Skip to main content
Download PDF

Patient and hospital characteristics predict prolonged emergency department length of stay and in-hospital mortality: a nationwide analysis in Korea

BMC Emergency Medicine volume 22, Article number: 183 (2022) Cite this article

Abstract

Background

Prolonged emergency department length of stay (EDLOS) in critically ill patients leads to increased mortality. This nationwide study investigated patient and hospital characteristics associated with prolonged EDLOS and in-hospital mortality in adult patients admitted from the emergency department (ED) to the intensive care unit (ICU).

Methods

We conducted a retrospective cohort study using data from the National Emergency Department Information System. Prolonged EDLOS was defined as an EDLOS of ≥ 6 h. We constructed multivariate logistic regression models of patient and hospital variables as predictors of prolonged EDLOS and in-hospital mortality.

Results

Between 2016 and 2019, 657,622 adult patients were admitted to the ICU from the ED, representing 2.4% of all ED presentations. The median EDLOS of the overall study population was 3.3 h (interquartile range, 1.9–6.1 h) and 25.3% of patients had a prolonged EDLOS. Patient characteristics associated with prolonged EDLOS included night-time ED presentation and Charlson comorbidity index (CCI) score of 1 or higher. Hospital characteristics associated with prolonged EDLOS included a greater number of staffed beds and a higher ED level. Prolonged EDLOS was associated with in-hospital mortality after adjustment for selected confounders (adjusted odds ratio: 1.18, 95% confidence interval: 1.16–1.20). Patient characteristics associated with in-hospital mortality included age ≥ 65 years, transferred-in, artificially ventilated in the ED, assignment of initial triage to more urgency, and CCI score of 1 or higher. Hospital characteristics associated with in-hospital mortality included a lesser number of staffed beds and a lower ED level.

Conclusions

In this nationwide study, 25.3% of adult patients admitted to the ICU from the ED had a prolonged EDLOS, which in turn was significantly associated with an increased in-hospital mortality risk. Hospital characteristics, including the number of staffed beds and the ED level, were associated with prolonged EDLOS and in-hospital mortality.

Peer Review reports

Background

Over the past few decades, critical care has become a significant and growing part of the treatment provided at emergency departments (EDs) [1, 2]. Studies conducted in the United States (US) reported that intensive care unit (ICU) admissions from the ED have increased at a greater rate than overall ED presentations, and that length of stay in the ED has markedly increased [3, 4]. The increasing provision of critical care in EDs contributes to hospital overcrowding and strain on emergency care systems, which is an important public health concern worldwide [5,6,7].

Critical care is extremely resource-intensive, and often requires extensive diagnostic testing, continuous monitoring, and invasive techniques [8, 9]. However, EDs are essentially designed to provide rapid triage, stabilization, and initial treatment for numerous patients with various conditions and acuity. Therefore, EDs may not be sufficiently equipped or staffed to provide the complex and continuous care needed for critically ill patients [2]. In addition, physicians and nurses in overcrowded EDs may not be able to provide timely care to critically ill patients [10, 11]. Therefore, there is a potential advantage in transferring critically ill patients immediately after stabilization from the ED to the ICU, which is a highly specialized and skilled setting for critical care [12].

ED length of stay (EDLOS), defined as the time interval from when a patient arrives at the ED until the patient leaves the ED, is a widely adopted performance indicator in studies evaluating ED processes [13, 14]. Prolonged EDLOS is associated with inefficient ED organization, untimely care, and poor adherence to clinical guidelines [15,16,17,18,19,20]. EDLOS has also been used as a proxy for ED overcrowding and boarding, which are potential threats to patient safety [21, 22]. Prolonged EDLOS in critically ill patients is associated with adverse outcomes, including increased mortality risk [23,24,25,26,27,28].

Previous studies have demonstrated the contribution of patient characteristics as predictors of prolonged EDLOS and the resulting outcome. However, Chalfin et al. suggested that certain institutional and structural factors may have contributed to these differences [25]. In fact, a study using health data from Ontario, Canada, indicated that the demand and capacity of ED and ICU were important determinants of prolonged EDLOS in critically ill patient [29]. However, since most other studies examining both patient and hospital factors have been limited to a single or selected hospitals, these results have limited generality [15, 18, 30,31,32].

Therefore, this nationwide study aimed to provide insight into the patient and hospital characteristics associated with prolonged EDLOS in critically ill patients directly admitted from the ED to the ICU. The secondary objective was to explore the association between prolonged EDLOS and patient outcomes, as well as related patient and hospital characteristics.

Methods

Study design

This study used data from a health database in Korea, the National Emergency Department Information System (NEDIS), between 2017 and 2019. The NEDIS is a nationwide ED-based database for evaluating the emergency care system in Korea, established in accordance with Article 15 of the Emergency Medical Service Act. To achieve this goal, the NEDIS collects ED visit-level data, including demographic, clinical, and administrative information. Each visit-level datum also has the corresponding hospital identifier and hospital characteristics, such as total staffed beds, level of ED, and region. All patient-related information was anonymized and electronically submitted to the central processing facility, which was examined both manually and using computerized algorithms to detect data inconsistencies. Between 2017 and 2019, the participation rate of nationwide EDs in the NEDIS was 99.3% (413/416) in 2017, 99.5% (399/401) in 2018, and 99.8% (401/402) in 2019. The design and variables of the NEDIS database have been described elsewhere [33,34,35].

