a b s t r a c t

Introduction: Emergency department (ED) overcrowding is linked to poor outcome and decreases patient satis- faction. Strategies to control Emergency department (ED) overcrowding has been subject of research.

Study objectives: The objective of this systematic review and meta-analysis was to investigate the impact of triage liaison providers (TLPs) on the ED throughput.

Methods: We searched PubMed, EMBASE, and Web of Science up to April 2019 for studies done in the United States. Primary outcomes were number of patients Left without being seen and patients’ emergency department length of stay (ED-LOS). ED-LOS data was pooled using mean difference with random effect model. Risk Ratio (RRs) for LWBS was calculated with random effect model with 95% confidence interval (95% CI).

Results: Twelve studies encompassing 329,340patients were included in the meta-analysis. Implementation of the TLP system using attending physicians was associated with a decrease in risk of LWBS 0.62 (95% CI 0.54, 0.71), The change in ED-LOS after implementation of TLP was too heterogeneous to pool the data with the mean ?ED-LOS ranging from -82 to +20 min. Stratification of studies by disposition, admitted versus discharged, did not decrease the heterogeneity.

Conclusion: Implementation of TLP can decrease the rate of LWBS however this review is inconclusive about the effect of TLP on ED-LOS due to the high heterogeneity observed in the literature.

(C) 2020

Introduction

ED volume has consistently increased over the last decade, with

145.6 million ED visits in 2016 [1]. This increase, combined with a mul- titude of other factors, has led to ED overcrowding. Studies have demon- strated overcrowding being tied to adverse side effects, including increasED wait times, decreased patient satisfaction, and most impor- tantly poor patient outcomes. Specifically, overcrowding has been linked with inadequate and delayed resuscitation in septic patients, de- creased and delayed analgesia administration, increased mortality in community acquired pneumonia patients, increase risk of adverse out- comes in patients presenting with chest pain, wrong patient medication administration, and generalized increase in morbidity and mortality [2- 11]. As a result, the Committee on Medicare and Medicaid Services (CMS) has begun to publicly report ED throughput metrics publicly to hold hospitals accountable.

* Corresponding author.

E-mail address: [email protected] (R. Benabbas).

In an effort to combat ED overcrowding and mitigate the risk of adverse outcomes, various solutions have been proposed, including triage protocols, fast track implementation, and the rise of urgent care centers [12-14]. A key area of interest has particularly been the investigation into the use of advanced providers in triage to help improve ED throughput and minimize patient risk exposure. Numerous groups have studied the impact of triage liaison physi- cians (TLPs) on several CMS quality metrics, with ED Length of Stay (ED-LOS) and Left Without Being Seen (LWBS) being the most perti- nent to the Emergency Department. Two previous systematic re- views and meta-analysis exist on the impact of TLPs on ED-LOS and LWBS. Both studies, Rowe et al. [15] and Abdulwahid et al. [16], in- cluded studies from different hospital settings in different countries and in both studies results were too heterogeneous to draw defini- tive conclusions.

In an attempt to better assess the impact of TLPs on ED throughput, we conducted a systematic review and meta-analysis of studies focused on US EDs. Through this, we hope to better ascertain the role of TLPs in improving throughput metrics to help minimize patient risk and better improve safety and quality metrics set forth by CMS.

https://doi.org/10.1016/j.ajem.2020.04.068

0735-6757/(C) 2020

Methods

Study design

We conducted a systematic review and meta-analysis of studies that examined the impact of Triage Liaison Provider (TLP) on emergency de- partment throughput. The systematic review was conducted using the Preferred Reporting Items for Systematic Review and Meta-analyses (PRISMA) guidelines [17]. The protocol for this systematic review and meta-analysis is published in the PROSPERO database with the registra- tion number: CRD42018094746.

Search strategy

In conjunction with a medical librarian, four investigators indepen- dently searched the medical literature in PubMed, Embase, and Web of Science from inception to April 2019. See Appendix A for the search strategy. The PubMed, Embase, and Web of Science searches were com- bined and limited by human-subject and English-language. We also hand searched the bibliography of the included articles as well as all previous systematic reviews and meta-analyses that were identified in our searches. We searched for abstracts at opengrey.eu, www.ntis.gov, and clinicaltrials.gov and also went through the scientific meetings of Society for Academic Emergency Medicine, and American College of Emergency Physicians from April 2012 to April 2019.: the Society of Ac- ademic Emergency Medicine (SAEM), the American College of Emer- gency Physicians (ACEP), the American Academy of Emergency Medicine (AAEM). We also searched Opengrey.edu and googlescholar. com. However in order to be able to assess the quality of the studies properly we only included studies that were available in full-text. When the full-text could not be found via database searches we attempted to contact the presenting authors of the abstracts.

