Abstract
Introduction:
Emergency Department (ED) crowding is a global public health phenomenon affecting access and quality of care. In this study, we seek to conduct a systematic review concerning the challenges and outcomes of ED crowding.
Methods:
This systematic review utilized original research articles published from 1st January 2007, to 1st January 2019. Relevant articles from the PubMed (MEDLINE), EMBASE, and Google scholar databases were extracted using predesigned keywords. Following the PRISMA guidelines, two reviewers independently evaluated the quality of the studies using Critical Appraisal Skills Programme for cohort studies and qualitative studies, and Joanna Briggs Institute Meta-Analysis of Statistics Assessment and Review Instrument for studies.
Results:
Out of the total of 73 articles in the final record, we excluded 15 of them because of poor quality. This systematic review synthesized the reports of 58 original articles. The outcomes of multiple individual patients and healthcare-related challenges are comprehensively assessed.
Conclusions:
ED crowding affects individual patients, healthcare systems and communities at large. The negative influences of crowding on healthcare service delivery result in delayed service delivery, poor quality care, and inefficiency; all negatively affecting the emergency patients' healthcare outcomes, in turn.
Key Words: Crowding, outcome assessment, emergency service, hospital, systematic review
Introduction
The requirement of emergency healthcare service is an ongoing issue (1). The emergency department (ED) is expected not only to provide emergency care to patients but also to fulfill the needs of the providers, and the communities at large. Besides, the emergency department might be the only source of healthcare services to people especially in rural communities (1, 2).
Evidence shows an increase in emergency healthcare service utilization because of the increased rates of accidental injuries. However, the capacity of the emergency healthcare systems has not been well developed to respond to such high demand because creating a balance between emergency services and the required resources is challenging, especially in under-resourced countries (3-5). This condition leads to crowding of the EDs, which in turn impose public health challenges related to quality of healthcare and outcomes. Crowding is a situation when an identified need for emergency healthcare services exceeds the available resources to provide emergency care to patients within an appropriate time frame (1, 3, 6).
Crowding of the ED leads to adverse outcomes for the patients, providers, the healthcare system and the community. Delay in service provision to patients not only can compromise the quality of the emergency services but can also worsen their consequences. Crowding of the ED might also lead to the violations of the norms and the service provision standards, which in turn might result in patients leaving the facilities without getting the required services. Thus, this systematic review aims to describe the consequences of ED crowding for emergency patients, emergency care providers, and healthcare systems. The findings are anticipated to provide inputs to decision-makers for a better understanding of the effects of ED crowding and to contextualize practical solutions to improve the quality of medical emergency services.
Methods
Search Strategy
In this review, we adopted the definition for “crowding” from the American College of Emergency Physicians which states “Crowding occurs when the identified need for emergency services exceeds available resources for patient care in the emergency department, hospital, or both.” Then, we searched for articles related to crowding in EDs and its major outcomes published in English between January 1, 2007, and January 1, 2019, in PubMed (MEDLINE) and Embase electronic databases. We applied search terms based on common keywords in the literature concerning the consequences of emergency department crowding (Table 1). We used suitable combinations of "OR" and "AND" in all databases. Also, we searched Google scholar and Google to find relevant papers.
Table 1.
Keywords used for searching published articles in databases
|
Emergency department related concepts
|
Crowding related concepts
|
Outcome related concepts | |||
|---|---|---|---|---|---|
| Controlled phrases | Keywords | Controlled term/phrase | Keywords | Keywords | |
| PubMed | emergency medicine, pediatric emergency medicine, hospital emergency service, emergency medical services | emergency, emergency medicine, pediatric emergency medicine, emergency medical services, emergency room, hospital emergency services, emergency health services, emergency department, emergency ward, ER, ED |
crowding | crowding, overcrowded, crowded overcrowding, divert, diversion, congestion, surged, surging, capacity, crises, crisis, occupancy, hospital bed utilization, bed, utilization | Left without being seen (LWBS), Length of stay (LOS), delayed treatment, satisfaction, adverse events mortality, morbidity, error, hospitalization, quality, performance, readmissions, overutilization, efficiency, cost |
| EMBASE | emergency ward, emergency medicine | crowding, hospital bed utilization |
|||
Data collection and quality assessment
Two reviewers (HR.R. & A.AE.), independently screened the titles, abstracts and the methodological validity of the records using data extraction format before their inclusion in the final review. Discussions with the senior author (M.E) were used to resolve any disagreements among the reviewers during the assessment phase.
The inclusion criterion was: All studies evaluating the effects and consequences of ED crowding. However, a study was excluded if it only reported the outcomes of a case report or systematic review investigations.