From the NEDIS database, we identified all patients admitted to the ICU directly from the ED between 1 January 2017 and 31 December 2019 based on the date of presentation to the ED. In Korea, there are several types of ICUs that provide intensive and specialized medical and nursing care to critically ill patients with various conditions [36]. However, in this study, the ICU was defined as any type of licensed ICU within the hospital. These operational definitions for critically ill patient and ICU were adopted from previous studies [3, 6, 29]. Patients with missing age or sex information, those < 18 years old, and those with missing days and times of ED presentation and departures were excluded.

Outcomes and variables

The primary outcome of this study was prolonged EDLOS, which was defined as an EDLOS of 6 h or more. This decision was based on existing evidence suggesting that an EDLOS of 6 h or more is associated with increased mortality risk and influences the quality of care in critically ill patients in the ED [25, 37, 38]. The secondary outcome was in-hospital mortality.

We identified patient and hospital variables a priori as potential predictors of prolonged EDLOS and in-hospital mortality risk in critically ill patients. Potential predictors were selected based on a review of the academic literature and data available in the NEDIS database [23,24,25,26,27,28, 39,40,41,42].

Patient variables included age, sex, insurance type, injury code, emergency ambulance attendance, transferred-in, date and time of ED presentation, initial triage score, artificial ventilation in the ED, diagnosis codes during hospitalization, Charlson comorbidity index (CCI), and discharge status. The initial triage was scored according to the Korean Triage and Acuity Scale (KTAS), which prioritizes patients according to the five ordinal scales reflecting clinical severity and acuity as follows: resuscitation: 1, emergent: 2, urgent: 3, less urgent: 4, non-urgent: 5 [43]. In Korea, the triage process begins with the patient’s ED presentation and can only be performed by qualified physicians, nurses, or paramedics [44]. The date and time of ED presentation were categorized according to the year, season (spring: March–May, summer: June–August, fall: September–November, winter: December–February) and ED shift time (day: 07:00–14:59, evening: 15:00–22:59, night: 23:00–06:59). Diagnostic codes used during hospitalization were identified based on codes defined in the International Classification of Diseases, Tenth Revision (ICD-10). The CCI score was calculated based on diagnostic codes used during hospitalization by applying the methods proposed in previous studies, which showed good-to-excellent discriminant power in predicting in-hospital mortality risk [45, 46].

Hospital variables were hospital staffed beds (≥ 1,000, 800–999, 600–799, 300–599, and < 300), level of ED (levels 1, 2, and 3), and location (metropolitan city versus provincial area). In Korea, EDs are classified into three levels in the following order according to their capabilities and functions: level 1, regional emergency centers; level 2, local emergency centers; and level 3, local emergency institutes [47]. Level 1 is the highest, with more medical staff, wider care space, and more equipment according to the standards specified by the Ministry of Health and Welfare.

Statistical analysis

We calculated the proportion of the study population with overall ED presentations and overall adult ED presentations, as well as the annual incidence/100,000 adult ED presentations.

Descriptive analyses were performed to compare the patient and hospital characteristics between critically ill patients with an EDLOS of ≥ 6 h and critically ill patients with an EDLOS of < 6 h. Categorical variables were reported as frequencies and proportions and were compared between patient groups using Pearson’s Chi-squared test. Continuous variables were described as the median and interquartile range (IQR) and were tested using the Wilcoxon rank-sum test. The median EDLOS with IQR and percentages of in-hospital mortality for each of the most common primary diagnoses were calculated.

We performed multivariate logistic regression analyses to model the effects of patient and hospital variables as predictors of prolonged EDLOS (both 6 h and 12 h) and in-hospital mortality. Since level 3 EDs may differ from level 1 or 2 EDs in ED settings, patient populations, and disease spectra [48], we conducted sensitivity analyses excluding patients from level 3 EDs using the same model. To evaluate the potential differential associations of hospital characteristics with prolonged EDLOS vs. in-hospital mortality, we performed stratified analyses with the highest hospital staffed bed category (1,000 or more) or type of ED (level 1) as the reference in the same logistic regression model.

All analyses were performed using SAS version 9.4 (SAS Inc., Cary, NC, USA) and R version 4.1.3 (R Development Core Team, https://cran.r-project.org/). All tests were two-tailed, and a p-value < 0.05 was considered statistically significant.

Results

Characteristics of critically ill patients directly admitted to the intensive care unit from the emergency department

Over the 3-year study period, 657,622 adult patients directly admitted to the ICU through the ED were identified in the NEDIS database, representing 3.0% of all adults presenting to the EDs and 2.4% of all ED presentations. The crude incidence rate/100,000 adult ED presentations was 3,026 in 2017, 2,955 in 2018, and 3,048 in 2019. Of the overall study population, 166,528 (25.3%) were transferred to the ICU after a stay of ≥ 6 h in the ED, and 491,094 (74.7%) were transferred from the ED to the ICU for < 6 h in the ED. The median EDLOS of the overall study population was 3.3 h (IQR, 1.9–6.1 h) (Fig. 1).

Fig. 1
figure 1

Distribution of emergency department length of stay in critically ill patients directly admitted to the intensive care unit from the emergency department. Abbreviations: EDLOS, emergency department length of stay; IQR, interquartile range

Full size image

Compared to critically ill patients with an EDLOS of < 6 h, those with an EDLOS of 6 h or more had a higher proportion of night-time presentations and had higher CCI scores (Table 1). Regarding hospital variables, an EDLOS of ≥ 6 h correlated with a greater number of staffed beds and a higher proportion of presentations to level 1 EDs than did EDLOS < 6 h. The characteristics of the study population, stratified according to staffed-bed category and type of ED, are shown in Additional file 1: Tables S1 and S2. In brief, the higher the staffed bed category and ED level, the greater the number of critically ill patients per institution, and the longer the median EDLOS.