Eligible studies were randomized control trials (RCTs),Controlled clinical trials, prospective, retrospective, case-control, or before- after in design done in the United States. Editorials and review articles were excluded. Studies were only included if they reported information re- garding at least one of main outcomes: number or proportion of pa- tients who left without being seen (LWBS) or emergency department length of stay (ED-LOS). In order to qualify for inclusion, articles had to clearly specify the number of patients seen in the ED in each group.

Data extraction

Information regarding study design and setting was extracted by the authors and were entered in predetermined data extraction sheets. The extraction sheets contained information such as year of publication, study design, number and type of study centers, type of provider partic- ipating in the TLP program, annual ED volume, admission rate, and mea- sure of outcomes: LWBS, ED-LOS, Left without complete assessment (LWCA).

Outcome variables

ED-LOS was defined as the time interval between registration and disposition. LWBS was defined as patients who left after triage but be- fore being evaluated by a physician or a NPP. LWCA was defined as pa- tients who left the ED after being evaluated by a physician or a NPP but before a final disposition by the provider.

Data analysis

ED-LOS data was pooled using mean difference with random effect model. Where data was reported as median and interquartile range IQR, we calculated the standard deviation (SD) from IQR [18]. Risk Ratio (RRs) for LWBS and LWCA were calculated with random effect model with 95% confidence interval (95% CI). Data was only pooled if

I-square was less than 75% [19]. All data was analyzed using RevMan5 review manager. (RevMan. Version 5.3. Copenhagen: The Nordic Cochrane Centre, The Cochrane Collaboration, 2014). For each outcome sensitivity analysis was performed in an attempt to separate and com- pare results based on type of setting (academic vs nonacademic) and type of provider acting as TLP (attending physician, resident physician, non-physician provider). Non-physician Providers (NPP) are physician assistants (PA) and nurse practitioners (NP) also known in some litera- ture as mid-level providers.

Quality assessment

We used a tool developed by the Effective Public Health Practice Pro- ject (EPHPP) [20] to appraise the quality of the included studies. This tool assesses the quality of studies in 6 domains: selection, design, con- founders, blinding, data collection, withdrawals and dropouts. Each do- main is graded as “weak”, “moderate” and “strong”. Each study also receives an overall assessment. All agreements were resolved by consensus.

Results

The combined PUBMED, EMBASE, Web of Science search resulted in 505 citations. Eighteen more studies were found during manual search or grey literature search. After removal of duplicates, 366 unique cita- tions were reviewed. Twenty-one studies were reviewed in full text and 12 studies [21-32] met the eligibility criteria (Fig. 1). See Table 1 for characteristics of included studies. Studies in which implementation of TLP was part of a bigger change in the system such as significant in- crease in ED size, opening an observation and/or decision units [33,34] were excluded. We also excluded studies that compared TLP with an- other intervention such as automatic or rotational provider assignment [35,36].

Using EPHPP tool the overall quality of 66.7% studies was assessed as moderate while 33.3% were of weak quality (Fig. 2). All studies were judged “weak” in data collection domain as they failed to provide details of how they trained and monitored data abstractors and if they used standardized abstraction forms. Most studies failed to blind providers to the nature of the study which can be a source of bias in prospective studies.

LWBS

Ten studies [21-25,27-31] encompassing 212,793 patients reported number of patients LWBS. The RR of LWBS ranged from 0.15 to 0.95 (Table 2, Fig. 3) which was too heterogeneous to pool the data, I- square of 96%. We performed a sensitivity analysis in an attempt to sep- arate the results based on type of provider who acted as TLP. We divided TLPs into physicians (attending physicians and resident physicians) and Non-physician Providers (Pas and NPs).

Six trials [21,22,25,27,30,31] encompassing 146,775 patients re- ported data of attending physicians as TLP with pooled RR of 0.62 (95% CI 0.54, 0.71), Fig. 4. Two studies [29,31] provided data on resident physicians as TLP and three studies [23,24,28] focused on nonphysician providers. Results were too heterogeneous to pool the data, I-square 83% for residents and 92% for NPPs. Table 2, Figs. 5, 6.

