Challenges and Contributing Factors to Emergency Department Overcrowding in Taiwan After the Lunar New Year Holiday: A 2024 Survey

Abstract

Background

Emergency department (ED) overcrowding has become a widespread global problem, with multi-factorial causes spanning input, throughput, and output domains. In Taiwan, the unique context of universal health coverage and a severe nursing shortage further complicates the situation. The Lunar New Year holiday period is associated with increased ED demand, yet the extent, causes, and responses to post-holiday overcrowding remain unclear.

Methods

We conducted a descriptive observational survey targeting ED directors from all certified emergency care hospitals in Taiwan one week after the 2024 Lunar New Year holiday (February 8 to 14). The questionnaire compared operational status with the same period in previous years, assessing patient volume, bed availability, staffing, perceived causes of overcrowding, and implemented countermeasures. Data from 59 responding hospitals were analyzed using Chi-square, ANOVA/Kruskal–Wallis tests, and logistic regression to identify factors associated with unusual operational status and prolonged waiting for beds.

Results

Of the 59 hospitals (18 medical centers, 20 regional hospitals, 21 district hospitals), 41 (69.5%) reported abnormal post-holiday ED operations, including severe overcrowding, hospitalization difficulties, and increased bed full notifications. In multivariate analysis, prolonged waiting for beds was the only factor significantly associated with severe operational anomalies (odds ratio [OR] = 11.31, p = 0.019). Factors contributing to prolonged waiting included decreased ED nurse staffing (OR = 5.40, p = 0.021), closure of general ward beds (OR = 3.26, p = 0.032), and closure of ICU beds (OR = 6.27, p = 0.025). A one-nurse decrease increased the odds of waiting for beds by 25% ( p = 0.008), and a 1% ward bed closure increased the odds by 7.1% ( p = 0.012). Although 35 hospitals implemented countermeasures such as opening extra beds or restricting transfers, only 14.3% reported significant improvement.

Conclusion

Reduced nursing staff and closure of general wards and ICU beds were strongly associated with prolonged waiting for beds and ED overcrowding after the Lunar New Year holiday. Current hospital-level measures have limited and temporary effects. A comprehensive approach integrating ED process optimization, hospital-wide management strategies, and community-level interventions is needed to improve bed allocation efficiency, strengthen nursing workforce sustainability, and alleviate overcrowding in Taiwan’s EDs.

Introduction

The issue of emergency department (ED) crowding has increasingly become a significant and common global problem over the past decade. Overcrowding represents an imbalance between the urgent medical needs of the ED and the hospital’s ability to provide services, resulting in a mismatch between supply and demand. 1 , 2 Many studies have already demonstrated that ED overcrowding can lead to delays in the diagnostic process and the initiation of treatment, thus exacerbating overcrowding itself. 3 - 5 Furthermore, it poses risks to patient safety. 3 Over the years, many believed that excessive congestion in EDs was due to high volumes of uninsured patients and unnecessary visits. 1 , 3 However, these two factors are insufficient to explain the overcrowding situation. Recent studies indicate that the issue of ED overcrowding is multifaceted and complex, encompassing input problems such as urgent visits, non-urgent visits, and ambulance arrivals; throughput problems such as the triage and bed placement process, and ED bed availability; and output problems such as patient boarding, hospital occupancy and inpatient bed shortages. 2 , 3 , 6

The environment in Taiwan is unique. Due to the national health insurance system, there are very few uninsured people. Taiwan is facing a severe shortage of nurses, which may be related to workplace environment and salary structure issues. 7 It may exacerbate the difficulties in the healthcare field. This study surveyed 59 hospitals in Taiwan to explore the issue of EDs overcrowding after holiday. Analysis of the challenges and contributing factors, along with a review of key literature related to emergency department crowding. It aimed to identify the main causes of overcrowding and discuss strategies for improvement within the existing environment and conditions.