Table 1 Characteristics of critically ill patients directly admitted to the ICU from the ED according to EDLOS
Full size table

The most common primary diagnosis in the study population was acute myocardial infarction, accounting for 8.6% of adult patients directly admitted to the ICU from the ED, with a median EDLOS of 2.1 h (IQR, 0.9–4.9 h), and an in-hospital mortality rate of 7.6%. The next most common primary diagnoses were intracranial injury (7.2%), cerebral infarction (7.0%), pneumonia (5.4%), and intra-cerebral hemorrhage (4.6%) (Table 2).

Table 2 The ten most common primary diagnoses for critically ill patients directly admitted to the ICU from the ED
Full size table

Variables associated with prolonged emergency department length of stay

The results of multivariate logistic regression analyses with prolonged EDLOS as the dependent outcome are shown in Table 3. For the patient variables of interest, night-time ED presentation was a significant predictor of prolonged EDLOS (adjusted odds ratio [aOR], 1.49; 95% confidence interval (CI), 1.46–1.51). KTAS scores of 4 (aOR, 1.48; 95% CI, 1.44–1.53) and 5 (aOR, 1.39; 95% CI, 1.30–1.48), indicating lower acuity, were significant predictors of prolonged EDLOS compared with a KTAS score 1 of the highest acuity. In addition, a CCI score of 1 or higher significantly predicted an EDLOS of ≥ 6 h, with patients with a CCI score of zero as the reference. For the hospital variables of interest, hospital staffed bed category was a significant predictor of prolonged EDLOS. Critically ill patients who admitted to hospitals with < 300 staffed beds were less likely to have prolonged EDLOS than those who admitted to hospitals with ≥ 1,000 staffed beds (aOR, 0.12; 95% CI, 0.11–0.12). These findings were consistent with those at the ED level. In the sensitivity analysis, the magnitude and direction of the aOR of patient and hospital variables did not change after excluding patients presented in the level 3 ED (Additional file 1: Table S3).

Table 3 Multivariate logistic regression analyses for prolonged EDLOS
Full size table

Variables associated with in-hospital mortality

The results of the multivariate logistic regression analyses with in-hospital mortality as the dependent outcome are shown in Table 4. After adjusting for patient and hospital variables, prolonged EDLOS was associated with an increased in-hospital mortality risk (aOR, 1.18; 95% CI, 1.16–1.20). artificial ventilation in the ED was a significant risk factor for in-hospital mortality (aOR, 2.73; 95% CI, 2.66–2.80). Age ≥ 65 y gave a nearly two-fold increase in risk for in-hospital mortality (aOR, 1.98; 95% CI, 1.95–2.02), and transfer from another hospital gave a 65% increased risk (aOR, 1.65; 95% CI, 1.61–1.68). In addition, a CCI score of 1 or higher predicted greater in-hospital mortality risk, while a KTAS score of 2 or higher predicted lower in-hospital mortality risk. With regard to hospital variables, hospital staffed bed category and ED type were significantly associated with in-hospital mortality, but contrary to the results of the multivariate analyses for prolonged EDLOS, aOR increased as the number of hospital beds and ED level increased. The risk of in-hospital mortality for critically ill patients admitted to hospitals with < 300 staffed beds was 23% higher than those admitted to hospitals with ≥ 1,000 staffed beds (aOR, 1.23; 95% CI, 1.18–1.27). These findings were consistent with those at the ED level. In the sensitivity analysis, the magnitude and direction of the aOR of patient and hospital variables did not change after excluding patients presented in the level 3 ED (Additional file 1: Table S4).

Table 4 Multivariate logistic regression analysis for in-hospital mortality
Full size table

In the stratified analysis, critically ill patients with an EDLOS < 6 h had a higher risk of in-hospital mortality as the staffed beds in the admitted hospital decreased. In contrast, critically ill patients with an EDLOS of ≥ 6 h showed no significant difference in mortality risk depending on the hospital staffed bed categories (Fig. 2-A). These findings were consistent at the ED level (Fig. 2-B).

Fig. 2
figure 2

Stratified analysis of in-hospital mortality according to emergency department length of stay and hospital characteristics. Each blue square and line represent the adjusted odds ratio and 95% confidence interval, respectively. (A) hospital staffed beds; (B) level of ED; EDLOS, emergency department length of stay; aOR, adjusted odds ratio; CI, confidence interval; ED, emergency department

Full size image

Discussion

We conducted a nationwide study, and found a median EDLOS of 3.3 h in critically ill adults admitted directly to the ICU from the ED in Korea. However, 25.3% of these ICU admissions did not meet the criterion of an EDLOS < 6 h, which is an internationally recognised performance indicator used to evaluate the quality of emergency care [25, 37, 38, 49]. Comparing the data reported from other countries, the median EDLOS for critically ill adult patients in Korea was longer than that for Australia (2.5 h) [26], and shorter than that for the US (4–5 h) [3, 6], and Canada (7 h) [29].

The most common primary diagnoses for ED presentation leading to ICU admission were potentially serious cardiovascular, cerebrovascular, and respiratory diseases, and head trauma. However, the top ten primary diagnoses accounted for only 43.3% of all ICU admissions to the ED. Similar to other countries, our finding demonstrates that critically ill patients receiving care in a Korean ED setting represent a highly heterogeneous population [50,51,52], highlighting the challenges of providing critical care in such an environment [53].