A total of 73 articles were eligible for the review (Figure 1). We further assessed the records using the standardized Critical Appraisal Skills Programme (CASP) for the Cohort Studies, and Qualitative Studies. Besides, the Joanna Briggs Institute Meta-Analysis of Statistics Assessment and Review Instrument (JBI-MAStARI) for studies which employed other designs was used (7). We addressed PRISMA checklist requirements. Finally, after excluding 15 records with eligibility assessment scores below 0.33 points (<33%), the final review was done on 59 records. Throughout the processes, we attempted to maintain the original intentions of authors such as effects on patients, effects on healthcare delivery process, effects on quality care, and effects on efficiency in service delivery.
Figure 1.

Study selection flowchart
Ethics approval and consent: The research protocol was approved by the review committee of the Baqiyatallah University of Medical Sciences.
Results
Our search initially retrieved 158 studies. However, 132 papers were excluded by reviewing title and abstract and assessing full-text due to non-relevance. Then, 15 studies were excluded after final quality measurement and scoring for primary screening due to receiving below 0.33 points (<33%). Finally, 58 eligible peer-reviewed original articles were included in the final review (Figure 1 and Table 2).
Table 2.
Studies examining outcomes of emergency department (ED) crowding
|
Author
Year |
Study Design | Sample | Quality | Outcome variable |
|---|---|---|---|---|
| Cremonesi,2015 | survey | 54,254 patients | High | average per-patient cost; severity of health condition |
| Wang,2015 | prospective pilot | 3139 patients | High | average length of stay (LOS); patient Left without being seen (LWBS) |
| Shenoi,2009 | cross-sectional | 63,780 admissions | High | diversion |
| Fee,2007 | cross-sectional | 39,000 visits | High | ED volume at the time of arrival |
| Ben-Yakov,2015 | cohort | 9,759 patients | High | ED crowding; patient disposition (admission/discharge) |
| Cha,2011 | regression | 125,031 patients | High | mean patient volume over 8-hour; hospital mortality |
| Chang,2017 | longitudinal | 2,619 hospitals | High | LOS for admitted patients |
| Chiu,2017 | cohort | 70,222 visits | High | ED occupancy status; decision-making time; LOS; patient disposition |
| Depinet,2014 | cross-sectional | 9,976 patients | High | time to critically abnormal vital sign reassessment; patients waiting for admission, patients waiting in the lobby |
| Derose,2014 | cohort | 136,740 patients | High | inpatient mortality; ED LOS |
| Dubin,2013 | retrospective | 69 patients | High | emergency physician (EP) errors; number of patients boarding at the time of patient disposition |
| Epstein,2012 | cohort | 533 patients | High | occurrence of preventable medical errors; ED Occupancy |
| Fee,2011 | cross-sectional | 486 patients | High | arrival-to-antibiotic-administration times; number of ED patients requiring admission at the time of arrival |
| Gabayan,2015 | cohort | 625,096 visits | High | inpatient admission; death within 7 days |
| Gaieski,2017 | cohort | 2913 patients | High | ED occupancy; waiting patients; time to antibiotics; mortality |
| Hong,2013 | cross-sectional | 1296 patients | High | delayed resuscitation efforts; hospital mortality |
| Hsia,2013 | cross-sectional | 3,368,527 patients | High | ED crowding; bounceback admission |
| Hwang,2008 | cross-sectional | 1,068 patient | High | number of admitted patients; pain care measures |
| Jo,2012 | cross-sectional | 477 cases | High | 28-day mortality; timeliness of antibiotic therapy |
| Jo,2014 | cross-sectional | 54,410 patients | High | Emergency department occupancy ratio; ED LOS |
| Jo,2015 | cross-sectional | 1801 patients | High | ED occupancy ratio; inpatient mortality |
| Kennebeck,2011 | cohort | 190 patients | High | ED crowding; timeliness of antibiotic administration |
| Kulstad,2009 | cross-sectional | 17 patients | High | time to the first electrocardiogram (ECG); time to patient arrival in catheterization laboratory; occupancy rate |
| Kulstad,2010 | observational | NA | High | average daily occupancy rate and the emergency department work index (EDWIN) score; number of medication errors |
| Lee,2012 | prospective review | 11491 adults | High | ED crowding |
| McCarthy,2009 | cohort | 4 EDs | High | crowding at 30-minute intervals throughout each patient's ED stay; waiting room time; treatment time; and boarding time; occupancy rate |
| McCusker,2014 | cohort | 677,475 patients | High | 30-day outcomes: mortality, return ED visits, occupancy ratio separately for ED bed and waiting room patients |
| Medley,2010 | prospective review | 6,640 imaging studies | High | number of radiology studies ordered per patient; occupancy rate |
| Michelson,2012 | cohort | 198,778 visits | High | ED occupancy rate; return visits to the ED within 48 hours |