Methods

Study Design and Survey Distribution

The study employed a descriptive observational design to investigate ED overcrowding in Taiwan specifically after the Lunar New Year holiday period. We chose the period following the Lunar New Year because it is a unique time when ED operations can be significantly affected by factors such as a temporary increase in holiday-related illnesses due to family gatherings, travel, and a subsequent surge in individuals seeking medical care after the holiday. However, the study also aimed to investigate whether ED overcrowding has become a more widespread issue beyond the immediate post-holiday period. By comparing the current year’s data with that from the same period in previous years, we sought to identify whether the surge in demand after the holiday was an isolated phenomenon or part of a broader trend of increasing ED overcrowding. The goal of this study was to understand both the acute impacts of the holiday and any persistent factors contributing to ongoing overcrowding trends, thus providing a comprehensive view of the dynamics influencing ED overcrowding in Taiwan. The survey was distributed one week after the Lunar New Year holiday to ED directors of hospitals in Taiwan certified as emergency care hospitals at various levels of emergency medical capability. Respondents were asked to compare the conditions and challenges of the week prior to completing the questionnaire with the corresponding week in previous years. The questionnaire assessed various aspects of ED operations and challenges. It included questions on comparing current conditions to previous years, changes in daily patient volumes, bed availability, staffing, perceived causes of overcrowding, and measures taken in response to unusual operational status. ( Table S1 ).

Statistical Analysis

Categorical data were expressed as frequency and percentage, while numerical data were expressed as mean ± standard deviation (SD) or median and interquartile range (Q1, Q3) depending on the normality of data distribution, assessed using the Shapiro-Wilk test. The differences between groups were analyzed using the Chi-Square test, ANOVA, or Kruskal-Wallis H test, depending on whether the data were categorical or numerical. The impacts of abnormal operational items and the contributing factors of changes in visits and service capacity were revealed using logistic regression. All statistical analyses were conducted using IBM SPSS Statistics for window version 27.0 (SPSS Inc., Chicago, USA).

Results

The study investigated ED overcrowding in Taiwan specifically after the Lunar New Year holiday. The Lunar New Year holiday spanned from February 8 to February 14, 2024, covering 7 consecutive days off. The survey was distributed on February 22, 2024, to a total of 205 ED directors of hospitals in Taiwan certified as emergency care hospitals at various levels of emergency medical capability. Respondents were asked to compare the conditions and challenges of the week prior to completing the questionnaire with the corresponding week in previous years. The questionnaire received responses from a total of 59 hospitals in Taiwan during February 22–26, 2024. Among them, there were 18 medical centers, 20 regional hospitals, and 21 district hospitals ( Table 1 ). And 41 hospitals experienced varying extents of operational anomalies in ED during the week after this year’s Lunar New Year holiday compared to previous years. Twenty-four hospitals (40.7%) reported experiencing substantial unusual operational status, while 17 hospitals (28.8%) reported minor unusual status. There was no significant difference among the three levels of hospitals ( p = 0.694). The abnormal items and the proportion within hospitals experiencing operational anomalies were listed in Table 1 . The abnormal items presented in over 50% of hospitals included severe overcrowding, difficulty in hospitalization, increase in bed full notifications, and increase in hospital admission. Thirty-four hospitals (57.6%) experienced an increase of more than 10% in waiting for beds.

Table 1 . The operational status and overcrowding in hospital ED after the Lunar New Year holiday compared to the corresponding week in previous years ^a .

^* The p -value less than 0.05 indicated significant differences between groups.

Characteristics	Total (N = 59)	Medical center (n = 18)	Regional hospital (n = 20)	District hospital (n = 21)	p
Operational status					0.694 b
No significant difference	18 (30.5)	4 (22.2)	6 (30.0)	8 (38.1)
Minor unusual	17 (28.8)	6 (33.3)	7 (35.0)	4 (19.0)
Substantial unusual	24 (40.7)	8 (44.4)	7 (35.0)	9 (42.9)
Abnormal items of unusual operational status (41 items), median (IQR)	3.0 (2.0, 4.0)	3.5 (2.0, 4.25)	3.0 (2.0, 4.0)	3.0 (1.0, 5.5)	0.315 c
Severe overcrowding, n (%)	29 (70.7)	10 (71.4)	11 (78.6)	8 (61.5)	0.622 b
Difficulty in hospitalization, n (%)	27 (65.9)	11 (78.6)	8 (57.1)	8 (61.5)	0.438 b
Increase in bed full notifications, n (%)	26 (63.4)	8 (57.1)	10 (71.4)	8 (61.5)	0.721 b
Increase in hospital admissions, n (%)	23 (56.1)	6 (42.9)	9 (64.3)	8 (61.5)	0.465 b
Increase in stretcher bed usage, n (%)	14 (34.1)	7 (50.0)	4 (28.6)	3 (23.1)	-
Increase in transfers, n (%)	10 (24.4)	6 (42.9)	0 (0.0)	4 (30.8)	-
Increase in discharges/transfers out, n (%)	3 (7.3)	0 (0.0)	0 (0.0)	3 (23.1)	-
Others, n (%) d	4 (9.8)	0 (0.0)	1 (7.1)	3 (23.1)	-
Waiting for beds compared to previous years, n (%)					0.882 b
Mild decreased	2 (3.4)	0 (0.0)	1 (5.0)	1 (4.8)
Increase < 10%	23 (39.0)	7 (38.9)	7 (35.0)	9 (42.9)
Increase 10%–20%	14 (23.7)	4 (22.2)	6 (30.0)	4 (19.0)
Increase > 20%	20 (33.9)	7 (38.9)	6 (30.0)	7 (33.3)

