentially reduced governmental support (e.g., cuts to Medicaid and Medicare) requires careful study. Machine learning and AI for patient flow prediction hold promise but require validation across diverse settings. Also, this review did not find any studies evaluating the effect of AI applications such as ambient AI documentation and decision support on throughput.
Impact of COVID-19 on ED crowding
The pandemic altered ED dynamics, with initial declines in volumes [201,202,205], especially low-acuity visits [203,204], but increased LOS [203,204,217–220]. Innovative strategies like drive-through clinics, forward triage areas, and converted spaces managed surges and highlighted the need for flexible healthcare systems [225–227].
Strengths and limitations
This systematic review offers several strengths. First, it provides the most current and comprehensive synthesis of the ED crowding literature to date, capturing studies published between 2018 and early 2025. Second, our structured approach categorizes the literature into five distinct domains (causes, consequences, interventions, metrics and prediction tools, and COVID-19-related crowding), offering a practical framework for clinicians, researchers, and policymakers. Third, by including literature from the COVID-19 pandemic, this review uniquely highlights how global healthcare stressors reshaped crowding dynamics and responses.
Several limitations exist. First, this review was limited to studies published in English. Second, the majority of studies were of low or acceptable quality based on the SIGN appraisal tool. Retrospective and single-cohort studies, which cannot be rated higher than acceptable, dominated the review. There was a scarcity of high-quality randomized controlled trials or prospective cohort studies, which affects the strength and reliability of the conclusions drawn. Third, the studies varied widely in terms of design, setting/regionality, population, and outcomes. This makes it challenging to synthesize findings and draw generalized conclusions. Fourth, the review may be affected by publication bias, where studies with significant or positive findings are more likely to be published. Fifth, the studies were conducted in various time periods and geographical locations. Changes in healthcare systems, policies, and practices over time and across regions may limit the applicability of the findings to current and specific local contexts. Sixth, the lack of a universal definition of ED crowding and the variability in operational definitions across studies could affect the comparability of the findings. Seventh, although interventions to mitigate ED crowding were identified, the review may not comprehensively cover all possible solutions. The effectiveness of the identified interventions may also vary depending on local context and implementation.
Conclusions
ED crowding is a complex, persistent, worldwide issue exacerbated by factors such as poor access to primary care, high-acuity cases, and systemic inefficiencies. The COVID-19 pandemic further highlighted these challenges. Crowding negatively impacts patient outcomes, delaying care and likely increasing mortality. Interventions such as provider-in-triage and split-flow models show promise, yet improving patient flow out of the ED is difficult and requires broader hospital operational changes. The quality of studies was generally low, emphasizing the need for high-quality research.

Author contributions

Conceptualization: all authors; Data curation: JJO, AL, NR, BS; Formal analysis: all authors; Investigation: all authors; Methodology: all authors; Project administration: JJO, JMP; Resources: JJO, JMP; Software: JJO; Supervision: JJO, JMP; Validation: JJO; Visualization: JJO; Writing–original draft: JJO; Writing–review & editing: all authors. All authors read and approved the final manuscript.

Conflicts of interest

The authors have no conflicts of interest to declare.

Funding

The authors received no financial support for this study.

Data availability

Data analyzed in this study are available from the corresponding author upon reasonable request.

Supplementary materials are available from https://doi.org/10.15441/ceem.25.172.

Suppl. 1.

Definition of crowding and search terms.
ceem-25-172-Suppl-1.pdf

Suppl. 2.

Search strategy details.
ceem-25-172-Suppl-2.pdf

Suppl. 3.

Causes of crowding.
ceem-25-172-Suppl-3.pdf

Suppl. 4.

Effects of crowding.
ceem-25-172-Suppl-4.pdf

Suppl. 5.

Solutions to crowding.
ceem-25-172-Suppl-5.pdf

Suppl. 6.

Artificial intelligence and data/measuring techniques.
ceem-25-172-Suppl-6.pdf

Suppl. 7.

COVID-19 studies.
ceem-25-172-Suppl-7.pdf
Fig. 1.
PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) flow diagram.
ceem-25-172f1.jpg
Table 1.
Studies on causes of ED crowding
Table 1.
Cause Reference
Recent progress [6]
Geographical study [7–14]
Type of patients
 Medical [9]
 Surgical [10]
 Psychiatric [15]
Input factor
 New arrivals to the ED [16]
 Excessive low acuity visits [17]
 Excessive high acuity visits [17,18,21]
 Poor access to primary care [19,20]
 After hours of nonemergency healthcare facilities [21]
 Need for specialty consultation [17,18]
 Seasonal illnesses (e.g., influenza) [24]
 Expansion of Medicaid [23]
 Closure of neighboring hospitals [22]
Throughput factor
 Staffing shortages/misallocation of staff [25–27,29]
 Lab and imaging delays [28–31]
 Physician decision making [32]
Output factor
 Late discharges [33]
 Inpatient bed occupancy [34]
 Psychiatric bed unavailability [35]

ED, emergency department.

Table 2.
Studies on effects of ED crowding
Table 2.
Effect Reference
Adverse outcome
 No increase in in-hospital mortality [36–39]
 Increased in-hospital mortality [40–48]
 Increased 7-day mortality [49–51]
 Increased 10-day mortality [52,53]
 Increased 30-day mortality [51,54–56]
 Worse outcomes for older patients [57,58]
Delayed assessment and care
 Longer wait times [59–62]
 Door-to-doctor time [63]
 Increased lab turnaround time [64,65]
 Delayed care for asthma patients [66]
 No delay in stroke care [67,68]
 Door-to-antibiotic time [69]
 Increased use of hallway beds [70]
Increased length of stay [41,46,71–73]
Risk of admission/readmission
 Increased risk of admission [63,74–78]
 Decreased risk of admission [79,80]
 No increased risk of readmission [74]
 Increased risk of 72-hr returns [81]
Patient elopement/left without being seen [59,63,66,82,83]
Reduced patient satisfaction [84–87]
Negative effects on staff [44,88,89]
Transfer boarding [90]
Increased costs [43,91,92]

ED, emergency department.

Table 3.
Studies on solutions to ED crowding
Table 3.
Solution Reference
Decreasing input
 Increased primary care access [95,101,104]
 Paramedic treatment program to divert low-acuity patients [93]
 Triage to non-ED settings [96–100,102,103]
 Freestanding EDs (negative findings) [105]
 Paramedic-facing ED capacity dashboard [94]
Improving throughput
 Provider in triage [106–115]
 Nurse-initiated orders [116,117]
 Prehospital lab draws [118]
 Streaming, split flow, fast track, vertical care [119–122]
 Improved turnaround time for laboratory tests and radiology [123–125]
 Electronic health records/push-text notifications to improve patient flow [126,127]
 Improved staffing [128,129]
 Observation units [130,131]
 Process improvement strategies (e.g., Lean methodology) [132–136]
 Other
  Nurse flow coordinator [137]
  Critical care nurse deployment in ED [138]
  Earlier prediction of admission [139]
  Models to predict prolonged ED length of stay [140]
  Psychiatric patient care pathways [141]
  Bedside registration, physician triage and full capacity protocol [142]
Increased output
 Nurse handoff protocols [143]
 Inpatient hallway boarding [144,145]
 Active bed management [146–148]
 Admission to another hospital [149,150]
 Reducing bed downtime [151,152]
 Modified admission protocols [153,154]
 Early discharges [155–159]
 Discharge lounge [160]
 Maximum boarding rules [161–165]
 Hospitalist management of boarded patients [166]
 Boarding nurse [167]

ED, emergency department.