| Mills,2009 | cross-sectional | 976 patients | High | administration of and delays in time to analgesia |
| Mills,2010 | prospective cohort | 767 patients | High | ED crowding; time from triage to computed tomography (CT) read |
| Muller,2015 | cross-sectional | 40 ED bed | High | time to initial physician assessment; and daily nursing hours |
| Mullins,2014 | ecological | 4810 hospitals | High | LWBS; waiting times; boarding times; and LOS for admitted and discharged patients |
| O'Connor,2014 | pilot | 500 patients | High | triage time; date; treatment area; time to physician initial assessment; return ED visits within 14 days |
| Pines,2007 | cohort | 694 patients | High | delay (>4 hours from arrival) |
| Pines,2007 | cross-sectional | 741 patients | High | ED crowding |
| Pines,2008 | cohort | 1,469 patients | High | ED crowding (hallway placement, waiting times, and boarding times); patient satisfaction |
| Pines,2008 | cohort | 13,758 patients | High | Poor care; a delay (>1 hour) from triage to first pain medication; a delay (>1 hour) from room placement to first pain medication |
| Pines,2009 | cross-sectional | 4574 patients | High | inpatient adverse outcomes |
| Pines,2010 | retrospective cohort | 1,716 patients | High | ED crowding; ED occupancy, waiting patients, admitted patients, and patient-hours); overall LOS; time to treatment |
| Reznek,2017 | retrospective | 463 patients | High | Door-to-Imaging Time (DIT) within the 25-minute goal |
| Shenoi,2011 | cross-sectional | 161 patients | High | ED census; time to analgesic administration |
| Shin,2013 | retrospective | 770 patients | High | ED occupancy rate; compliance |
| Sikka,2010 | correlation | 334 patients | High | overall time to antibiotic administration |
| Sills,2011 | cross-sectional | 927 patients | High | ED occupancy; number waiting to see an attending-level physician |
| Sun,2013 | cohort | 995,379 ED visits, 187 hospitals | High | inpatient mortality; hospital length of stay; costs |
| Tekwani,2013 | cross-sectional | 1591 surveys | High | ED crowding; hospital diversion status; satisfaction |
| van der Linden,2014 | cohort | 169 patients | High | walkout from emergency |
| Van Der Linden,2016 | retrospective | 39110 patient | High | time to triage; time to treatment; age; 24-h mortality; 10-day mortality. |
| van der Linden,2016 | cross-sectional | 49539 patient | High | occupancy ratio; ED occupancy; LOS; time to triage |
| Verelst,2015 | cohort | 108,229 patients | High | in-hospital death; hospital; acquired morbidities; total hospital stay |
| Wang,2017 | cohort | 1345 participants | High | ED crowding; patient real-time satisfaction. |
| Ward,2015 | cross-sectional | 405 hospitals | High | admitted LOS; discharged LOS; boarding time; waiting time |
| Wiler,2013 | cross-sectional | 87,705 visits | High | patient LWBS |
| Wu,2015 | cohort | 852 patients | High | inpatient outcomes |
| Phillips, 2017 | cohort | 2,557 patients | High | ED LOS |
| Higginson, 2017 | cross-sectional | NA | High | bed occupancy |
| Geelhoed,2012 | quasi-experimental | NA | High | mortality rates; overcrowding rates |
The consequences of patient crowding in hospitals are multifaceted involving effects related to patient health outcomes, healthcare delivery system and the community at large. Table 3 presents a summary of the commonly reported outcomes of ED crowding. ED crowding leads to delayed care for emergency patients and risk of not being visited by clinical care providers in a timely manner (8-14). The patients may react to prolonged stay to get services and to the crowding by frequent walkouts (15). The worsening of their illness (16) could result in frequent re-admissions (17, 18), prolonged hospitalizations (16, 19, 20), and related costs (21). Dissatisfaction of emergency patients (22-25), medication errors and adverse events (26-29), and patient death (16, 17, 19-21, 30-36) were also common consequences.
Table 3.
Effects of crowding in emergency departments
| Effects on patients |
|---|
| Healthcare delivery system process |
|
| Effects on quality care |
| Effects on efficiency in service delivery |
The response to emergency and non-emergency patients influences the quality of services provided, patients’ outcomes and the healthcare system. Discharge of patients even with high-risk clinical features (17) and diverting the patients to other facilities (37) might have affected the health outcomes. These conditions not only decrease admission rates (38) and prolong the time to receive and transfer outpatients (39), but also compromise the patients' health outcomes and lead to high admission and re-admission rates (22, 36, 40) followed by a decrease in discharge rate of patients (17). In addition, the prolonged hospitalization of patients leads to overutilization of diagnostic and other laboratory facilities (40).