The daily visits in ED during the week after the Lunar New Year holiday showed significant differences among three levels of hospitals: medical centers, regional hospitals, and district hospitals with average or median of 294.3, 174 and 100 visits, respectively ( p < 0.001) ( Table 2 ). Regarding changes in visits to ED compared to previous years, 50.9% of the hospitals experienced changes greater than 10%, while 49.2% experienced changes less than 10% and minor decreases. Approximately half of hospitals experienced an increase in patients, and half did not. Regarding changes in service capacity, in terms of staffing, hospitals with a decrease in emergency physicians accounted for only 8.6%, while those with a decrease in emergency nurses amounted to as much as 40.7%. Additionally, 52.5% of hospitals closed general ward beds, and 23.7% closed ICU beds, with the average proportion of closed beds being 20.7% and 21.0%, respectively.

Table 2 . Fluctuations in emergency department (ED) visits and service capacity after the Lunar New Year holiday compared to the corresponding week in previous years ^a .

^* The p -value less than 0.05 indicated significant differences between groups.

Characteristics	Total (N = 59)	Min to Max	Medical center (n = 18)	Regional hospital (n = 20)	District hospital (n = 21)	p
Daily visits after the Lunar New Year holiday	170 (100, 269)	20 to 1,021	294.3 ± 94.4	174 (117.5, 206)	100 (80, 133)	< 0.001*, b
Changes in visits, n (%)						0.131 c
Minor decreased	4 (6.8)		1 (5.6)	2 (10.0)	1 (4.8)
Increase < 10%	25 (42.4)		10 (55.6)	9 (45.0)	6 (28.6)
Increase 10%–20%	25 (42.4)		4 (22.2)	9 (45.0)	12 (57.1)
Increase > 20%	5 (8.5)		3 (16.7)	0 (0.0)	2 (9.5)
Changes in ED Physician staffing, n (%)						0.168 c
Decreased	11 (18.6)	1 to 3	4 (22.2)	3 (15.0)	4 (19.0)
No change	30 (50.8)		5 (27.8)	12 (60.0)	13 (61.9)
Increased	18 (30.5)	1 to 3	9 (50.0)	5 (25.0)	4 (19.0)
Changes in ED nurse staffing, n (%)						0.832 c
Decreased	24 (40.7)	1 to 13	9 (50.0)	8 (40.0)	7 (33.3)
No change	21 (35.6)		5 (27.8)	8 (40.0)	8 (38.1)
Increased	14 (23.7)	1 to 6	4 (22.2)	4 (20.0)	6 (28.6)
Closing of general ward beds						0.522 d
No	28 (47.5)		8 (44.4)	8 (40.0)	12 (57.1)
Yes	31 (52.5)	12 to 288	10 (55.6)	12 (60.0)	9 (42.9)
The proportion of closed general ward beds (%)	20.7 ± 12.2	2.2 to 49.0	15.17 ± 7.93	20.93 ± 11.00	26.70 ± 16.02	0.153
Closing of ICU beds						0.529 c
No	45 (76.3)		12 (66.7)	16 (80.0)	17 (81.0)
Yes	14 (23.7)	4 to 25	6 (33.3)	4 (20.0)	4 (19.0)
The proportion of closed ICU beds (%)	21.0 ± 11.1	4 to 40	13.55 ± 10.42	26.93 ± 6.84	26.14 ± 10.40	0.082

In response to ED overcrowding, 35 hospitals had proposed and implemented various contingency measures as outlined in Table 3 . These measures included reporting full beds, increasing inpatient beds for ED, accelerating handovers, supporting emergency medical staff, increasing transfers to other facilities, creating waiting areas, restricting referrals/transfers, and others such as expediting patient discharge processes, borrowing stretchers from other units, transitioning to home care and intra-system transfers, which listed in Table 3 . However, these measures were perceived as temporarily effective by 54.3% of hospitals, with only 14.3% showing significant improvement ( Table 3 ).