Table 4.
Studies on artificial intelligence and measuring crowding
Table 4.
AI and machine learning Reference
Predict flow [168–174]
Predict disposition [147,176–182]
Predict crowding and mortality [175]
Measuring crowding
Evaluation of existing metrics
 National Emergency Department Overcrowding Score (NEDOCS) [183–186,189,190]
 Emergency Department Work Index (EDWIN) [187,188,190]
 Length of stay [198]
 Other [191–193]
New methods of measurement
 No. of ambulances needed to relieve overcapacity [194]
 Staircase model [195]
 Wait hours [196]
 Lost bed capacity [199]
 Hierarchical polynomial regression prediction model [195]
 Data mining/ordinal logistic regression prediction model [200]
Table 5.
Studies on COVID-19 and crowding
Table 5.
COVID-19 Reference
Decreased ED visits during early pandemic [201,202,205,209–215]
Decreased low acuity visits [203,204]
Decreased ED radiology volumes [208]
Increased telehealth visits [206,212]
Gradual increase in ED visits [207]
Increased LOS [203,204,217–221]
Decreased LOS in early pandemic [201,202]
High levels of boarding/crowding [213,216,220,222,223]
Crowding solution
 Drive-through clinic [224]
 Tents/alternative assessment area [225–228]
 Observation and admission protocols [229,230]

ED, emergency department; LOS, length of stay.