In our study, prolonged EDLOS was significantly associated with night-time presentations, suggesting decreased access to specialist consultations and diagnostic or treatment modalities compared to regular working hours [29, 54]. Patients assigned to the lower acuity scores in the initial triage were more likely to have prolonged EDLOS than those assigned to higher acuity scores. Possible explanations for prolonged EDLOS in patients with lower acuity scores include diagnostic uncertainty requiring additional diagnostic testing and specialist consultations [41], lowering the priority of patients assigned to higher acuity scores [55], and deteriorating clinical condition while the patient is waiting in the ED. Age ≥ 65 y was also associated with prolonged EDLOS. Older patients may have an increased risk of under-triage due to the presentation of non-specific symptoms or vital signs compared with younger patients, which could lead to prolonged EDLOS [56]. In terms of hospital variables, greater numbers of staffed beds and higher ED levels generally represent more in-hospital resources that could increase ED throughput and output. However, the logistic regression model showed an inverse relationship with prolonged EDLOS. According to the input-throughput-output conceptual model, this means that larger hospitals and higher levels of EDs have more “inputs” than smaller hospitals [57]. Indeed, there were more critically ill patients in larger hospitals and in hospitals with higher ED levels, and these patients also had a significantly longer median EDLOS [58, 59].

Here, as in previous studies, prolonged EDLOS was significantly associated with in-hospital mortality [23,24,25,26,27,28]. In terms of patient variables of interest, logistic regression analysis identified age ≥ 65 y, arrival via emergency ambulance, transfer from other hospitals, night-time presentation, higher initial triage score, artificial ventilation in the ED, and CCI score of 1 or higher as independent risk factors for in-hospital mortality, which is consistent with findings reported in previous studies. Interestingly, even after adjusting for EDLOS and patient variables, the difference in mortality risk between the ED levels and hospital staffed bed categories persisted. As mentioned earlier, hospitals with higher ED levels and more staffed beds cared for more critically ill patients. Increasing evidence suggests that hospitals with higher patient volumes achieve better patient outcomes across various medical conditions and surgical procedures [60,61,62,63]. Our findings may reflect this “volume-outcome relationship”. Previous studies have suggested several causal pathways whereby hospital patient volume can affect mortality. First, larger hospitals have more available resources, such as consultants, advanced diagnostic capabilities, and emergency procedural intervention, in order to provide resource-intensive care for specific conditions such as myocardial infarction or sepsis [62]. Second, larger hospitals which deal with higher patient volumes may have greater exposure to time-sensitive conditions, which can lead to the development of institutional policies and treatment processes that improve the quality of care for critically ill patients [64]. However, Nguyen et al. suggested that volume-outcome relationships can be partially mediated by managerial and organizational factors [65]. This view emphasises the importance of introducing mitigation strategies regardless of hospital volume. Recent studies on mitigation strategies have shown that suitable interventions, such as ED-based electronic ICU monitoring systems, streamlined admissions, and ED-based ICUs, can reduce EDLOS or improve clinical outcomes in critically ill patients [53, 66,67,68,69,70].

Our study has several limitations. First, the operational definition of critically ill patients was based solely on ICU admission without objective physiological parameters. The criteria for ICU admission may vary significantly among hospitals. Alternative methodologies for identifying critically ill patients, such as the acute physiology and chronic health evaluation or the simplified acute physiology score, require data not collected in the NEDIS. However, the operational definition used in this study provides a pragmatic representation of ED use in critically ill patients at the nationwide level [3]. Second, since there is no standard risk adjustment method for critically ill patients in the ED setting [53], we attempted to include as many variables as possible in the regression model, but there may be other unaccounted variables contributing to the observed results [71]. In particular, as mentioned above, objective physiological parameters were not included in the analysis, which limits the results related to in-hospital mortality. Also, information on ED overcrowding, staffing, teaching hospital status, ICU capacity, and organizational factors was not reflected in the analysis because these variables fluctuated over time or were not available from the NEDIS. Future work is needed to assess these factors for association with EDLOS and in-hospital mortality. Third, this study was based solely on data from Korea. Regional differences in practices, institutions, and systems can make knowledge transfer difficult; therefore, further studies from other regions and countries are required. Finally, the statistically significant differences observed in this study may be partly due to the large study population size and should be interpreted with caution.

Conclusions

In Korea, ED is a significant component of the critical care delivery system, from where more than 200,000 adult critically-ill patients are admitted to ICUs annually. Approximately a quarter of these patients stayed in the ED for ≥ 6 h, and prolonged EDLOS was associated with in-hospital mortality. Hospital characteristics were also associated with prolonged EDLOS and in-hospital mortality, after adjusting for patient characteristics. These results highlight the need to introduce mitigation strategies that target potentially modifiable factors, such as the hospital's organizational and managerial elements.

Availability of data and materials

The sharing of anonymised data from this study was restricted due to ethical and legal constraints. Data contain sensitive personal health information, which is protected under the Personal Information Protection Act in Korea, thus making all data requests subject to institutional review board (IRB) approval. According to the National Medical Center (NMC) IRB, the data that support the findings of this study are restricted to transmission to those in the primary investigative team. Data sharing with investigators outside the team requires IRB approval. All requests for anonymised data will be reviewed by the research team and submitted to the NMC IRB for approval.

Abbreviations

ED:

Emergency department

ICU:

Intensive care unit

US:

United States

EDLOS:

Emergency department length of stay

NEDIS:

National emergency department information system

CCI:

Charlson comorbidity index

KTAS:

Korean triage and acuity scale

ICD-10:

International classification of disease 10th edition

IQR:

Interquartile range

aOR:

Adjusted odds ratio

CI:

Confidence interval

References

  1. Cowan RM, Trzeciak S. Clinical review: emergency department overcrowding and the potential impact on the critically ill. Crit Care. 2005;9:291–5.