Table 3 . The contingency measures and their effectiveness for overcrowding in hospital emergency departments (ED) during unusual operational statuses ^a .

^a Categorical data was expressed as frequency and percentage. The differences among the three levels of hospitals were analyzed using Chi-Square\Likelihood Ratio test as more than one of cell count less than 5 or 20% in crosstab more than 2 × 2 cells.

Characteristics	Total	Medical center	Regional hospital	District hospital	p c
Implementation of contingency measures for overcrowding	n = 24	n = 9	n = 12	n = 7	0.181
No, n (%)	3 (12.5)	1 (33.3)	0 (40.0)	2 (47.6)
Yes, n (%)	21 (87.5)	8 (66.7)	8 (60.0)	5 (52.4)
Contingency measures for overcrowding	n = 35	n = 12	n = 12	n = 11
Report full beds	32 (91.4)	12 (100.0)	10 (83.3)	10 (90.9)	0.228
Increase inpatient beds for ED	19 (54.3)	9 (75.0)	5 (41.7)	5 (45.5)	-
Accelerate handovers	18 (51.4)	5 (41.7)	6 (50.0)	7 (63.6)	-
Support emergency medical staff	16 (45.7)	6 (50.0)	4 (33.3)	6 (54.5)	-
Increase transfers to other facilities	10 (28.6)	3 (25.0)	4 (33.3)	3 (27.3)	-
Create waiting area	7 (20.0)	2 (16.7)	2 (16.7)	3 (27.3)	-
Restrict referrals/transfers	7 (20.0)	1 (8.3)	3 (25.0)	3 (27.3)	-
Others b	4 (11.4)	1 (8.3)	2 (16.7)	1 (9.1)	-
The effectiveness of contingency measures for overcrowding	n = 35	n = 12	n = 12	n = 11	0.058
Ineffective	3 (8.6)	0 (0.0)	2 (16.9)	1 (9.1)
Inconclusive	8 (22.9)	3 (25.0)	4 (33.3)	1 (9.1)
Temporarily effective	19 (54.3)	9 (75.0)	3 (25.0)	7 (63.6)
Effective	5 (14.3)	0 (0.0)	3 (25.0)	2 (18.2)

Forty-one hospital EDs experienced various degrees of unusual operational status with minor or substantial ( Table 1 ). First, we attempted to identify which of the abnormal items had the greatest impact on the severity of overall anomaly using univariate and multivariate logistic regression analysis ( Table 4 ). The extent of waiting for beds, severe overcrowding, and difficulty in hospitalization were found to be potentially associated with the severity of unusual operational status, with odds ratios (ORs) of 10.20 ( p = 0.016), 7.88 ( p = 0.009), and 7.14 ( p = 0.008), analyzed using univariate logistic regression. The results of the multivariate logistic regression indicated that only the severity of waiting times for beds was significantly correlated with the severity of unusual operational status in the ED (OR = 11.31, p = 0.019). This suggested a causal relationship where severe overcrowding and difficulty in hospitalization contributed to the severity of waiting for beds. Next, we endeavored the contributing factors affected waiting for beds ( Table 5 ). The results from logistic regression analysis showed that decreased ED nurse staffing (OR = 5.40 compared to Increased, p = 0.021), closing of general ward (OR = 3.26, p = 0.032), and closing of ICU beds (OR = 6.27, p = 0.025) contributed to waiting for beds. While decreasing one-nurse got increasing 25% risk ( p = 0.008) in waiting for beds, and closing 1% of general ward bed got increasing 7.1% risk ( p = 0.012).

Table 4 . Analysis of the impact of the abnormal items on the severity of unusual operational status in emergency department (ED) using logistic regression ^a .