  • 1. Hoot NR, Aronsky D. Systematic review of emergency department crowding: causes, effects, and solutions. Ann Emerg Med 2008;52:126-36.
  • 2. Morley C, Unwin M, Peterson GM, Stankovich J, Kinsman L. Emergency department crowding: a systematic review of causes, consequences and solutions. PLoS One 2018;13:e0203316.
  • 3. Asplin BR, Magid DJ, Rhodes KV, Solberg LI, Lurie N, Camargo CA. A conceptual model of emergency department crowding. Ann Emerg Med 2003;42:173-80.
  • 4. American College of Emergency Physicians. Crowding. Ann Emerg Med 2006;47:585.
  • 5. Scottish Intercollegiate Guidelines Network (SIGN). Using our guidelines: methodology: checklists [Internet]. SIGN; [cited 2024 Aug 21]. Available from: https://www.sign.ac.uk/using-our-guidelines/methodology/checklists/
  • 6. Bouda Abdulai AS, Mukhtar F, Ehrlich M. United States’ performance on emergency department throughput, 2006 to 2016. Ann Emerg Med 2021;78:174-90.
  • 7. Marsilio M, Roldan ET, Salmasi L, Villa S. Operations management solutions to improve ED patient flows: evidence from the Italian NHS. BMC Health Serv Res 2022;22:974.
  • 8. Beczek A, Vamosi M. Prevalence of prolonged length of stay in an emergency department in urban Denmark: a retrospective health records repository review. J Emerg Nurs 2022;48:102.
  • 9. Otto R, Blaschke S, Schirrmeister W, Drynda S, Walcher F, Greiner F. Length of stay as quality indicator in emergency departments: analysis of determinants in the German Emergency Department Data Registry (AKTIN registry). Intern Emerg Med 2022;17:1199-209.
  • 10. Nouri Y, Gholipour C, Aghazadeh J, et al. Evaluation of the risk factors associated with emergency department boarding: a retrospective cross-sectional study. Chin J Traumatol 2020;23:346-50.
  • 11. Wretborn J, Henricson J, Ekelund U, Wilhelms DB. Prevalence of crowding, boarding and staffing levels in Swedish emergency departments: a national cross sectional study. BMC Emerg Med 2020;20:50.
  • 12. Rowe BH, McRae A, Rosychuk RJ. Temporal trends in emergency department volumes and crowding metrics in a western Canadian province: a population-based, administrative data study. BMC Health Serv Res 2020;20:356.
  • 13. Simkhada P, Acharya S, Lama R, Dahal S, Lohola N, Thapa A. Emergency stay duration of patients in emergency department of a tertiary care hospital in Nepal: a descriptive cross-sectional study. JNMA J Nepal Med Assoc 2020;58:84-7.
  • 14. Velt KB, Cnossen M, Rood PP, et al. Emergency department overcrowding: a survey among European neurotrauma centres. Emerg Med J 2018;35:447-8.
  • 15. Yoon J, Bui LN, Govier DJ, Cahn MA, Luck J. Determinants of boarding of patients with severe mental illness in hospital emergency departments. J Ment Health Policy Econ 2020;23:61-75.
  • 16. Eiset AH, Kirkegaard H, Erlandsen M. Crowding in the emergency department in the absence of boarding: a transition regression model to predict departures and waiting time. BMC Med Res Methodol 2019;19:68.
  • 17. Wang Z, Xiong X, Wang S, Yan J, Springer M, Dellinger RP. Causes of emergency department overcrowding and blockage of access to critical services in Beijing: a 2-year study. J Emerg Med 2018;54:665-73.
  • 18. Kim J, Yun BJ, Aaronson EL, et al. The next step to reducing emergency department (ED) crowding: engaging specialist physicians. PLoS One 2018;13:e0201393.
  • 19. Tapia AD, Howard JT, Bebo NL, et al. A retrospective review of emergency department visits that may be appropriate for management in non-emergency settings. Mil Med 2022;187:e1153-9.
  • 20. Ellis CM, Esson MI. Crowd-out and emergency department utilization. J Health Econ 2021;80:102542.
  • 21. Kienbacher CL, Steinacher A, Fuhrmann V, Herkner H, Laggner AN, Roth D. Factors influencing door-to-triage- and triage-to-patient administration-time. Australas Emerg Care 2022;25:219-23.
  • 22. Ramedani S, George DR, Leslie DL, Kraschnewski J. The bystander effect: impact of rural hospital closures on the operations and financial well-being of surrounding healthcare institutions. J Hosp Med 2022;17:901-6.
  • 23. Allen L, Gian CT, Simon K. The impact of Medicaid expansion on emergency department wait times. Health Serv Res 2022;57:294-9.
  • 24. Noel G, Maghoo A, Piarroux J, Viudes G, Minodier P, Gentile S. Impact of viral seasonal outbreaks on crowding and health care quality in pediatric emergency departments. Pediatr Emerg Care 2021;37:e1239-43.
  • 25. Mallows JL. Effects of staff grade, overcrowding and presentations on emergency department performance: a regression model. Emerg Med Australas 2022;34:341-6.
  • 26. de Barros LB, Caldas LP, Bohomol E, Sarantopoulos A, Minatogawa V, Gasparino RC. Evaluation of waste related to the admission process of low-complexity patients in emergency services, in light of the lean healthcare philosophy. Int J Environ Res Public Health 2022;19:7044.
  • 27. Ramsey Z, Palter JS, Hardwick J, Moskoff J, Christian EL, Bailitz J. Decreased nursing staffing adversely affects emergency department throughput metrics. West J Emerg Med 2018;19:496-500.
  • 28. Vrijsen BE, Haitjema S, Westerink J, Hulsbergen-Veelken CA, van Solinge WW, Ten Berg MJ. Shorter laboratory turnaround time is associated with shorter emergency department length of stay: a retrospective cohort study. BMC Emerg Med 2022;22:207.
  • 29. Doupe MB, Chateau D, Chochinov A, et al. Comparing the effect of throughput and output factors on emergency department crowding: a retrospective observational cohort study. Ann Emerg Med 2018;72:410-9.
  • 30. Cheng I, Zwarenstein M, Kiss A, Castren M, Brommels M, Schull M. Factors associated with failure of emergency wait-time targets for high acuity discharges and intensive care unit admissions. CJEM 2018;20:112-24.
  • 31. van der Veen D, Remeijer C, Fogteloo AJ, Heringhaus C, de Groot B. Independent determinants of prolonged emergency department length of stay in a tertiary care centre: a prospective cohort study. Scand J Trauma Resusc Emerg Med 2018;26:81.
  • 32. Khorram-Manesh A, Wennman I, Andersson B, Dahlen Holmqvist L, Carlson T, Carlstrom E. Reasons for longer LOS at the emergency departments: practical, patient-centred, medical, or cultural? Int J Health Plann Manage 2019;34:e1586-96.
  • 33. Salehi L, Phalpher P, Valani R, Wan A, Herman J, Mercuri M. Time of transfer of admitted patients from the ED: a contributor to ED boarding in high-volume community hospitals. Healthc Q 2019;21:48-53.
  • 34. Driesen BE, van Riet BH, Verkerk L, Bonjer HJ, Merten H, Nanayakkara PW. Long length of stay at the emergency department is mostly caused by organisational factors outside the influence of the emergency department: a root cause analysis. PLoS One 2018;13:e0202751.
  • 35. Hoffmann JA, Stack AM, Monuteaux MC, Levin R, Lee LK. Factors associated with boarding and length of stay for pediatric mental health emergency visits. Am J Emerg Med 2019;37:1829-35.
  • 36. Fekri O, Manukyan E, Klazinga N. Associations between hospital deaths (HSMR), readmission and length of stay (LOS): a longitudinal assessment of performance results and facility characteristics of teaching and large-sized hospitals in Canada between 2013-2014 and 2017-2018. BMJ Open 2021;11:e041648.
  • 37. Lord K, Parwani V, Ulrich A, et al. Emergency department boarding and adverse hospitalization outcomes among patients admitted to a general medical service. Am J Emerg Med 2018;36:1246-8.
  • 38. Af Ugglas B, Djarv T, Ljungman PL, Holzmann MJ. Association between hospital bed occupancy and outcomes in emergency care: a cohort study in Stockholm Region, Sweden, 2012 to 2016. Ann Emerg Med 2020;76:179-90.
  • 39. Huang YB, Li SY, Hung SK, Tsai LH, Ng CJ, Chen SY. Emergency department crowdedness and emergency department cardiac arrest occurrence: an observational study in the COVID-19 pandemic. Signa Vitae 2022;18:33-9.
  • 40. Menon NV, Jayashree M, Nallasamy K, Angurana SK, Bansal A. Bed utilization and overcrowding in a high-volume tertiary level pediatric emergency department. Indian Pediatr 2021;58:723-5.
  • 41. Boulain T, Malet A, Maitre O. Association between long boarding time in the emergency department and hospital mortality: a single-center propensity score-based analysis. Intern Emerg Med 2020;15:479-89.
  • 42. Reznek MA, Upatising B, Kennedy SJ, Durham NT, Forster RM, Michael SS. Mortality associated with emergency department boarding exposure: are there differences between patients admitted to ICU and non-ICU settings? Med Care 2018;56:436-40.
  • 43. Huang KW, Yin CH, Chang R, Chen JS, Chen YS. Price for waiting: the adverse outcomes of boarding critically ill elderly medical patients in the emergency department. Postgrad Med J 2024;100:391-8.
  • 44. Eriksson J, Gellerstedt L, Hilleras P, Craftman AG. Registered nurses’ perceptions of safe care in overcrowded emergency departments. J Clin Nurs 2018;27:e1061-7.
  • 45. O Saggaf AM, Mugharbel A, Aboalola A, et al. Emergency department boarding of mechanically ventilated patients. Cureus 2022;14:e23990.