    Article  PubMed  Google Scholar 

  2. Ghosh R, Pepe P. The critical care cascade: a systems approach. Curr Opin Crit Care. 2009;15:279–83.

    Article  PubMed  Google Scholar 

  3. Herring AA, Ginde AA, Fahimi J, Alter HJ, Maselli JH, Espinola JA, et al. Increasing critical care admissions from U.S. emergency departments, 2001–2009. Crit Care Med. 2013;41:1197–204. https://doi.org/10.1097/CCM.0b013e31827c086f.

  4. Lambe S, Washington DL, Fink A, Herbst K, Liu H, Fosse JS, et al. Trends in the use and capacity of California’s emergency departments, 1990–1999. Ann Emerg Med. 2002;39:389–96.

    Article  PubMed  Google Scholar 

  5. Ansah JP, Ahmad S, Lee LH, Shen Y, Ong MEH, Matchar DB, et al. Modeling Emergency Department crowding: restoring the balance between demand for and supply of emergency medicine. PLoS ONE. 2021;16:e0244097.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  6. Mullins PM, Goyal M, Pines JM. National growth in intensive care unit admissions from emergency departments in the United States from 2002 to 2009. Acad Emerg Med. 2013;20:479–86.

    Article  PubMed  Google Scholar 

  7. Pan C, Pang JJ, Cheng K, Xu F, Chen YG. Trends and challenges of emergency and acute care in Chinese mainland: 2005–2017. World J Emerg Med. 2021;12:5–11.

    Article  PubMed  PubMed Central  Google Scholar 

  8. Cecconi M, De Backer D, Antonelli M, Beale R, Bakker J, Hofer C, et al. Consensus on circulatory shock and hemodynamic monitoring Task force of the European Society of Intensive Care Medicine. Intensive Care Med. 2014;40:1795–815.

    Article  PubMed  PubMed Central  Google Scholar 

  9. Saugel B, Vincent JL. Cardiac output monitoring: how to choose the optimal method for the individual patient. Curr Opin Crit Care. 2018;24:165–72.

    Article  PubMed  Google Scholar 

  10. Jaffe TA, Goldstein JN, Yun BJ, Etherton M, Leslie-Mazwi T, Schwamm LH, et al. Impact of emergency department crowding on delays in acute stroke care. West J Emerg Med. 2020;21:892–9.

    Article  PubMed  PubMed Central  Google Scholar 

  11. Peltan ID, Bledsoe JR, Oniki TA, Sorensen J, Jephson AR, Allen TL, et al. Emergency department crowding is associated with delayed antibiotics for sepsis. Ann Emerg Med. 2019;73:345–55.

    Article  PubMed  Google Scholar 

  12. Weled BJ, Adzhigirey LA, Hodgman TM, Brilli RJ, Spevetz A, Kline AM, et al. Critical care delivery: the importance of process of care and ICU structure to improved outcomes: an update from the American College of Critical Care Medicine Task Force on Models of Critical Care. Crit Care Med. 2015;43:1520–5.

    Article  PubMed  Google Scholar 

  13. Andersson J, Nordgren L, Cheng I, Nilsson U, Kurland L. Long emergency department length of stay: a concept analysis. Int Emerg Nurs. 2020;53:100930.

    Article  PubMed  Google Scholar 

  14. Wiler JL, Welch S, Pines J, Schuur J, Jouriles N, Stone-Griffith S. Emergency department performance measures updates: proceedings of the 2014 emergency department benchmarking alliance consensus summit. Acad Emerg Med. 2015;22:542–53.

    Article  PubMed  Google Scholar 

  15. Diercks DB, Roe MT, Chen AY, Peacock WF, Kirk JD, Pollack CV, et al. Prolonged emergency department stays of non-ST-segment-elevation myocardial infarction patients are associated with worse adherence to the American College of Cardiology/American Heart Association guidelines for management and increased adverse events. Ann Emerg Med. 2007;50:489–96.

    Article  PubMed  Google Scholar 

  16. Nelson KA, Boslaugh SE, Hodge D. Risk factors for extremely long length-of-stay among pediatric emergency patients. Pediatr Emerg Care. 2009;25:835–40.

    Article  PubMed  Google Scholar 

  17. Brouns SH, Stassen PM, Lambooij SL, Dieleman J, Vanderfeesten IT, Haak HR. Organisational factors induce prolonged emergency department length of stay in elderly patients–a retrospective cohort study. PLoS ONE. 2015;10:e0135066.

    Article  PubMed  PubMed Central  Google Scholar 

  18. Perdahl T, Axelsson S, Svensson P, Djärv T. Patient and organizational characteristics predict a long length of stay in the emergency department - a Swedish cohort study. Eur J Emerg Med. 2017;24:284–9.

    Article  PubMed  Google Scholar 

  19. Salehi L, Phalpher P, Valani R, Meaney C, Amin Q, Ferrari K, et al. Emergency department boarding: a descriptive analysis and measurement of impact on outcomes. CJEM. 2018;20:929–37.

    Article  PubMed  Google Scholar 

  20. Sweeny A, Keijzers G, O’Dwyer J, Arendts G, Crilly J. Predictors of a long length of stay in the emergency department for older people. Intern Med J. 2020;50:572–81.