^* P -value less than 0.05 indicated significant differences between groups.

Variables	Univariate Odds ratio	p	Multivariate Odds ratio	p
Independent variable
Severity of waiting for beds (Increase > 20% vs. Increase 10%–20%)	10.20	0.016 *	11.31	0.019 *
Severe overcrowding	7.88	0.009 *	0.34	0.511
Difficulty in hospitalization	7.14	0.008 *	4.87	0.204
Increase in bed full notifications	3.38	0.072	1.02	0.983
Increase in hospital admissions	2.86	0.110
Increase in stretcher bed usage	2.32	0.233
Increase in transfers	3.75	0.128
Increase in discharges/transfers out	NA b
Others	NA b

Table 5 . Revelation of the contributing factors affected waiting for beds using logistic regression .

^* P -value less than 0.05 indicated significant differences.

Variables	Odds ratio a	p
Independent variable
Visits increased > 10%	2.87	0.053
Changes in ED physician staffing
Decreased	0.42	0.265
No change	0.65	0.494
Increased	Ref
Changes ED Physician counts	0.74 a	0.173
Changes in ED nurse staffing
Decreased	5.40	0.021 *
No change	1.98	0.335
Increased	Ref
Changes ED nurse counts	1.25 a	0.008 *
Closing of general ward beds	3.26	0.032 *
The proportion of closed general ward beds (%)	1.071	0.012 *
Closing of ICU beds	6.27	0.025 *
The proportion of closed ICU beds (%)	1.064	0.082

Discussion

In this study, a survey of 59 hospitals indicated that 41 experienced altered operations in their ED after 2024 Lunar New Year holiday compared to previous years. Common abnormalities included severe overcrowding, difficulty in hospitalization, increase in bed full notifications, and increase in hospital admissions. The statistical results showed that severe overcrowding and hospitalization difficulties contributed to the unusual operational issue of waiting for beds. Moreover, decreased ED nurse staffing levels and closures of general wards and ICU beds were significantly associated with waiting for beds in EDs.

Causes of ED Overcrowding

When discussing the issue of ED crowding, the input-throughput-output model is often used. 2 , 5 We used this model to analyze the overcrowding situation after the Lunar New Year period.

One week after the Lunar New Year holiday, the number of emergency patients increases at medical centers, regional hospitals, and district hospitals alike. As mentioned in the results, the increase in patient volume is significantly correlated with unusual operation status. At the medical center, the average daily patient volume is close to 300 visits. Moreover, more than half of the hospitals reported an increase in patient visits by more than 10% compared to previous years. Possible reasons include reduced availability of medical services during the holiday period, with local clinics or outpatient department services being either not offered or limited. 9 , 10 Furthermore, during the holiday season, many people travel or return to their hometowns, enduring mild symptoms until after the holiday when their condition worsens and they seek medical attention.

Throughput factors are those internal to the ED itself. As for staffing issues within the ED, while only 18% of hospitals reported a decrease in emergency attending physicians, 40% of hospitals experienced a reduction in nursing staff. Additionally, adjustments in nursing staff are indeed associated with the severity of waiting bedslist. In the ED, nursing duties include triage, executing orders, administering medications, drawing blood, and continuously monitoring patients’ vital signs, among other tasks. 11 Therefore, with reduced nursing staff, even if there are enough doctors who can quickly diagnose and issue medical orders, the time and manpower of nurses to carry out medical orders are not enough. When tasks are delayed, more patients will be waiting in the ED, exacerbating congestion. Additionally, the increase in the number of patients waiting for beds will strain nursing manpower, creating a vicious cycle.

Output factors can be summarized by admission, discharged or transferred. The availability of beds has a big impact on overcrowding. 3 The inability to smoothly transfer patients to admission results in patients lingering longer in the ED. Consequently, more manpower is required to care for them and address issues that should be resolved during hospitalization. 5 Several studies have reported exit block is able to negatively influence the patient’s outcome, for example, the waiting times for patients requiring emergency surgery have increased significantly. 12

During the Lunar New Year period, many hospitals close some of wards. 52.5% of hospitals close regular ward beds, while 23.7% close ICU beds. The supply of beds is less than the demand, resulting in increased waiting beds and decreased accessibility to medical care for patients. The ED, which already experiences increased patient volume, becomes even more congested due to the phenomenon of exit block, exacerbating the situation. Our survey also indicates that the severity of waiting for beds is a contributing factor to severe congestion. We believe that the closure of the ward is also related to nurse staffing. Already strained during normal periods, the number of nurses working during holidays is even lower than usual. 13