  • 46. Hsuan C, Segel JE, Hsia RY, Wang Y, Rogowski J. Association of emergency department crowding with inpatient outcomes. Health Serv Res 2023;58:828-43.
  • 47. Chang YH, Shih HM, Chen CY, Chen WK, Huang FW, Muo CH. Association of sudden in-hospital cardiac arrest with emergency department crowding. Resuscitation 2019;138:106-9.
  • 48. Mathews KS, Durst MS, Vargas-Torres C, Olson AD, Mazumdar M, Richardson LD. Effect of emergency department and ICU occupancy on admission decisions and outcomes for critically ill patients. Crit Care Med 2018;46:720-7.
  • 49. Jones PG, van der Werf B. Emergency department crowding and mortality for patients presenting to emergency departments in New Zealand. Emerg Med Australas 2021;33:655-64.
  • 50. Af Ugglas B, Lindmarker P, Ekelund U, Djarv T, Holzmann MJ. Emergency department crowding and mortality in 14 Swedish emergency departments, a cohort study leveraging the Swedish Emergency Registry (SVAR). PLoS One 2021;16:e0247881.
  • 51. Wessman T, Arnlov J, Carlsson AC, et al. The association between length of stay in the emergency department and short-term mortality. Intern Emerg Med 2022;17:233-40.
  • 52. Berg LM, Ehrenberg A, Florin J, Ostergren J, Discacciati A, Goransson KE. Associations between crowding and ten-day mortality among patients allocated lower triage acuity levels without need of acute hospital care on departure from the emergency department. Ann Emerg Med 2019;74:345-56.
  • 53. Eidsto A, Yla-Mattila J, Tuominen J, Huhtala H, Palomaki A, Koivistoinen T. Emergency department crowding increases 10-day mortality for non-critical patients: a retrospective observational study. Intern Emerg Med 2024;19:175-81.
  • 54. Jones S, Moulton C, Swift S, et al. Association between delays to patient admission from the emergency department and all-cause 30-day mortality. Emerg Med J 2022;39:168-73.
  • 55. Woodworth L. Swamped: emergency department crowding and patient mortality. J Health Econ 2020;70:102279.
  • 56. Af Ugglas B, Djarv T, Ljungman PL, Holzmann MJ. Emergency department crowding associated with increased 30-day mortality: a cohort study in Stockholm Region, Sweden, 2012 to 2016. J Am Coll Emerg Physicians Open 2020;1:1312-9.
  • 57. Joseph JW, Elhadad N, Mattison ML, et al. Boarding duration in the emergency department and inpatient delirium and severe agitation. JAMA Netw Open 2024;7:e2416343.
  • 58. Duquette CL, Andrew MK, Kuchel GA, Clarke JA, Ohle R, Verschoor CP. Emergency department boarding time is associated with functional decline in older adults six months post discharge. Can J Aging 2024;43:571-9.
  • 59. Turner AJ, Anselmi L, Lau YS, Sutton M. The effects of unexpected changes in demand on the performance of emergency departments. Health Econ 2020;29:1744-63.
  • 60. Paling S, Lambert J, Clouting J, Gonzalez-Esquerre J, Auterson T. Waiting times in emergency departments: exploring the factors associated with longer patient waits for emergency care in England using routinely collected daily data. Emerg Med J 2020;37:781-6.
  • 61. Pryce A, Unwin M, Kinsman L, McCann D. Delayed flow is a risk to patient safety: a mixed method analysis of emergency department patient flow. Int Emerg Nurs 2021;54:100956.
  • 62. Stephens AS, Broome RA. Impact of emergency department occupancy on waiting times, rates of admission and representation, and length of stay when hospitalised: a data linkage study. Emerg Med Australas 2019;31:555-61.
  • 63. Huang Y, Ortiz SS, Rowe BH, Rosychuk RJ. Emergency department crowding negatively influences outcomes for adults presenting with asthma: a population-based retrospective cohort study. BMC Emerg Med 2022;22:209.
  • 64. Stoyanov KM, Biener M, Hund H, et al. Effects of crowding in the emergency department on the diagnosis and management of suspected acute coronary syndrome using rapid algorithms: an observational study. BMJ Open 2020;10:e041757.
  • 65. Alemu GH, Negari KG, Rodamo KM, Hirigo AT. Factors associated with the length of stay in emergency departments in Southern-Ethiopia. BMC Res Notes 2019;12:239.
  • 66. Sagaidak S, Rowe BH, Ospina MB, Rosychuk RJ. Emergency department crowding negatively influences outcomes for children presenting with asthma: a population-based retrospective cohort study. Pediatr Res 2021;89:679-85.
  • 67. Jaffe TA, Goldstein JN, Yun BJ, et al. Impact of emergency department crowding on delays in acute stroke care. West J Emerg Med 2020;21:892-9.
  • 68. Momeni M, Vahidi E, Seyedhosseini J, Jarchi A, Naderpour Z, Saeedi M. Emergency overcrowding impact on the quality of care of patients presenting with acute stroke. Adv J Emerg Med 2017;2:e3.
  • 69. Peltan ID, Bledsoe JR, Oniki TA, et al. Emergency department crowding is associated with delayed antibiotics for sepsis. Ann Emerg Med 2019;73:345-55.
  • 70. Kim DA, Sanchez LD, Chiu D, Brown IP. Social determinants of hallway bed use. West J Emerg Med 2020;21:949-58.
  • 71. Lane DJ, Roberts L, Currie S, Grimminck R, Lang E. Association of emergency department boarding times on hospital length of stay for patients with psychiatric illness. Emerg Med J 2022;39:494-500.
  • 72. Dormont B, Dottin A. Does the opening of an emergency department influence hospital admissions? Evidence from French private hospitals. Soc Sci Med 2024;340:116380.
  • 73. Brathwaite D, Strain A, Waller AE, Weinberger M, Stearns SC. The effect of increased emergency department demand on throughput times and disposition status for pediatric psychiatric patients. Am J Emerg Med 2023;64:174-83.
  • 74. Doan Q, Wong H, Meckler G, et al. The impact of pediatric emergency department crowding on patient and health care system outcomes: a multicentre cohort study. CMAJ 2019;191:E627-35.
  • 75. Ouyang H, Wang J, Sun Z, Lang E. The impact of emergency department crowding on admission decisions and patient outcomes. Am J Emerg Med 2022;51:163-8.
  • 76. Jung HM, Kim MJ, Kim JH, et al. The effect of overcrowding in emergency departments on the admission rate according to the emergency triage level. PLoS One 2021;16:e0247042.
  • 77. Chiu IM, Lin YR, Syue YJ, Kung CT, Wu KH, Li CJ. The influence of crowding on clinical practice in the emergency department. Am J Emerg Med 2018;36:56-60.
  • 78. Chen W, Linthicum B, Argon NT, et al. The effects of emergency department crowding on triage and hospital admission decisions. Am J Emerg Med 2020;38:774-9.
  • 79. Chou SC, Chang YC, Chen PC, Schuur JD, Weiner SG. Hospital occupancy and its effect on emergency department evaluation. Ann Emerg Med 2022;79:172-81.
  • 80. Wyatt S, Joshi R, Mortimore JM, Mohammed MA. Relationship between emergency department and inpatient occupancy and the likelihood of an emergency admission: a retrospective hospital database study. Emerg Med J 2022;39:174-80.
  • 81. McRae AD, Rowe BH, Usman I, et al. A comparative evaluation of the strengths of association between different emergency department crowding metrics and repeat visits within 72 hours. CJEM 2022;24:27-34.
  • 82. Rathlev NK, Visintainer P, Schmidt J, Hettler J, Albert V, Li H. Patient characteristics and clinical process predictors of patients leaving without being seen from the emergency department. West J Emerg Med 2020;21:1218-26.
  • 83. Gorski JK, Arnold TS, Usiak H, Showalter CD. Crowding is the strongest predictor of left without being seen risk in a pediatric emergency department. Am J Emerg Med 2021;48:73-8.
  • 84. Reznek MA, Larkin CM, Scheulen JJ, Harbertson CA, Michael SS. Operational factors associated with emergency department patient satisfaction: analysis of the Academy of Administrators of Emergency Medicine/Association of Academic Chairs of Emergency Medicine national survey. Acad Emerg Med 2021;28:753-60.
  • 85. Berlyand Y, Copenhaver MS, White BA, et al. Impact of emergency department crowding on discharged patient experience. West J Emerg Med 2022;24:185-92.
  • 86. Wang S, Gao JY, Li X, et al. Correlation between crowdedness in emergency departments and anxiety in Chinese patients. World J Clin Cases 2020;8:2802-16.
  • 87. Liyanage-Don NA, Edelman DS, Chang BP, Schultebraucks K, Thanataveerat A, Kronish IM. Associations between emergency department crowding and perceptions of interpersonal care in patients presenting with suspected acute coronary syndrome. Emerg Med J 2022;39:186-90.
  • 88. Wubben BM, Chmielewski N, Van Heukelom P, Wittrock C. The impact of emergency department crowding and patient boarding on resident point-of-care ultrasound education. J Am Coll Emerg Physicians Open 2024;5:e13220.
  • 89. Moffett P, Best A, Lewis N, et al. The effect of hospital boarding on emergency medicine residency productivity. West J Emerg Med 2025;26:53-61.
  • 90. McNaughton CD, Bonnet K, Schlundt D, et al. Rural interfacility emergency department transfers: framework and qualitative analysis. West J Emerg Med 2020;21:858-65.
