Abstract
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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.
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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).
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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.
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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.
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Keywords: Emergency department; Emergency department crowding; Emergency department length of stay; COVID-19; Length of stay
Capsule Summary
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.
INTRODUCTION
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.
METHODS
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].
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.
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).
RESULTS
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].
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].
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].
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].
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].
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].
DISCUSSION
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].
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.
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.
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].
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.
NOTES
-
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
Fig. 1.PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) flow diagram.
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] |
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] |
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] |
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] |
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