Current Solutions

Current hospital solutions and response measures included notifying when beds are full, restricting transfers, increasing support for ED healthcare personnel, or opening up additional temporary bed space. In some cases, hospitals may borrow beds from other units, transfer patients to other hospitals within the same healthcare system, or transition to home-based care as alternative measures. Unfortunately, only 14% of these measures are effective, with the rest showing limited effectiveness or having a short duration of effect. Currently, individual hospital-based response measures have proven to be short-lived and limited in effectiveness. Therefore, addressing ED overcrowding cannot rely solely on individual hospital initiatives but requires a more comprehensive and systemic approach.

Reform strategy

Strategies to improve ED overcrowding can be discussed from various aspects including ED processes, hospital management, and community involvement, as shown schematically in Table 6 . 2 , 5 , 6

Table 6 . Reform strategy .

Strategies	Solutions
Emergency department processes	Unnecessary visits
	Bed utilization
	Team triage
	Hospitalists assist
Hospital management	Strengthening the connections between other medical departments
	Hospitalist ward model
	Nurse manpower
Policy	Transparency of hospita lbed availability
	Referral system
	Public education

(1) Unnecessary visits: The use of emergency rooms by patients with minor ailments has long been a point of criticism and is a topic frequently discussed in international studies addressing ED overcrowding. These patients would typically seek care at clinics or outpatient departments during regular times. However, with routine medical services closed during holiday periods, they have no choice but to seek assistance at the ED. 5 , 14 One approach is to establish areas similar to outpatient clinics, providing alternative medical resources for patients with non-urgent complaints such as the common cold or mild gastroenteritis could help alleviate ED congestion. 14 - 16

(2) Bed utilization: Optimizing bed utilization can enhance ED workflows. 17 , 18 Additionally, literature has proposed a strategy for efficient bed utilization by strictly enforcing a 48-hour treatment limit, providing emergency patients needing admission with early admission opportunities. This strategy compels other specialties to vacate space to timely care for these patients, effectively managing their inpatient bed occupancy. 19

(3) Team triage: Team triage is another model that has been considered, where nursing and medical staff jointly triage patients. However, its effectiveness in addressing ED overcrowding is controversial. Only one study found a decrease in mortality rates, but no impact on waiting or treatment times was observed. 14 , 15 , 20 , 21

(4) Hospitalists assist: Due to prolonged waiting time for a bed in the ED, ongoing assessment of the patient’s current condition is necessary. Recent studies have found that having hospitalists assist in the care of emergency patients who need hospitalization can improve the quality of care, especially in situations where EDs are congested. Briones (2019) 22 had reported that hospitalist-led caring can improve the follow-up of test results and medication orders, as well as reduce the reliance on medical resources. Bajaj also indicated that general medicine consultant-led ward rounds (CWRs) in the ED can help reduce the length of stay. 23 The role of a hospitalist goes beyond caring for patients; they should also constantly evaluate whether a patient still needs to be hospitalized. In some cases, by the time a patient is waiting for a bed, their condition might have already improved. 6 This approach can also enhance bed utilization efficiency.

As mentioned, ED overcrowding is often not solely an issue within the ED itself but rather requires efforts across the entire hospital. However, the strategies and approaches proposed by each hospital differed.

(1) Strengthening the connections between other medical departments: Enhancing outpatient and home healthcare functions can also help reduce the frequency of patient visits to the ED. 24 Although “frequent flyers” represent only a small portion of all ED patients, they account for about one-third of visits. 1 , 25 Among them, the elderly with chronic health conditions are the primary group. 26 By effectively identifying this population through comprehensive geriatric assessments and intervening with services such as rehabilitation, home nursing, day care, and frailty education, it is possible to significantly reduce admissions at the index ED visit and, consequently, decrease the number of individuals requiring hospitalization. 27 , 28