  • 91. Moore N, Middleton PM, Ren S. Lost capacity in emergency departments and its economic implications: a simulation study and economic analysis. Emerg Med Australas 2020;32:974-9.
  • 92. Napoli AM, Ali S, Baird J, Shanin D, Jouriles N. Extremes of emergency department boarding are associated with poorer financial performance among hospitals. J Healthc Manag 2024;69:219-30.
  • 93. Somers S, Brown J, Fitzpatrick S, Landi C, Gingold DB, Marcozzi D. Innovative use of emergency medicine providers in an urban setting to reduce overutilization of 9-1-1. J Emerg Med 2020;59:836-42.
  • 94. Baan-Kooman EC, Mol S, van der Linden MC, Gaakeer MI, de Ridder VA. Emergency department crowding in the Netherlands: evaluation of a real-time ambulance diversion dashboard. Int J Emerg Med 2025;18:18.
  • 95. Ansah JP, Ahmad S, Lee LH, et al. Modeling Emergency Department crowding: restoring the balance between demand for and supply of emergency medicine. PLoS One 2021;16:e0244097.
  • 96. Morreel S, Homburg I, Philips H, et al. Cost effects of nurse led triage at an emergency department with the advice to consult the adjacent general practice cooperative for low-risk patients, a cluster randomised trial. Health Policy 2022;126:980-7.
  • 97. Liedes-Kauppila M, Heikkinen AM, Rahkonen O, et al. Development of the use of primary health care emergency departments after interventions aimed at decreasing overcrowding: a longitudinal follow-up study. BMC Emerg Med 2022;22:108.
  • 98. Feral-Pierssens AL, Morris J, Marquis M, et al. Safety assessment of a redirection program using an electronic application for low-acuity patients visiting an emergency department. BMC Emerg Med 2022;22:71.
  • 99. Wartelle A, Mourad-Chehade F, Yalaoui F, et al. Multimorbidity clustering of the emergency department patient flow: impact analysis of new unscheduled care clinics. PLoS One 2022;17:e0262914.
  • 100. Adriani L, Dall’Oglio I, Brusco C, et al. Reduction of waiting times and patients leaving without being seen in the tertiary pediatric emergency department: a comparative observational study. Pediatr Emerg Care 2022;38:219-23.
  • 101. Sachs CJ, Yu CK, Nauka PC, Schriger DL. Systems opportunities to reduce ED crowding from nonemergency referrals. Am J Med Qual 2018;33:37-42.
  • 102. Reid S, Bhatt M, Zemek R, Tse S. Virtual care in the pediatric emergency department: a new way of doing business? CJEM 2021;23:80-4.
  • 103. Sommers-Olson B, Christianson J, Neumann T, et al. Reducing nonemergent visits to the emergency department in a veterans affairs multistate system. J Emerg Nurs 2023;49:539-45.
  • 104. Thakkar AB, Chandrashekar P, Wang W, Blanchfield BB. Impact of a student-run clinic on emergency department utilization. Fam Med 2019;51:420-3.
  • 105. Xu Y, Ho V. Freestanding emergency departments in Texas do not alleviate congestion in hospital-based emergency departments. Am J Emerg Med 2020;38:471-6.
  • 106. Fleming A, Whitty K. Implementation of a standardized protocol for telehealth provider in triage to improve efficiency and ED throughput: a quality improvement project. Adv Emerg Nurs J 2022;44:312-21.
  • 107. Rademacher NJ, Cole G, Psoter KJ, et al. Use of telemedicine to screen patients in the emergency department: matched cohort study evaluating efficiency and patient safety of telemedicine. JMIR Med Inform 2019;7:e11233.
  • 108. Furmaga J, McDonald SA. Impact of rapid medical evaluation on patient flow in an urban emergency department. J Med Syst 2021;45:63.
  • 109. Napoli AM, Ali S, Lawrence A, Baird J. Boarding is associated with reduced emergency department efficiency that is not mitigated by a provider in triage. West J Emerg Med 2020;21:647-52.
  • 110. Jesionowski M, Riordan J, Quatrara B. Does a provider in triage and rapid medical evaluation help with left without being seen rates and ED crowding? J Emerg Nurs 2019;45:38-45.
  • 111. Spencer S, Stephens K, Swanson-Biearman B, Whiteman K. Health care provider in triage to improve outcomes. J Emerg Nurs 2019;45:561-6.
  • 112. Gardner RM, Friedman NA, Carlson M, Bradham TS, Barrett TW. Impact of revised triage to improve throughput in an ED with limited traditional fast track population. Am J Emerg Med 2018;36:124-7.
  • 113. Anderson JS, Burke RC, Augusto KD, et al. The effect of a rapid assessment zone on emergency department operations and throughput. Ann Emerg Med 2020;75:236-45.
  • 114. Heslin SM, Francis A, Cloney R, et al. Team triage increases discharges and decreases time to discharge without increasing test ordering. J Am Coll Emerg Physicians Open 2021;2:e12311.
  • 115. Morais Oliveira M, Marti C, Ramlawi M, et al. Impact of a patient-flow physician coordinator on waiting times and length of stay in an emergency department: a before-after cohort study. PLoS One 2018;13:e0209035.
  • 116. Sobolewski KA, Koo S, Deutsch RJ. Improving the flow: optimization of available triage standing medication orders in the pediatric emergency department. Pediatr Emerg Care 2022;38:157-61.
  • 117. Zaboli A, Pfeifer N, Solazzo P, et al. Blood sampling during nurse triage reduces patient length of stay in the emergency department: a propensity score-weighted, population-based study. Int Emerg Nurs 2020;49:100826.
  • 118. Stopyra JP, Snavely AC, Ashburn NP, et al. EMS blood collection from patients with acute chest pain reduces emergency department length of stay. Am J Emerg Med 2021;47:248-52.
  • 119. Garrett JS, Berry C, Wong H, Qin H, Kline JA. The effect of vertical split-flow patient management on emergency department throughput and efficiency. Am J Emerg Med 2018;36:1581-4.
  • 120. Wallingford G, Joshi N, Callagy P, Stone J, Brown I, Shen S. Introduction of a horizontal and vertical split flow model of emergency department patients as a response to overcrowding. J Emerg Nurs 2018;44:345-52.
  • 121. Martin HA, Noble M, Wilmarth J. Improving patient flow and decreasing patient length of stay in the pediatric emergency department through implementation of a fast track. Adv Emerg Nurs J 2021;43:162-9.
  • 122. Easter B, Houshiarian N, Pati D, Wiler JL. Designing efficient emergency departments: discrete event simulation of internal-waiting areas and split flow sorting. Am J Emerg Med 2019;37:2186-93.
  • 123. Jimenez-Barragan M, Rodriguez-Oliva M, Sanchez-Mora C, et al. Emergency severity level-3 patient flow based on point-of-care testing improves patient outcomes. Clin Chim Acta 2021;523:144-51.
  • 124. Hausfater P, Hajage D, Bulsei J, et al. Impact of point-of-care testing on length of stay of patients in the emergency department: a cluster-randomized controlled study. Acad Emerg Med 2020;27:974-83.
  • 125. Perotte R, Lewin GO, Tambe U, et al. Improving emergency department flow: reducing turnaround time for emergent CT scans. AMIA Annu Symp Proc 2018;2018:897-906.
  • 126. Trotzky D, Posner L, Mosery J, Cohen A, Avisar S, Pachys G. Do automatic push notifications improve patient flow in the emergency department? Analysis of an ED in a large medical center in Israel. PLoS One 2021;16:e0258169.
  • 127. Al-Na’seh MH, Elheet AM, Alhayek AM, Sabri AT, Al Owaidat AK. Optimizing emergency department length of stay and quality of care: a quality improvement project. Cureus 2024;16:e71989.
  • 128. Spiegelman L, Jen M, Matonis D, et al. The effects of implementing a “waterfall” emergency physician attending schedule. West J Emerg Med 2021;22:882-9.
  • 129. Amorim FF, Almeida KJ, Barbalho SC, et al. Reducing overcrowding in an emergency department: a pilot study. Rev Assoc Med Bras (1992) 2019;65:1476-81.
  • 130. Harhara T, Ibrahim H, Gaba WH, Kamour AM. Development of an acute medical unit to optimize patient flow and early discharges in a tertiary care hospital in the United Arab Emirates. BMC Health Serv Res 2022;22:1447.
  • 131. Ok M, Choi A, Kim MJ, et al. Emergency short-stay wards and boarding time in emergency departments: a propensity-score matching study. Am J Emerg Med 2020;38:2495-9.
  • 132. Patey C, Norman P, Araee M, et al. SurgeCon: priming a community emergency department for patient flow management. West J Emerg Med 2019;20:654-65.
  • 133. Improta G, Romano M, Di Cicco MV, et al. Lean thinking to improve emergency department throughput at AORN Cardarelli hospital. BMC Health Serv Res 2018;18:914.
  • 134. Le DX, Do HT, Bui KT, et al. Lean management for improving hospital waiting times: case study of a Vietnamese public/general hospital emergency department. Int J Health Plann Manage 2022;37:156-70.
  • 135. Van der Linden MC, Van Loon-van Gaalen MM, Meylaerts SA, et al. Improving emergency department flow by introducing four interventions simultaneously: a quality improvement project. Int Emerg Nurs 2024;76:101499.
  • 136. Imhoff B, Marshall K, Nazir N, Pal A, Parkhurst M. Reducing time to admission in emergency department patients: a cross-functional quality improvement project. BMJ Open Qual 2022;11:e001987.
  • 137. Rathlev NK, Anderson J, Schmidt J, et al. Key players in key roles: the baystate patient progress initiative to improve emergency department efficiency and productivity. J Emerg Nurs 2018;44:123-31.