LWCA

Two studies [26,32] encompassing 116,547 patients reported num- ber of patients who left without complete assessment (LWCA) representing patients who were seen by a provider but left the ED be- fore completion of treatment. Implementing TLP resulted in a decrease in LWCA, RR 0.60 (95% CI 0.57-0.64). In both studies physicians acted as TLP. (Fig. 7).

Fig. 1. Study Selection process.

ED-LOS

Nine studies reported ED-LOS. In all studies except for Traub 2014 [30], LOS decreased in the TLP group. However, the change in ED-LOS was too heterogeneous to pool the data with the mean difference of ED-LOS pre and post TLP, ranging from -82 to +20 min, Fig. 8. Stratifi- cation of studies by disposition (admitted vs discharged) did not de- crease the heterogeneity. (Figs. 9, 10).

Discussion

Our meta-analysis found a decrease in LWBS (RR 0.62, 95% CI 0.54-0.71) when attending physicians acted as TLP. The results were not conclusive for resident physicians or mid-level providers prompting more research in this area. We also found a decrease in LWCA when TLP was implemented, RR 0.60 (95% CI 0.57-0.64). Analysis of the ED-LOS revealed high heterogeneity in the results precluding our ability to do a meta-analysis. The majority of the included studies were at moderate risk of bias. All included studies failed to provide details of how they trained and monitored data abstractors and if they used standardized abstraction forms and were rated as weak in the domain of data collec- tion. This is a common shortcoming in research when it comes to chart reviews [37].

Our results complement the two [15,16] previously done meta-

analysis on this subject. By limiting included studies to those done in the United States and excluding trials with insufficient data or limited setting, we attempted to decrease heterogeneity. Row et al [15] in- cluded 28 articles including 17 trials from the United States. Of these two studies [21,25] were included in our review while 13 were

excluded due to insufficient data for quality assessment or meta- analysis [38-50]. One study [51] limited their TLP intervention to a mil- itary ED and was excluded from our review due to concerns regarding generalizability of the results. Lastly, one study [52] was excluded as the implemented changes in the ED this study went beyond TLP and this could have skewed the results.

Abdulwahid et al. [16] review included 25 studies with 12 studies from the United States. Of these 12 studies, 6 were included in our re- view while 6 were excluded due to insufficient data [47,53,54], implanting changes other than TLP [36,55], or possibility of overlapping data with another study by the same team [56].

Similar to our meta-analysis, Rowe et al [15] found a decrease in LWBS in almost all studies after implementation of TLP however the results were too heterogeneous to pool the data. We attempted to minimize the heterogeneity by only including studies from the United States and performing a sensitivity analysis based on type of provider in triage and found a decrease in LWBS when attending physicians work as TLP (pooled RR of 0.62, 95% CI 0.54-0.71). This is similar to findings by Abdulwahid et al. [16] who found a RR of 0.56 (95% CI 0.51-0.61) across two United States-based studies using a “senior doc- tor” in triage.

The decrease in LWBS likely arises from the provider being stationed in triage and seeing the patient from an earlier point of entry and reduc- ing the possibility of a patient leaving before physically seeing a pro- vider. Previous studies show that the most common cause of LWBS is being “fed up with waiting” [57]. Implementation of TLP provides an in- teraction with the provider early in the course of the ED stay and can offer some level of reassurance to the patients that their care is being ex- pedited. This is supported by the reduction in LWCA (RR 0.60 95% CI

Table 1

Description of included studies.