(2) Hospitalist ward model: There are also some rules that affect the smoothness of ED flow. Taiwan operates under a sub-specialty system, which means that if a patient’s admission requires multiple sub-specialties or if the severity of the illness is not high but the patient’s aging and disability are severe, hospitals’ sub-specialties may prioritize these patients as lower priority for admission. The hospitalist ward, composed of physicians from various specialties, will admit these difficult-to-hospitalize patients and serve as a post-ED ward, effectively addressing the issue of ED congestion. Therefore, the hospitalist ward model has been widely adopted in recent years. 29 - 31

(3) Nurse manpower: The reduction in nursing staff is also correlated with difficulties in hospitalization, further highlighting the importance of nursing personnel in the healthcare system. However, there is a high turnover rate and short career duration among nurses in Taiwan. Burnout and disproportionate compensation are major reasons for nurse turnover. Kelly et al. (2021) reported that in their study sample, more than half of the nurses suffer from moderate burnout. As their tenure increases, their emotional exhaustion scores also rise, and this is correlated with staff turnover. 32 Therefore, it is necessary to implement a more favorable nursing system, including adjustments to shift schedules and reducing the patient-nurse ratio. 33 , 34 Blindly expanding nursing program enrollment or lowering the qualifications for nursing licensure exams are not suitable solutions. It is essential to fundamentally improve nurses’ salaries and benefits, especially for those working in critical care units.

(1) Transparency of hospital bed availability: Currently, in Taiwan’s policies, there is a lack of transparency regarding bed availability among hospitals, and sometimes even within individual hospitals themselves. Therefore, transparency regarding bed availability is necessary and important. It can provide clearer directions for emergency referrals and transfers. 35 It also enhances the efficiency of medical care-seeking processes.

(2) Referral system: There are no clear legal regulations governing the referral system now. As a result, many patients remain stranded in the EDs of medical centers and are unwilling to be hospitalized in medium-sized or smaller hospitals. Strengthening alliances between medium-sized hospitals and medical centers could be beneficial. By assigning referral coordinators to facilitate the transfer process, they can assist in transferring mildly ill patients to medium-sized hospitals. 36

(3) Public education: Furthermore, the government should strongly advocate for and ensure the implementation of patient consultation times based on triage. Additionally, the government should educate the public on correct healthcare-seeking behaviors through electronic media. 37

Conclusion

Reduced nurse staffing, closures of general wards and ICU beds and difficulties in hospitalization resulted in waiting for beds and overcrowding in the ED. Unfortunately, the current system is unable to address this dilemma. A more comprehensive approach should be developed, encompassing ED processes, hospital management, and community involvement. By exploring these three aspects, we aim to achieve more effective allocation of beds and improve the nursing work environment to alleviate overcrowding in Taiwan’s EDs.

Table S1 . Suevey questionnaire topic .

為了解實際狀況，以適時向官署提出警訊，請各位主管協助回答下列問題

醫院名稱:

1. 醫院等級 □醫中 □區域 □地區

2. 近一周每日急診病人量平均約 人(含兒童人數)

3. 相較往年同一時期，此病人量 □略降　□差異<10%　□成長10%-20%　□成長>20%

4. 近一周每日急診待床數平均約 人(含兒童人數)

5. 相較往年同一時期，此待床數　□略降　□差異<10%　□成長10%-20%　□成長>20%

6. 與往年春節後相較，貴急診目前運作狀況？　□差異不大　□輕微異常　□明顯異常

7. 異常現象為(可複選)？　□需住院者異常增多　□壅塞異常嚴重　□住院異常困難　□轉入異常增多 □轉出異常增多　□通報滿床次數增多　□扣擔架床次數增多　□ 其他

8. 後線普通病房有無關床情形？　□無　□有 (請問原普通病房床數？ ，目前開放床數？ )

9. 後線加護病房有無關床情形？　□無　□有 (請問原登記ICU床數？ ，目前開放ICU床數？ )

10. 相較去年同一時期，急診主治醫師人數？　□增加　□減少？ 位

11. 相較去年同一時期，急診護理人數？　□增加　□減少？ 位

12. 貴院急診發生壅塞時，有無 啟動應變機制？　□無　□有 (續答12.13)

13. 壅塞應變機制包括？　□通報滿床　□限制轉診　□支援急診醫護人力　□增多給急診的住院床數 □另闢待床空間　□加速後線交班　□增加轉至他院人數　□其他

14. 貴院壅塞應變機制 成效?　□無效　□不明顯　□短暫有效　□成效良好