  • 138. Nesbitt J, Mitarai T, Chan GK, et al. Effect of emergency critical care nurses and emergency department boarding time on in-hospital mortality in critically ill patients. Am J Emerg Med 2021;41:120-4.
  • 139. Lee EE, Kwok ES, Vaillancourt C. Using emergency physicians’ abilities to predict patient admission to decrease admission delay time. Emerg Med J 2020;37:417-22.
  • 140. d’Etienne JP, Zhou Y, Kan C, et al. Two-step predictive model for early detection of emergency department patients with prolonged stay and its management implications. Am J Emerg Med 2021;40:148-58.
  • 141. Esposito J, Lavelle J, M’Farrej M, et al. Responding to a behavioral health crisis: applying a new care model in the emergency department. Pediatr Emerg Care 2022;38:e1147-50.
  • 142. Alishahi Tabriz A, Trogdon JG, Fried BJ. Association between adopting emergency department crowding interventions and emergency departments’ core performance measures. Am J Emerg Med 2020;38:258-65.
  • 143. Potts L, Ryan C, Diegel-Vacek L, Murchek A. Improving patient flow from the emergency department utilizing a standardized electronic nursing handoff process. J Nurs Adm 2018;48:432-6.
  • 144. Ben Shoham A, Munter G. The association between hallway boarding in internal wards, readmission and mortality rates: a comparative, retrospective analysis, following a policy change. Isr J Health Policy Res 2021;10:8.
  • 145. Alishahi Tabriz A, Birken SA, Shea CM, Fried BJ, Viccellio P. What is full capacity protocol, and how is it implemented successfully? Implement Sci 2019;14:73.
  • 146. Moon SW, Lee JH, Lee HS, et al. Effects of emergency transfer coordination center on length of stay of critically ill patients in the emergency department. West J Emerg Med 2022;23:846-54.
  • 147. Cheng L, Tapia M, Menzel K, Page M, Ellis W. Predicting need for hospital beds to reduce emergency department boarding. Perm J 2022;26:14-20.
  • 148. Yun CC, Huang SJ, Kuo T, Li YC, Juang WC. Impact of new bed assignment information system on emergency department length of stay: an effect evaluation for lean intervention by using interrupted time series and propensity score matching analysis. Int J Environ Res Public Health 2022;19:5364.
  • 149. Lee AH, Berlyand Y, Dunn PF, et al. Level-loading a health system by transferring emergency department patients to a community hospital: prospective cohort study. Am J Emerg Med 2022;60:29-33.
  • 150. Lee AH, Dunn PF, Cooper S, et al. COVID-19 level-loading: transferring emergency department patients to a partner academic medical center within a healthcare system. Am J Med Qual 2021;36:368-70.
  • 151. Bodnar B, Kane EM, Rupani H, et al. Bed downtime: the novel use of a quality metric allows inpatient providers to improve patient flow from the emergency department. Emerg Med J 2022;39:224-9.
  • 152. DeAnda R. Stop the bottleneck: improving patient throughput in the emergency department. J Emerg Nurs 2018;44:582-8.
  • 153. Kilaru AS, Resnick D, Flynn D, et al. Practical alternative to hospitalization for emergency department patients (PATH): a feasibility study. Healthc (Amst) 2021;9:100545.
  • 154. Shin S, Lee SH, Kim DH, et al. The impact of the improvement in internal medicine consultation process on ED length of stay. Am J Emerg Med 2018;36:620-4.
  • 155. Hammer C, DePrez B, White J, Lewis L, Straughen S, Buchheit R. Enhancing hospital-wide patient flow to reduce emergency department crowding and boarding. J Emerg Nurs 2022;48:603-9.
  • 156. Mendlovic J, Zalut T, Munter G, Merin O, Yinnon AM, Katz DE. Mixed effect of increasing outflow of medical patients from an emergency department. Isr J Health Policy Res 2021;10:59.
  • 157. Alotaibi Y, Alnowaiser N, Alamry A. Improving hospital flow ‘Ensiab Project’. BMJ Open Qual 2021;10:e001505.
  • 158. Moroco DM, Pazin-Filho A. Decreasing boarders in the emergency department by reducing clerical work in the discharge process of in-hospital patients in Brazil: an interrupted time-series analysis. BMC Emerg Med 2022;22:99.
  • 159. Kausar K, Coffield E, Tarkovsky R, Alvarez MA, Hochman KA, Press RA. Implementing and evaluating a discharge before noon initiative in a large tertiary care urban hospital. Jt Comm J Qual Patient Saf 2024;50:127-38.
  • 160. Woods R, Sandoval R, Vermillion G, et al. The discharge lounge: a patient flow process solution. J Nurs Care Qual 2020;35:240-4.
  • 161. Keogh B, Monks T. The impact of delayed transfers of care on emergency departments: common sense arguments, evidence and confounding. Emerg Med J 2020;37:95-101.
  • 162. Forero R, Man N, Ngo H, et al. Impact of the four-hour National Emergency Access Target on 30 day mortality, access block and chronic emergency department overcrowding in Australian emergency departments. Emerg Med Australas 2019;31:58-66.
  • 163. Nahidi S, Forero R, McCarthy S, et al. Qualitative analysis of perceptions and experiences of emergency department staff in relation to implementation and outcomes of the Four-Hour Rule/National Emergency Access Target in Australia. Emerg Med Australas 2019;31:378-86.
  • 164. Lee JH, Kim JH, Park I, et al. Effect of a boarding restriction protocol on emergency department crowding. Yonsei Med J 2022;63:470-9.
  • 165. Flowers LM, Maass KT, Melin GJ, et al. Consequences of the 48-h rule: a lens into the psychiatric patient flow through an emergency department. Am J Emerg Med 2018;36:2029-34.
  • 166. Hrycko A, Tiwari V, Vemula M, et al. A hospitalist-led team to manage patient boarding in the emergency department: impact on hospital length of stay and cost. South Med J 2019;112:599-603.
  • 167. Haq N, Stewart-Corral R, Hamrock E, Perin J, Khaliq W. Emergency department throughput: an intervention. Intern Emerg Med 2018;13:923-31.
  • 168. Deng L, Cheng F, Gao X, et al. Hospital crowdedness evaluation and in-hospital resource allocation based on image recognition technology. Sci Rep 2023;13:299.
  • 169. Wartelle A, Mourad-Chehade F, Yalaoui F, Laplanche D, Sanchez S. Analysis of saturation in the emergency department: a data-driven queuing model using machine learning. Stud Health Technol Inform 2022;294:88-92.
  • 170. Taylor B, Young M. Pediatric ED departmental complexity: a different approach to the concept of ED crowding. CJEM 2022;24:318-24.
  • 171. Harrou F, Kadri F, Sun Y, Khadraoui S. Monitoring patient flow in a hospital emergency department: aRMA-based nonparametric GLRT scheme. Health Informatics J 2021;27:14604582211021649.
  • 172. Cheng Q, Argon NT, Evans CS, Liu Y, Platts-Mills TF, Ziya S. Forecasting emergency department hourly occupancy using time series analysis. Am J Emerg Med 2021;48:177-82.
  • 173. Cheng N, Kuo A. Using Long Short-Term Memory (LSTM) neural networks to predict emergency department wait time. Stud Health Technol Inform 2020;270:1425-6.
  • 174. Vainieri M, Panero C, Coletta L. Waiting times in emergency departments: a resource allocation or an efficiency issue? BMC Health Serv Res 2020;20:549.
  • 175. Wu L, Chen X, Khalemsky A, et al. The association between emergency department length of stay and in-hospital mortality in older patients using machine learning: an observational cohort study. J Clin Med 2023;12:4750.
  • 176. Chang YH, Shih HM, Wu JE, et al. Machine learning-based triage to identify low-severity patients with a short discharge length of stay in emergency department. BMC Emerg Med 2022;22:88.
  • 177. Tello M, Reich ES, Puckey J, et al. Machine learning based forecast for the prediction of inpatient bed demand. BMC Med Inform Decis Mak 2022;22:55.
  • 178. Yao LH, Leung KC, Tsai CL, Huang CH, Fu LC. A novel deep learning-based system for triage in the emergency department using electronic medical records: retrospective cohort study. J Med Internet Res 2021;23:e27008.
  • 179. Lucini FR, Reis MA, Silveira GJ, et al. Man vs. machine: predicting hospital bed demand from an emergency department. PLoS One 2020;15:e0237937.
  • 180. Noel G, Bonte N, Persico N, et al. Real-time estimation of inpatient beds required in emergency departments. Eur J Emerg Med 2019;26:440-5.
  • 181. Parker CA, Liu N, Wu SX, Shen Y, Lam SS, Ong ME. Predicting hospital admission at the emergency department triage: a novel prediction model. Am J Emerg Med 2019;37:1498-504.
  • 182. Lee SY, Chinnam RB, Dalkiran E, Krupp S, Nauss M. Prediction of emergency department patient disposition decision for proactive resource allocation for admission. Health Care Manag Sci 2020;23:339-59.
  • 183. Hargreaves D, Snel S, Dewar C, Arjan K, Parrella P, Hodgson LE. Validation of the National Emergency Department Overcrowding Score (NEDOCS) in a UK non-specialist emergency department. Emerg Med J 2020;37:801-6.
  • 184. Ilhan B, Kunt MM, Damarsoy FF, Demir MC, Aksu NM. NEDOCS: is it really useful for detecting emergency department overcrowding today? Medicine (Baltimore) 2020;99:e20478.