    Article  PubMed  Google Scholar 

  21. Mohr NM, Wessman BT, Bassin B, Elie-Turenne MC, Ellender T, Emlet LL, et al. Boarding of critically ill patients in the emergency department. Crit Care Med. 2020;48:1180–7.

    Article  PubMed  PubMed Central  Google Scholar 

  22. 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.

    Article  PubMed  PubMed Central  Google Scholar 

  23. Al-Qahtani S, Alsultan A, Haddad S, Alsaawi A, Alshehri M, Alsolamy S, et al. The association of duration of boarding in the emergency room and the outcome of patients admitted to the intensive care unit. BMC Emerg Med. 2017;17:34.

    Article  PubMed  PubMed Central  Google Scholar 

  24. Cha WC, Cho JS, Shin SD, Lee EJ, Ro YS. The impact of prolonged boarding of successfully resuscitated out-of-hospital cardiac arrest patients on survival-to-discharge rates. Resuscitation. 2015;90:25–9.

    Article  PubMed  Google Scholar 

  25. Chalfin DB, Trzeciak S, Likourezos A, Baumann BM, Dellinger RP. Impact of delayed transfer of critically ill patients from the emergency department to the intensive care unit. Crit Care Med. 2007;35:1477–83.

    Article  PubMed  Google Scholar 

  26. Flabouris A, Jeyadoss J, Field J, Soulsby T. Association between emergency department length of stay and outcome of patients admitted either to a ward, intensive care or high dependency unit. Emerg Med Australas. 2013;25:46–54.

    Article  PubMed  Google Scholar 

  27. Groenland CNL, Termorshuizen F, Rietdijk WJR, van den Brule J, Dongelmans DA, de Jonge E, et al. Emergency department to ICU time is associated with hospital mortality: a registry analysis of 14,788 patients from six university hospitals in The Netherlands. Crit Care Med. 2019;47:1564–71.

    Article  PubMed  PubMed Central  Google Scholar 

  28. Guttmann A, Schull MJ, Vermeulen MJ, Stukel TA. Association between waiting times and short term mortality and hospital admission after departure from emergency department: population based cohort study from Ontario. Canada BMJ. 2011;342:d2983.

    Article  PubMed  Google Scholar 

  29. Rose L, Scales DC, Atzema C, Burns KE, Gray S, Doing C, et al. Emergency department length of stay for critical care admissions A population-based study. Ann Am Thorac Soc. 2016;13:1324–32.

    Article  PubMed  Google Scholar 

  30. Driesen BEJM, van Riet BHG, Verkerk L, Bonjer HJ, Merten H, Nanayakkara PWB. 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.

    Article  PubMed  PubMed Central  Google Scholar 

  31. 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.

    Article  PubMed  PubMed Central  Google Scholar 

  32. Aitavaara-Anttila M, Liisanantti JH, Raatiniemi L, Ohtonen P, Ala-Kokko T. Factors related to delayed intensive care unit admission from emergency department-A retrospective cohort study. Acta Anaesthesiol Scand. 2019;63:939–46.

    PubMed  Google Scholar 

  33. Lee SY, Ro YS, Shin SD, Moon S. Epidemiologic trends in cancer-related emergency department utilization in Korea from 2015 to 2019. Sci Rep. 2021;11:21981.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  34. Sung HK, Paik JH, Lee YJ, Kang S. Impact of the COVID-19 outbreak on emergency care utilization in patients with acute myocardial infarction: a nationwide population-based study. J Korean Med Sci. 2021;36:e111.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  35. Min HS, Chang HJ, Sung HK. Emergency department utilization of adult cancer patient in Korea: a nationwide population-based study, 2017–2019. Cancer Res Treat. 2022;54:680–9.

    Article  PubMed  Google Scholar 

  36. Cho NR, Jung WS, Park HY, Kang JM, Ko DS, Choi ST. Discrepancy between the demand and supply of intensive care unit beds in South Korea from 2011 to 2019: a cross-sectional analysis. Yonsei Med J. 2021;62:1098–106.

    Article  PubMed  PubMed Central  Google Scholar 

  37. Horwitz LI, Green J, Bradley EH. US emergency department performance on wait time and length of visit. Ann Emerg Med. 2010;55:133–41.

    Article  PubMed  Google Scholar 

  38. Zhang Z, Bokhari F, Guo Y, Goyal H. Prolonged length of stay in the emergency department and increased risk of hospital mortality in patients with sepsis requiring ICU admission. Emerg Med J. 2019;36:82–7.

    PubMed  CAS  Google Scholar 

  39. Hsieh CC, Lee CC, Hsu HC, Shih HI, Lu CH, Lin CH. Impact of delayed admission to intensive care units on patients with acute respiratory failure. Am J Emerg Med. 2017;35:39–44.

    Article  PubMed  Google Scholar 

  40. Hung SC, Kung CT, Hung CW, Liu BM, Liu JW, Chew G, et al. Determining delayed admission to intensive care unit for mechanically ventilated patients in the emergency department. Crit Care. 2014;18:485.

    Article  PubMed  PubMed Central  Google Scholar 

  41. Pitts SR, Pines JM, Handrigan MT, Kellermann AL. National trends in emergency department occupancy, 2001 to 2008: effect of inpatient admissions versus emergency department practice intensity. Ann Emerg Med. 2012;60:679-686.e3.

    Article  PubMed  Google Scholar 

  42. Tilluckdharry L, Tickoo S, Amoateng-Adjepong Y, Manthous CA. Outcomes of critically ill patients. Am J Emerg Med. 2005;23:336–9.