Study Design Hospital Setting Intervention

Han et al. 2010

Imperato

et al. 2012

Nestler et al. 2012

Nestler et al. 2014

Partovi et al. 2001

Rogg et al. 2013

Soremekun et al. 2012

Spencer

et al. 2019

Svirsky et al. 2013

Retrospective

One group before and after intervention

Retrospective

One group before and after intervention

Prospective Two groups

control versus intervention

Prospective Observational Before-and-after

Prospective Two groups

control versus intervention days

Retrospective

One group before and after the intervention

Retrospective

One group before and after the intervention

Prospective

One group before and after the intervention

Prospective

two groups control versus intervention

Setting: Urban Academic, Tertiary Care, Level 1 Trauma center

ED volume: 50,000

Admission Rate: NS

Setting: Community teaching hospital

ED volume: 36,000

Admission Rate: 30%

Setting: academic tertiary care hospital,

Level I

trauma center

ED volume: 72,000

Admission Rate: 30%

Providers: Physician assistants

Setting: Academinc Tertiary Care, Level 1 trauma

ED volume: 73,000

Admission Rate 30%

Setting: Urban county-owned university hospital, Level 2 trauma

ED volume: 52,000

Admission Rate: 16%

Setting: Urban tertiary care, Academic Center

ED volume:90,000 Admission rate: 26%

Setting: Urban tertiary care, Academic Center

ED volume: 90,000

Admission rate: 27%

Setting: Level 1 trauma center ED volume: 58,000 Admission Rate: NS

Setting: community-

based, academic, Level III trauma hospital

ED volume: NS

Admission Rate: NS

Providers: Physician

Model: Resource-additive

TLP availability: 7 days a week from 1:00 pm to 9:00 pm

Responsibilities of TLP:

Initiation of diagnostic testing and treatment Discharge of patients if appropriate

Acuity of patients seen by TLP: All Duration of study: 9 weeks Providers: Physician

Model: Resource-additive

TLP availability: 7 days a week from 1 pm to 9 pm

Responsibilities of TLP:

Initiation of diagnostic testing and treatment Discharge of patients if appropriate

Acuity of patients seen by TLP: NS Duration of study: 3 months Providers: PA

Model: Resource-additive

TLP availability: 12:00 PM to 8:00 PM on Mondays and Wednesdays Responsibilities of TLP:

Initiation of diagnostic testing and treatment Acuity of patients seen by TLP: ESI level 3, 4, or 5 Duration of study: 8 days

Providers: PA

Model: Resource-neutral

TLP availability: 4:00 PM to 12:00 AM on weekdays (excluding Mondays) and 12:00 PM to 12:00 AM on weekends

Responsibilities of TLP:

Initiation of diagnostic testing and treatment Acuity of patients seen by TLP: ESI level 3, 4, or 5 Providers: Physician

Model: Resource-additive

TLP availability: 9:00 AM to 9:00 PM on Mondays

Responsibilities of TLP:

Initiation of diagnostic testing and treatment

Evaluating and moving serious patients to patient care areas MSE and discharge of low-acuity patients as needed and at the discretion of the TLP Acuity of patients seen by TLP: Medical level 3, 4, or 5 per hospital’s triage criteria. All trauma level 1,2,3 were excluded

Duration of study: 16 days Providers: Physician Model: Resource-additive

TLP availability: 11:00 a.m. and 11:00 p.m., 7 days per week

Responsibilities of TLP:

Identification of subtle presentations of life threats Initiation of diagnostic testing and treatment

Accelerating the disposition of a subset of medium acuity patients (discharge or direct admission)

Acuity of patients seen by TLP: Medium acuity (those triaged to the fast track or acute area were excluded)

Duration of study: 3 years Providers: Physician Model: Resource-additive

TLP availability: 11:00 AM to 11:00 PM every day

Responsibilities of TLP:

Identification of subtle presentations of life threats Initiation of diagnostic testing and treatment

Accelerating the disposition of a subset of medium acuity patients (discharge or direct admission)

Acuity of patients seen by TLP: Medium acuity

Duration of study: 12 months

Providers: NP or PA

Model: Resource-additive

TLP availability: 10:00 am to 10:00 pm every day

Responsibilities of TLP: NS

Acuity of patients seen by TLP: NS Duration of study: 6 months Providers: Senior residents (PGY2-3) Model: Resource-additive

TLP availability: 12:00 PM-06:00 PM for first half of the study and

2:00 PM-08:00 PM for the second half of the study on Wednesdays and Fridays

Responsibilities of TLP:

Initiation of diagnostic testing and treatment

(continued on next page)

Table 1 (continued)

Study Design Hospital Setting Intervention

Discharge of patients if appropriate (after presenting to the attending physician)

Acuity of patients seen by TLP: All ESIs

Duration of study: 16 days

Traub et al. 2015

Weston

et al. 2017

White et al. 2012

Retrospective two groups control versus intervention

Retrospective

One group before and after intervention

Retrospective

One group before and after intervention

Setting: Community Hospital affiliated with a tertiary care teaching hospital ED volume: 24,500