  • 185. Getachew M, Musa I, Degefu N, Beza L, Hawlte B, Asefa F. Emergency department overcrowding and its associated factors at HARME medical emergency center in Eastern Ethiopia. Afr J Emerg Med 2024;14:26-32.
  • 186. Altun M, Kudu E, Demir O, et al. Effect of access block on emergency department crowding calculated by NEDOCS score. Am J Emerg Med 2024;82:136-41.
  • 187. Colella Y, Di Laura D, Borrelli A, Triassi M, Amato F, Improta G. Overcrowding analysis in emergency department through indexes: a single center study. BMC Emerg Med 2022;22:181.
  • 188. Improta G, Majolo M, Raiola E, Russo G, Longo G, Triassi M. A case study to investigate the impact of overcrowding indices in emergency departments. BMC Emerg Med 2022;22:143.
  • 189. Jobe J, Donneau AF, Scholtes B, Ghuysen A. Quantifying emergency department crowding: comparison between two scores. Acta Clin Belg 2018;73:207-12.
  • 190. Ahalt V, Argon NT, Ziya S, Strickler J, Mehrotra A. Comparison of emergency department crowding scores: a discrete-event simulation approach. Health Care Manag Sci 2018;21:144-55.
  • 191. Clouston R, Atkinson P, Canales DD, et al. Emergency department occupancy is useful as a simple real-time measure of crowding. CJEM 2022;24:23-6.
  • 192. Wretborn J, Starkenberg H, Ruge T, Wilhelms DB, Ekelund U. Validation of the modified Skane emergency department assessment of patient load (mSEAL) model for emergency department crowding and comparison with international models: an observational study. BMC Emerg Med 2021;21:21.
  • 193. Jones P, Wells S, Ameratunga S. Towards a best measure of emergency department crowding: lessons from current Australasian practice. Emerg Med Australas 2018;30:214-21.
  • 194. Ioannides KL, Dekker AM, Shin ME, Schriger DL. Ambulances required to relieve overcapacity hospitals: a novel measure of hospital strain during the COVID-19 pandemic in the United States. Ann Emerg Med 2022;80:301-13.
  • 195. Karpman M, Menon N, Turcotte J. Polynomial modeling of emergency department arrivals: an analysis of local and national utilization patterns. J Emerg Manag 2021;19:189-96.
  • 196. Heaton HA, Nestler DM, Pasupathy KS, Wutthisirisart P, Sir MY, Hellmich TR. Unnecessary wait hours: a novel ED system-level metric. Emerg Med J 2020;37:552-4.
  • 197. Stenson BA, Joseph JW, Antkowiak PS, Chiu DT, Sanchez LD. Understanding demand and capacity mismatch in an academic emergency department using a staircase model of provider capacity and staggered shift start times. J Emerg Med 2021;61:336-43.
  • 198. Walsh B, Smith S, Wren MA, Eighan J, Lyons S. The impact of inpatient bed capacity on length of stay. Eur J Health Econ 2022;23:499-510.
  • 199. Middleton PM, Moore N, Ren S. Lost bed capacity in emergency departments: a descriptive analysis and data visualisation exploration. Emerg Med Australas 2021;33:310-5.
  • 200. Ataman MG, Sariyer G. Predicting waiting and treatment times in emergency departments using ordinal logistic regression models. Am J Emerg Med 2021;46:45-50.
  • 201. Cummins NM, Garavan C, Barry LA, et al. The impact of COVID-19 on an Irish Emergency Department (ED): a cross-sectional study exploring the factors influencing ED utilisation prior to and during the pandemic from the patient perspective. BMC Emerg Med 2022;22:176.
  • 202. Tseng WM, Lin PH, Wu PC, Kao CH. Examining patient flow in a tertiary hospital’s emergency department at a low coronavirus prevalence region. BMC Emerg Med 2022;22:137.
  • 203. Savioli G, Ceresa IF, Guarnone R, et al. Impact of coronavirus disease 2019 pandemic on crowding: a call to action for effective solutions to “access block”. West J Emerg Med 2021;22:860-70.
  • 204. Savioli G, Ceresa IF, Novelli V, Ricevuti G, Bressan MA, Oddone E. How the coronavirus disease 2019 pandemic changed the patterns of healthcare utilization by geriatric patients and the crowding: a call to action for effective solutions to the access block. Intern Emerg Med 2022;17:503-14.
  • 205. Butt AA, Azad AM, Kartha AB, Masoodi NA, Bertollini R, Abou-Samra AB. Volume and acuity of emergency department visits prior to and after COVID-19. J Emerg Med 2020;59:730-4.
  • 206. Xu S, Glenn S, Sy L, et al. Impact of the COVID-19 pandemic on health care utilization in a large integrated health care system: retrospective cohort study. J Med Internet Res 2021;23:e26558.
  • 207. Panovska-Griffiths J, Ross J, Elkhodair S, Baxter-Derrington C, Laing C, Raine R. Exploring overcrowding trends in an inner city emergence department in the UK before and during COVID-19 epidemic. BMC Emerg Med 2021;21:43.
  • 208. Houshyar R, Tran-Harding K, Glavis-Bloom J, et al. Effect of shelter-in-place on emergency department radiology volumes during the COVID-19 pandemic. Emerg Radiol 2020;27:781-4.
  • 209. Comelli I, Scioscioli F, Cervellin G. Impact of the COVID-19 epidemic on census, organization and activity of a large urban emergency department. Acta Biomed 2020;91:45-9.
  • 210. Lorenzen B, Schwartz A. Changes in emergency department patient volume and acuity associated with early stages of the COVID-19 pandemic in a unique environment. Perm J 2021;25:20.212.
  • 211. Mann E, Swedien D, Hansen J, et al. Reduction in emergency department presentations in a regional health system during the COVID-19 pandemic. West J Emerg Med 2021;22:842-50.
  • 212. Reno EM, Li BH, Eutermoser M, Davis CB, Haukoos JS, Shy BD. Temporal associations between emergency department and telehealth volumes during the COVID-19 pandemic: a time-series analysis from 2 academic medical centers. Am J Emerg Med 2022;54:238-41.
  • 213. Oskvarek JJ, Zocchi MS, Black BS, et al. Emergency department volume, severity, and crowding since the onset of the coronavirus disease 2019 pandemic. Ann Emerg Med 2023;82:650-60.
  • 214. Yilmaz A, Esenay FI. An evaluation of pediatric emergency cases in the first year of the COVID-19 pandemic: a single-centre experience. J Pediatr Nurs 2024;74:17-22.
  • 215. Lee YT, Lai YW, Chen JH, Chen WL, Wu MY, Chung JY. Assessing the impact of the COVID-19 pandemic on pediatric emergency department visits in Taiwan. Medicina (Kaunas) 2024;60:288.
  • 216. Kilaru AS, Scheulen JJ, Harbertson CA, Gonzales R, Mondal A, Agarwal AK. Boarding in US academic emergency departments during the COVID-19 pandemic. Ann Emerg Med 2023;82:247-54.
  • 217. Sangal RB, Peaper DR, Rothenberg C, et al. Universal SARS-CoV-2 testing of emergency department admissions increases emergency department length of stay. Ann Emerg Med 2022;79:182-6.
  • 218. Guo F, Qin Y, Fu H, Xu F. The impact of COVID-19 on emergency department length of stay for urgent and life-threatening patients. BMC Health Serv Res 2022;22:696.
  • 219. Van Der Linden MC, Kunz L, Van Loon-Van Gaalen M, Van Woerden G, Van Der Linden N. Association between COVID-19 surge and emergency department patient flow and experience. Int Emerg Nurs 2023;66:101241.
  • 220. Janke AT, Melnick ER, Venkatesh AK. Hospital occupancy and emergency department boarding during the COVID-19 pandemic. JAMA Netw Open 2022;5:e2233964.
  • 221. Zipursky AR, Olson KL, Bode L, et al. Emergency department visits and boarding for pediatric patients with suicidality before and during the COVID-19 pandemic. PLoS One 2023;18:e0286035.
  • 222. Af Ugglas B, Skyttberg N, Wladis A, Djarv T, Holzmann MJ. Emergency department crowding and hospital transformation during COVID-19, a retrospective, descriptive study of a university hospital in Stockholm, Sweden. Scand J Trauma Resusc Emerg Med 2020;28:107.
  • 223. Sandhu P, Shah AB, Ahmad FB, et al. Emergency department and intensive care unit overcrowding and ventilator shortages in US hospitals during the COVID-19 pandemic, 2020-2021. Public Health Rep 2022;137:796-802.
  • 224. Stuart S, Bannerman G, Jackson M. Drive-through medicine: a novel health care delivery mechanism for the COVID-19 pandemic. J Emerg Med 2022;63:747-54.
  • 225. Moskovitz JB, Khambhati K, Sasson C, D’Amore J, Jones MP, Sperling J. Impact of a novel emergency department forward treatment area during the New York City COVID-19 surge. West J Emerg Med 2021;22:871-7.
  • 226. Miller NM, Jones I, Russ S, et al. A model for rapid emergency department expansion for the COVID-19 pandemic. Am J Emerg Med 2020;38:2065-9.
  • 227. Paganini M, Pizzato M, Weinstein E, et al. Implementation of a nurse-led alternate care site for the management of the surge of patients with COVID-19 in an Italian emergency department. Emerg Med J 2022;39:554-8.
  • 228. Barten DG, Kusters RW, Peters NA. A swift and dynamic strategy to expand emergency department capacity for COVID-19. Disaster Med Public Health Prep 2022;16:1190-3.
  • 229. Kilaru AS, Porges SB, Grossman L, et al. An accelerated hospital observation pathway to reduce length of stay for patients with COVID-19. Am J Manag Care 2022;28:262-8.
  • 230. Berlyand Y, Baugh JJ, Lee AH, et al. Evaluation of a COVID-19 emergency department observation protocol. Am J Emerg Med 2022;56:205-10.