    Article  PubMed  Google Scholar 

  43. Ryu JH, Min MK, Lee DS, Yeom SR, Lee SH, Wang IJ, et al. Changes in relative importance of the 5-level triage system, Korean triage and acuity scale, for the disposition of emergency patients induced by forced reduction in its level number: a multi-center registry-based retrospective cohort study. J Korean Med Sci. 2019;34:e114.

    Article  PubMed  PubMed Central  Google Scholar 

  44. Choi DH, Hong WP, Song KJ, Kim TH, Shin SD, Hong KJ, et al. Modification and validation of a complaint-oriented emergency department triage system: a multicenter observational study. Yonsei Med J. 2021;62:1145–54.

    Article  PubMed  PubMed Central  Google Scholar 

  45. Quan H, Li B, Couris CM, Fushimi K, Graham P, Hider P, et al. Updating and validating the Charlson comorbidity index and score for risk adjustment in hospital discharge abstracts using data from 6 countries. Am J Epidemiol. 2011;173:676–82.

    Article  PubMed  Google Scholar 

  46. Pylväläinen J, Talala K, Murtola T, Taari K, Raitanen J, Tammela TL, et al. Charlson Comorbidity Index based on hospital episode statistics performs adequately in predicting mortality, but its discriminative ability diminishes over time. Clin Epidemiol. 2019;11:923–32.

    Article  PubMed  PubMed Central  Google Scholar 

  47. Kim YJ, Hong JS, Hong SI, Kim JS, Seo DW, Ahn R, et al. The Prevalence and emergency department utilization of patients who underwent single and double inter-hospital transfers in the emergency department: a nationwide population-based study in Korea, 2016–2018. J Korean Med Sci. 2021;36:e172.

    Article  PubMed  PubMed Central  Google Scholar 

  48. Kim JS, Seo DW, Kim YJ, Hong SI, Kang H, Kim SJ, et al. Emergency department as the entry point to inpatient care: a nationwide, population-based study in South Korea, 2016–2018. J Clin Med. 2021;10:1747.

    Article  PubMed  PubMed Central  Google Scholar 

  49. Affleck A, Parks P, Drummond A, Rowe BH, Ovens HJ. Emergency department overcrowding and access block. CJEM. 2013;15:359–84.

    Article  PubMed  Google Scholar 

  50. Castela Forte J, Yeshmagambetova G, van der Grinten ML, Hiemstra B, Kaufmann T, Eck RJ, et al. Identifying and characterizing high-risk clusters in a heterogeneous ICU population with deep embedded clustering. Sci Rep. 2021;11:12109.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  51. Engebretsen S, Bogstrand ST, Jacobsen D, Rimstad R. Characteristics, management and outcome of critically ill general medical patients in the emergency department: an observational study. Int Emerg Nurs. 2021;54:100939.

    Article  PubMed  Google Scholar 

  52. Wilhelms SB, Wilhelms DB. Emergency department admissions to the intensive care unit - a national retrospective study. BMC Emerg Med. 2021;21:122.

    Article  PubMed  PubMed Central  Google Scholar 

  53. Gunnerson KJ, Bassin BS, Havey RA, Haas NL, Sozener CB, Medlin RP, et al. Association of an emergency department-based intensive care unit with survival and inpatient intensive care unit admissions. JAMA Netw Open. 2019;2:e197584.

    Article  PubMed  PubMed Central  Google Scholar 

  54. You JS, Park YS, Chung SP, Lee HS, Jeon S, Kim WY, et al. Relationship between time of emergency department admission and adherence to the Surviving Sepsis Campaign bundle in patients with septic shock. Crit Care. 2022;26:43.

    Article  PubMed  PubMed Central  Google Scholar 

  55. Berg LM, Ehrenberg A, Florin J, Östergren J, Discacciati A, Göransson 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.

    Article  PubMed  Google Scholar 

  56. Grossmann FF, Zumbrunn T, Ciprian S, Stephan FP, Woy N, Bingisser R, et al. Undertriage in older emergency department patients–tilting against windmills. PLoS ONE. 2014;9:e106203.

    Article  PubMed  PubMed Central  Google Scholar 

  57. 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.

    Article  PubMed  Google Scholar 

  58. Augustine JJ. Latest data reveal the ED’s role as hospital admission gatekeeper. ACEP Now. 2019;38:1–3.

    Google Scholar 

  59. 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.

    Article  PubMed  Google Scholar 

  60. Glance LG, Li Y, Osler TM, Dick A, Mukamel DB. Impact of patient volume on the mortality rate of adult intensive care unit patients. Crit Care Med. 2006;34:1925–34.

    Article  PubMed  Google Scholar 

  61. Kahn JM, Goss CH, Heagerty PJ, Kramer AA, O’Brien CR, Rubenfeld GD. Hospital volume and the outcomes of mechanical ventilation. N Engl J Med. 2006;355:41–50.

    Article  PubMed  CAS  Google Scholar 

  62. Kocher KE, Haggins AN, Sabbatini AK, Sauser K, Sharp AL. Emergency department hospitalization volume and mortality in the United States. Ann Emerg Med. 2014;64:446-457.e6.

    Article  PubMed  Google Scholar 

  63. Lee H, Choi S, Jang EJ, Lee J, Kim D, Yoo S, et al. Effect of institutional case volume on in-hospital and long-term mortality in critically ill patients requiring mechanical ventilation for 48 hours or more. J Korean Med Sci. 2019;34:e212.

    Article  PubMed  PubMed Central  Google Scholar 

  64. Gandjour A, Lauterbach KW. The practice-makes-perfect hypothesis in the context of other production concepts in health care. Am J Med Qual. 2003;18:171–5.