Admission Rate: 30%

Setting: Urban academic

ED volume: 88,000

Admission Rate: 20% and 15% placed on observation

Setting:Urban Academic

ED volume: 85,000

Admission Rate: 26%

Providers: Physician

Model: Resource-additive

TLP availability: 11:00 AM-08:00 PM on Mondays and Fridays

Responsibilities of TLP:

Initiation of diagnostic testing and treatment Discharge of patients if appropriate

Acuity of patients seen by TLP: No set criteria-patients were chosen at the discretion of the triage team

Duration of study: 24 day

Providers: Attending and senior resident physicians (PGY 3-4)

Model: Resource-additive

TLP availability: 11:30 AM and 07:30 PM Monday through Friday

Responsibilities of TLP:

Reviewing EKGs

Initiation of diagnostic testing and treatment

Accelerating the disposition of a subset of medium acuity patients (discharge or direct admission)

Acuity of patients seen by TLP: ESI 3 Duration of study: 3 months Providers: Physician

Model: Resource-additive

TLP availability: 11:00 a.m.-11:00 p.m. every day

Responsibilities of TLP:

Identification of subtle presentations of life threats Initiation of diagnostic testing and treatment

Accelerating the disposition of a subset of medium acuity patients (discharge or direct admission)

Acuity of patients seen by TLP: Medium acuity (those triaged to the fast track or acute area were excluded)

Duration of study: 3 months

0.57, 0.64) observed in our meta-analysis which is similar to findings by Abdulwahid et al. [16].

With CMS’s publication of this ED metric on their website [58] and financially penalizing hospitals for poor metrics, the significant reduction in LWBS may be a valuable reason to implement TLP in the United States. The reduction in LWBS is linked to increase patient satisfaction [59] and ensures patients are not only seen by a provider, but can potentially mitigate a catastrophic miss. LWBS poses a signif- icant medicolegal, financial, and public relations risk for healthcare systems if the patient has an adverse outcome upon leaving. Despite these benefits, the importance of LWBS requires further studies as current data on LWCA are limited. In addition, LWBS does not ad- dress the original issue of ED overcrowding. Although fewer patients may leave without being seen, it does not address the throughput of patients. The adverse outcomes associated with overcrowding, such

as delay in time to antibiotics and delay in ACS management, likely are minimally affected by TLP if the ED remains just as crowded as before.

Implementation of TLP resulted in reduction of ED-LOS in all except one study [24] however the results were too heterogeneous to pool the data. A subgroup analysis based on disposition, admitted versus discharged from the ED, or type of TLP, physician vs NPP did not de- crease the heterogeneity. This heterogeneity was reported in previous systematic reviews as well. Both Rowe et al [15] and Abdulwahid et al.

[16] could only pool the data across a few studies done outside of the United States.

The heterogeneous impact of TLP on ED-LOS likely results from dif- ferent structures of the operating hospital prior to TLP implementation as well as design of TLP. In one study that used a “resource-neutral” ap- proach [23], where providers were pulled from another area to act as

Fig. 2. Quality assessment of included studies.

Fig. 3. The RR of LWBS.

Fig. 4. RR of LWBS when attending physicians acted as TLP.

Fig. 5. RR of LWBS when residents acted as TLP.

TLP, ED-LOS increased by 20 (95% CI 3.74-36.26) minutes questioning the benefit of implementation of TLP without increasing resources, in- cluding staffing.

Although ED-LOS is theorized to be lower in patients after imple- mentation of TLP because some aspects of their care is expedited, this significant impact was not seen in our study. Several rationales likely

Fig. 6. RR of LWBS when NPPs acted as TLP.

Fig. 7. RR of LWCA when physicians acted as TLP.

Fig. 8. ED-LOS pre and post TLP.

Fig. 9. ED-LOS pre and post TLP among admitted patients.

Fig. 10. ED-LOS pre and post TLP among discharged patients.

Table 2

Summary of pooled effect estimates for main outcomes.