Download Citation

Download a citation file in RIS format that can be imported by all major citation management software, including EndNote, ProCite, RefWorks, and Reference Manager.

Format:

Include:

Emergency department crowding in the modern era: a systematic review (2018–2025)
Clin Exp Emerg Med. 2026;13(2):119-139.   Published online January 14, 2026
DOI: https://doi.org/10.15441/ceem.25.172
Download Citation

Download a citation file in RIS format that can be imported by all major citation management software, including EndNote, ProCite, RefWorks, and Reference Manager.
Format:
Include:
Emergency department crowding in the modern era: a systematic review (2018–2025)
Clin Exp Emerg Med. 2026;13(2):119-139.   Published online January 14, 2026
DOI: https://doi.org/10.15441/ceem.25.172
Close

Figure

  • 0

Related topics

Emergency department crowding in the modern era: a systematic review (2018–2025)
Image
Fig. 1. PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) flow diagram.

Fig. 1.

Emergency department crowding in the modern era: a systematic review (2018–2025)
Cause Reference
Recent progress [6]
Geographical study [7–14]
Type of patients
 Medical [9]
 Surgical [10]
 Psychiatric [15]
Input factor
 New arrivals to the ED [16]
 Excessive low acuity visits [17]
 Excessive high acuity visits [17,18,21]
 Poor access to primary care [19,20]
 After hours of nonemergency healthcare facilities [21]
 Need for specialty consultation [17,18]
 Seasonal illnesses (e.g., influenza) [24]
 Expansion of Medicaid [23]
 Closure of neighboring hospitals [22]
Throughput factor
 Staffing shortages/misallocation of staff [25–27,29]
 Lab and imaging delays [28–31]
 Physician decision making [32]
Output factor
 Late discharges [33]
 Inpatient bed occupancy [34]
 Psychiatric bed unavailability [35]
Effect Reference
Adverse outcome
 No increase in in-hospital mortality [36–39]
 Increased in-hospital mortality [40–48]
 Increased 7-day mortality [49–51]
 Increased 10-day mortality [52,53]
 Increased 30-day mortality [51,54–56]
 Worse outcomes for older patients [57,58]
Delayed assessment and care
 Longer wait times [59–62]
 Door-to-doctor time [63]
 Increased lab turnaround time [64,65]
 Delayed care for asthma patients [66]
 No delay in stroke care [67,68]
 Door-to-antibiotic time [69]
 Increased use of hallway beds [70]
Increased length of stay [41,46,71–73]
Risk of admission/readmission
 Increased risk of admission [63,74–78]
 Decreased risk of admission [79,80]
 No increased risk of readmission [74]
 Increased risk of 72-hr returns [81]
Patient elopement/left without being seen [59,63,66,82,83]
Reduced patient satisfaction [84–87]
Negative effects on staff [44,88,89]
Transfer boarding [90]
Increased costs [43,91,92]
Solution Reference
Decreasing input
 Increased primary care access [95,101,104]
 Paramedic treatment program to divert low-acuity patients [93]
 Triage to non-ED settings [96–100,102,103]
 Freestanding EDs (negative findings) [105]
 Paramedic-facing ED capacity dashboard [94]
Improving throughput
 Provider in triage [106–115]
 Nurse-initiated orders [116,117]
 Prehospital lab draws [118]
 Streaming, split flow, fast track, vertical care [119–122]
 Improved turnaround time for laboratory tests and radiology [123–125]
 Electronic health records/push-text notifications to improve patient flow [126,127]
 Improved staffing [128,129]
 Observation units [130,131]
 Process improvement strategies (e.g., Lean methodology) [132–136]
 Other
  Nurse flow coordinator [137]
  Critical care nurse deployment in ED [138]
  Earlier prediction of admission [139]
  Models to predict prolonged ED length of stay [140]
  Psychiatric patient care pathways [141]
  Bedside registration, physician triage and full capacity protocol [142]
Increased output
 Nurse handoff protocols [143]
 Inpatient hallway boarding [144,145]
 Active bed management [146–148]
 Admission to another hospital [149,150]
 Reducing bed downtime [151,152]
 Modified admission protocols [153,154]
 Early discharges [155–159]
 Discharge lounge [160]
 Maximum boarding rules [161–165]
 Hospitalist management of boarded patients [166]
 Boarding nurse [167]
AI and machine learning Reference
Predict flow [168–174]
Predict disposition [147,176–182]
Predict crowding and mortality [175]
Measuring crowding
Evaluation of existing metrics
 National Emergency Department Overcrowding Score (NEDOCS) [183–186,189,190]
 Emergency Department Work Index (EDWIN) [187,188,190]
 Length of stay [198]
 Other [191–193]
New methods of measurement
 No. of ambulances needed to relieve overcapacity [194]
 Staircase model [195]
 Wait hours [196]
 Lost bed capacity [199]
 Hierarchical polynomial regression prediction model [195]
 Data mining/ordinal logistic regression prediction model [200]
COVID-19 Reference
Decreased ED visits during early pandemic [201,202,205,209–215]
Decreased low acuity visits [203,204]
Decreased ED radiology volumes [208]
Increased telehealth visits [206,212]
Gradual increase in ED visits [207]
Increased LOS [203,204,217–221]
Decreased LOS in early pandemic [201,202]
High levels of boarding/crowding [213,216,220,222,223]
Crowding solution
 Drive-through clinic [224]
 Tents/alternative assessment area [225–228]
 Observation and admission protocols [229,230]
Table 1. Studies on causes of ED crowding

ED, emergency department.

Table 2. Studies on effects of ED crowding

ED, emergency department.

Table 3. Studies on solutions to ED crowding

ED, emergency department.

Table 4. Studies on artificial intelligence and measuring crowding
Table 5. Studies on COVID-19 and crowding

ED, emergency department; LOS, length of stay.

Table 1.

Table 2.

Table 3.

Table 4.

Table 5.

CEEM : Clinical and Experimental Emergency Medicine

ABOUT

BROWSE ARTICLES

FOR CONTRIBUTORS

© The Korean Society of Emergency Medicine.