    Article  PubMed  Google Scholar 

  65. Nguyen YL, Wallace DJ, Yordanov Y, Trinquart L, Blomkvist J, Angus DC, et al. The volume-outcome relationship in critical care: a systematic review and meta-analysis. Chest. 2015;148:79–92.

    Article  PubMed  PubMed Central  Google Scholar 

  66. Fryman L, Talley C, Kearney P, Bernard A, Davenport D. Maintaining an open trauma intensive care unit bed for rapid admission can be cost-effective. J Trauma Acute Care Surg. 2015;79(1):98–104. https://doi.org/10.1097/TA.0000000000000688.

  67. McCoy JV, Gale AR, Sunderram J, Ohman-Strickland PA, Eisenstein RM. Reduced hospital duration of stay associated with revised emergency department-intensive care unit admission policy: a before and after study. J Emerg Med. 2015;49:893–900.

    Article  PubMed  Google Scholar 

  68. Fuentes E, Shields JF, Chirumamilla N, Martinez M, Kaafarani H, Yeh DD, et al. “One-way-street” streamlined admission of critically ill trauma patients reduces emergency department length of stay. Intern Emerg Med. 2017;12:1019–24.

    Article  PubMed  Google Scholar 

  69. Kadar RB, Amici DR, Hesse K, Bonder A, Ries M. Impact of telemonitoring of critically ill emergency department patients awaiting ICU transfer. Crit Care Med. 2019;47:1201–7.

    Article  PubMed  PubMed Central  Google Scholar 

  70. Puls HA, Haas NL, Cranford JA, Medlin RP, Bassin BS. Emergency department length of stay and outcomes of emergency department-based intensive care unit patients. J Am Coll Emerg Physicians Open. 2022;3:e12684.

    PubMed  PubMed Central  Google Scholar 

  71. Wiler JL, Griffey RT, Olsen T. Review of modeling approaches for emergency department patient flow and crowding research. Acad Emerg Med. 2011;18:1371–9.

    Article  PubMed  Google Scholar 

Download references

Acknowledgements

We appreciate the dedication of Dr. Han-duk Yoon, the founder of NEDIS.

Disclosure

The authors have no potential conflicts of interest to disclose.

Funding

This study was supported by a grant from the National Medical Center, Korea (grant number: NMC2022-PR-01). However, the funding organization did not have any role in the collection, management, analysis, or interpretation of the data; preparation, review, or approval of the manuscript; or the decision to submit the manuscript for publication.

Author information

Authors and Affiliations

  1. Public Health Research Institute, National Medical Center, 245 Eulgi-Ro, Jung-Gu, Seoul, 04564, Korea

    Kyung-Shin Lee, Hye Sook Min & Ho Kyung Sung

  2. Department of Internal Medicine, Chungnam National University School of Medicine, Daejeon, Korea

    Jae Young Moon

  3. Department of Pulmonary and Critical Care Medicine, Chungnam National University Sejong Hospital, Sejong, Korea

    Jae Young Moon

  4. Department of Emergency Medicine, Seoul Medical Center, Seoul, Korea

    Daesung Lim

  5. Department of Trauma Surgery, National Medical Center, Seoul, Korea

    Younghwan Kim

  6. National Emergency Medical Center, National Medical Center, Seoul, Korea

    Eunsil Ko, Jeehye Lee & Ho Kyung Sung

  7. Department of Pediatrics, National Medical Center, Seoul, Korea

    You Sun Kim

  8. Division of Pulmonary and Critical Care Medicine, Department of Internal Medicine, National Medical Center, Seoul, Korea

    Joohae Kim

Authors
  1. Kyung-Shin Lee
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Hye Sook Min
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Jae Young Moon
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Daesung Lim
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Younghwan Kim
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Eunsil Ko
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. You Sun Kim
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Joohae Kim
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. Jeehye Lee
    View author publications

    You can also search for this author in PubMed Google Scholar

  10. Ho Kyung Sung
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

KSL, HSM, JYM, DL, and HKS contributed substantially to the conception and design of this study. KSL performed all the statistical analyses. KSL and HKS wrote the initial manuscript. HSM, JYM, DL, YK, EK, YSK, JK and JL critically read and revised the manuscript. All authors have read and approved the final version of the manuscript.

Corresponding author

Correspondence to Ho Kyung Sung.

Ethics declarations

Ethics approval and consent to participate

This study was approved by the institutional review board of the National Medical Center (approval number: NMC-2021–10-123) and conformed to the provisions of the Declaration of Helsinki. Because of the retrospective nature of this study, patient informed consent for inclusion was waived by the same board that approved the study protocol.

Consent for publication

Not applicable.

Competing interests

The authors have declared no competing interest to disclose.

Additional information

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary Information

Additional file 1:

Table S1. Characteristics of critically ill patients directly admitted to the intensive care unit from the emergency department by hospital staffed-bed category. Table S2. Characteristics of critically ill patients directly admitted to the intensive care unit by type of emergency department. Table S3. Sensitivity analysis for prolonged EDLOS. Table S4. Sensitivity analysis for in-hospital mortality.

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Lee, KS., Min, H.S., Moon, J.Y. et al. Patient and hospital characteristics predict prolonged emergency department length of stay and in-hospital mortality: a nationwide analysis in Korea. BMC Emerg Med 22, 183 (2022). https://doi.org/10.1186/s12873-022-00745-y

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1186/s12873-022-00745-y

Keywords

BMC Emergency Medicine

ISSN: 1471-227X

Contact us