Outcome	Number of studies	Number of patients	Statistical Method	Effect Estimate
LWBS	10	212,793	Risk Ratio (M-H, Random, 95% CI)	Too heterogeneous
LWBS-Attending physicians only	6	146,775	Risk Ratio (M-H, Random, 95% CI)	(I-square 96%) 0.62 [0.54, 0.71]
LWBS-Senior residents only	2	29,137	Risk Ratio (M-H, Random, 95% CI)	Too heterogeneous
LWBS-NPPs	3	56,179	Risk Ratio (M-H, Random, 95% CI)	(I-square 83%) Too heterogeneous
LWCA	2	116,547	Risk Ratio (M-H, Random, 95% CI)	(I-square 92%) 0.60 [0.57, 0.64]
LOS	9	234,763	Mean Difference (IV, Random, 95% CI [minute])	Too heterogeneous (I-square 98%)
LOS- Admitted patients	5	100,441	Mean Difference (IV, Random, 95% CI)	Too heterogeneous (I-square 98%)
LOS- Discharged patients	4	93,236	Mean Difference (IV, Random, 95% CI)	Too heterogeneous (I-square 98%)

explain this discrepancy between theory and practice. Most importantly, the ED-LOS for a patient is a metric affected by multiple variables. Several studies have described the impact of multiple patient-centered and environment-centered factors that affect ED-LOS. Patient’s characteris- tics, time of the day and day of the week, need for laboratory work, im- aging, and consults, the occupancy rate of the hospital, the acuity mix of the patients in the ED, nurse and physician staffing, provider hand- offs, number of admission in that shift can all affect ED-LOS [60-64]. Al- though expediting some aspects of a patient’s care may appear to be a reasonable fix, this needs to be contextualized into environment the pa- tient is treated, which may explain the lack of impact of TLP in ED-LOS.

A key factor that explains why ED-LOS may also remain unchanged is that TLP impacts mostly ambulatory patients and patients with me- dium acuity. Patients with higher acuity were excluded in most of the TLP studies. Their care is prioritized and having sicker patients in the ED negatively affects the LOS of less acute patients because these pa- tients are resource intensive [60]. In addition, different providers may have different clinical assessments of their patients, which cause a new workup to be performed, with additional lab testing and imaging being ordered. This negates a potential benefit from the TLP. A change in the position’s status from entry to assessment by the ED provider and the need for reassessment also may contribute towards an un- changed ED-LOS. The impact of TLP on ED-LOS and throughput remains limited and alternative methods may need to be employed to improve this key CMS and patient-centered metric. CMS’ focus on improving throughput metrics is all-in-all short-sighted and is an attempt to treat numbers and not the patients. Targeting metrics that do not affect the quality and efficiency of care that patients receive in the emergency department does not benefit the patients nor the hospital. Tying pay- ments to the above metrics incentivizes hospitals to focus on metrics that will ultimately harm patients. Hospital systems should aim to im- prove their efficiency and improve ED overcrowding and will need to carefully consider the method they employ to improve patient care.

Limitations

Our study can only be interpreted after acknowledging several limi- tations to our analysis. First, the heterogeneity of the data limited our ability to perform meta-analysis for every outcome variable. In addition, ED-LOS, LWBS, and LWCA were the most commonly reported metrics and other metrics such as door-to-doctor, costs, were commonly miss- ing and therefore we cannot comment on the effect of TLP on these im- portant metric. Fortunately, two of the metrics analyzed, LWBS and ED- LOS, best reflect CMS and patient-oriented metrics and may be used as a surrogate.

Conclusions

Implementation of TLP reduces LWBS when attending physicians act as TLP however more studies are needed to clarify the role of resident physicians or NPPs as TLPs. Reducing LWBS may be the primary source of value to hospitals to help mitigate risk and avoid CMS financial punishment.

Although TLP was originally intended to improve ED throughput and decrease overcrowding, its actual impact on these performance mea- sures does not appear to hold up in practice. Changing the ED-LOS is an arduous task wrought with numerous interconnected variables.

Prior presentations

None.

Declaration of competing interest

None.

Acknowledgments

The authors would like to thank Mr. Christopher Stewart, senior li- brarian at State University of New York Downstate Medical Center, for his help in formulating the literature search strategy.

Appendix A

PUBMED

(“triage”[MeSH Terms] OR “triage”[All Fields]) AND (“length of stay”[MeSH Terms] OR “length of stay”[All Fields]) AND (“walkout”[All Fields] OR “leave”[All Fields] OR “left”[All Fields])

EMBASE

(‘length of stay’/exp OR ‘length of stay’) AND (‘triage’/exp OR triage) AND

(leave OR left OR walkout)

(triage AND length of stay AND left without being seen)

Web of science

(triage AND length of stay AND left without being seen)

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