The crowding of the EDs negatively influences both the healthcare delivery process and the outcomes. The high workload (41) results in delayed service provision, delayed clinical decision making, and increased length of stay (LOS) of patients (20, 21, 31, 35, 36, 40-54). These situations negatively influence the quality of services and efficiency (8, 14, 16, 36, 55-58). A properly managed medical emergency contributes to the prevention of the event in communities. For example, a successfully treated patient with community-acquired pneumonia will be less likely to transmit the disease to other community members (13).
Discussion
This systematic review synthesized the outcomes related to ED crowding in hospitals. Crowding of ED can result in consequences for emergency patients’ health outcomes, the healthcare delivery system, and the community at large.
The high inflow of emergency patients to ED leads to crowding of the ED, which can in turn negatively affect the healthcare delivery process and outcomes. Delayed emergency healthcare service provision and patients leaving without being seen (LWBS) (8-14) have been commonly identified as consequences of crowding. This condition could inevitably lead to increased walkout of patients due to the perceived high length of stay. As a result, the emergency patients' morbidity worsened, and subsequent mortalities increased (16, 17, 19-21, 30-36). The frequent readmissions and prolonged hospitalizations of emergency patients not only increase ED crowding, but also negatively affect the cost of treatment (21) and patient satisfaction (22-25). Hoot and Aronsky in their systematic review identified a direct relationship between ED crowding and emergency patient death, reduced quality of care, and increased treatment costs (59). Delayed patient assessment and care provision could result in increased mortality, medical error, and decreased patient satisfaction (60).
The increase in the workload of emergency healthcare staff due to the high patient flow results in delayed clinical decision making and emergency healthcare service provision and increased ED LOS of patients (20, 21, 31, 35, 36, 40-42, 44-54, 61, 62). This condition again leads to discharge of patients even with high-risk clinical features (17) and to the diversion of emergency patients to other health facilities (37). ED crowding can also be associated with decreased admission rates (38), delayed emergency healthcare provision, and delay in transfer of emergency patients to inpatient wards (39). In contrast, the high admission and re-admission rates of emergency patients (22, 36, 40) followed by a decreased patient discharge rates (17) and prolonged hospitalization can lead to overutilization of diagnostic imaging and laboratory tests (40). Thus, several emergency healthcare-related consequences seem to be overlooked in the Morley et al. synthesis as they mainly focused on inpatient LOS and ED LOS (60). Our review broadly highlighted the healthcare delivery system-related consequences of ED crowding under the categories of healthcare delivery process, quality care, and efficiency.
ED crowding can negatively affect the quality of emergency healthcare. The higher the number of emergency patients, the longer the time it takes to investigate their conditions and to take supportive actions (49). These conditions can lead to reduced emergency healthcare quality and poor healthcare outcomes, which may result in an increase in bed occupancy rate (63). Besides, these conditions may negatively affect performances and result in inefficiency due to an increase in treatment costs (8, 14, 16, 36, 55, 56, 58). Similarly, others also identified the negative influence of ED crowding on the cost of treatment (59) and non-adherence to best practice guidelines for emergency service provision (60).
Strengths and Limitation
This systematic review synthesized original articles related to outcomes of the emergency department crowding in hospitals globally. Several studies identified complex issues related to emergency department crowding. Our review identified several crowding-related challenges and consequences including patient and staff reactions. The relevant original articles on ED crowding were accessed from the PubMed, Embase, and google scholar databases using comprehensive search keywords. The qualities of the records have been assessed using relevant checklists and those with low quality have been excluded. Our review also adds to the comprehensiveness of the view about the issues. The more explicit schematization of our synthesis compared to other existing reviews can facilitate a better understanding of the complex phenomenon. However, this review has certain limitations. It used study reports published only in English retrieved from the two mentioned sources. Moreover, the reviewed studies did not have a shared definition of crowding.
Conclusion:
ED crowding affects individual patients, healthcare systems and communities at large. The negative influences of crowding on healthcare service delivery result in delayed service delivery, poor quality care, and inefficiency; all negatively affecting the emergency patients' healthcare outcomes, in turn. This review highlights the importance of response to emergencies and emergency-related crowding and preventing the consequences to better address the healthcare needs of emergency patients and increase the effectiveness of healthcare service delivery centers.
List of abbreviations
ED: Emergency Department
MeSH: Medical Subject Headings
CASP: Critical Appraisal Skills Programme
JBI-MAStARI: Joanna Briggs Institute Meta-Analysis of Statistics Assessment and Review Instrument
LWBS: Left Without Being Seen
LOS: Length of Stay
Availability of data and supporting materials:
The datasets used and analyzed during the current study are available from the corresponding author on request.
Conflicts of interests:
No conflicts of interest
Funding:
Not applicable
Authors' contributions:
All the authors have contributed to development of the concept and production of the final manuscript.
Acknowledgments:
Not applicable
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