Continuous Quality Improvement Program, Based on Lean Concepts, Allows Emptying of Emergency Department Corridors

December 16, 2015
Enrique Casalino, MD, PhD

Christophe Choquet, MD

Mathias Wargon, MD, PhD

Romain Hellmann, MD

Michel Ranaivoson, MD

Luisa Colosi, MD

Gaëlle Juillien, MD

Julien Bernard, MD

The American Journal of Accountable Care, December 2015, Volume 3, Issue 4

A continuous quality improvement program, based on Lean concepts and including architectural, managerial, and organizational features, allows the emptying of emergency department corridors and the improvement of time interval measurements/quality indicators.


Objectives: To assess the impact on an emergency department (ED) and the sustainability of a multifaceted continuous quality improvement (CQI) program based on Lean concepts.

Study Design: A prospective interventional 6-year study was conducted.

Methods: Interventions included managerial, organizational, and architectural features. Evaluation was conducted using pre-/post intervention ANOVA com­parisons and interrupted time series (ITS) analysis.

Results: We analyzed 413,392 attendances. During the study period, significant (P <.001) trends were found for decreased length of stay (LOS) (—191 minutes; 95% CI, –280 to –101), waiting time before seeing an ED provider (–142 minutes; 95% CI, –225 to –66), and for increased percentage of patients leaving the ED in less than 4 hours (+24%; 95% CI, 12%-38%). ITS analysis demonstrated that the initial quality improvement step and having an ED medical coordinator had a significant impact on LOS, whereas the policy of having 0 patients in ED corri&shy;dors led to significant improvements in LOS in the percentage of patients leaving the ED in less than 4 hours, and in the waiting time before seeing a medical provider. Fast-track pathways were implemented for low- and very low–com&shy;plexity patients.

Conclusions: Our results indicate that a CQI program based on Lean concepts—including successive interventions and a “fast track” for low- and very low—complexity patients—allows for the emptying of ED corridors and the enhancement of time-intervals measurements/quality indicators.

Emergency department (ED) overcrowding has a negative impact on the quality of care and on patient and staff satisfaction.1,2 Operational performance measures have been suggested and various time intervals have been proposed as quality and performance indicators, or crowding markers, in the ED. Longer ED length of stay (LOS) has been associated with a greater risk of death in the short term.3,4 The percentage of patients leaving the ED in less than 4 hours,5 the time spent waiting to see a medical provider,6 and the time spent waiting for a triage nurse7 have all been proposed as time-interval measure&shy;ment/quality indicators (TIMQIs).

Various strategies to improve ED patient-flow management, performance, and quality of care have been proposed.8,9 Continu&shy;ous quality improvement (CQI) is probably the most established approach,10 with “Lean thinking”— first implemented in man&shy;ufacturing, later extended to healthcare organizations including EDs11—as one approach to CQI. Various other tools have been proposed to improve the quality of the EDs,12 including fast-track13 triage area organization,14 reorganization of patient flow, and clinical information system and process redesign. However, evaluations of their success were frequently carried out by using short-term pre-/post intervention measurements.15 The long-term impact of interventions is difficult to evaluate in quality im&shy;provement evaluations. However, a central idea in improvement is to make changes incrementally, by learning from experience.16 We have found very few studies that have reported a continuous evaluation over time.17

The aim of this study was to measure the impact of the re&shy;organization of an ED based on CQI and Lean thinking on TIMQIs. Thus, we used pre- and post intervention comparisons for short-term evaluation. To evaluate the long-term impact of successive interventions, we used interrupted time series (ITS) analysis, assessment technique, developed to analyze work flows and processes within an organization (reengineering).18


Patients and Data Collection

This anonymous data set did not contain any identifiable person&shy;al health information, and it is currently used as an ED quality and performance measurement as part of an ongoing emergen&shy;cy activity and performance evaluation. The study was approved by the emergency ethical committee of the Assistance Pub&shy;lique-Hôpitaux de Paris.

Study Design

We report a CQI project conducted over a 6-year period. Specific interventions were introduced in a sequential manner during the study period. To evaluate the impact of each specific interven&shy;tion, we conducted a prospective pre-/post intervention 1-way analysis of variance (ANOVA),19 comparing the means of several quality and performance indicators from the 28 days before to 42 days after each intervention.

An ITS analysis was also conducted, which included monthly average values for the performance indicators as a way to eval&shy;uate each specific intervention in the overall CQI project. The time series included 72 measured points from January 2006 to December 2011, which spanned a period prior to CQI imple&shy;mentation to 24 months after the last intervention.20


The study was carried out in an academic hospital (Bichat-Claude Bernard [BCB]) located in the Paris metropolitan area. Over the course of the study, there were no significant changes in the number or composition of the medical staff (trainees, residents, or staff emergency physicians), in the paramedical staff, in the hospital policies on admissions, or in access to radiology or lab&shy;oratories. ED renovation had already been planned, including a temporary move to a provisional structure, and finally, into our permanent structure.

Interventions Description

Each of the interventions included managerial, organizational, and architectural modifications. Briefly, they included: a) initial quality improvement step: the creation of a CQI team project that includes a change of culture promoting performance and quality; b) implementation of an ED medical coordinator re&shy;sponsible for the good functioning of the ward through lead&shy;ership and formal authority; c) a triage nurse who streams the patients into 2 different sectors according to their acuity level; d) the move to a provisional ED structure (ED area reduced by 20%) with a new policy of 0 patients in the ED corridors; e) the move to our permanent ED structure (40% larger than original ED area) with all sectors on 1 unique floor, meaning personnel can be deployed according to patient flow in real time; and f) a dedicated physician for fast-tracking low-complexity patients.

Details of each intervention are shown in Table 1. We did not evaluate intervention “a”—the initial quality improvement step as a pre-/post intervention)—but it was integrated into the ITS model because it represents the first modification to the culture of the service and accompanied the first organizational modifi&shy;cations to our ED.


Daily and monthly values for time-interval measurements were extracted automatically in real time from the electronic med&shy;ical and hospital database records. All visits to the BCB’s ED during the study period were included. The following variables were studied: number of daily and monthly visits; acuity level; patient’s final outcome (ie, not admitted, admitted in our obser&shy;vation ward, admitted to another ward, or transferred to another hospital). Acuity was measured according to the Canadian Triage scale from 1 to 5 (1 = high acuity or complexity, 5 = low acuity or complexity).

Time intervals (minutes) were calculated automatically on a daily and monthly basis and were defined as follows: a) LOS— minutes between the time the patient was identified in the ED and the time the patient left the ED; b) percentage of patients leaving in less than 4 hours—calculated from the LOS for each visit; c) waiting time to triage nurse—minutes between the time the patient was identified in the ED and the time the patient was seen by the triage nurse; d) waiting time to ED provider—min&shy;utes between the time the patient was identified in the ED and the time the patient was first treated by the ED provider. The final decision of the ED provider is registered by the ED com&shy;puter program.

Statistical Analysis

The pre-/post intervention analysis compares pre- and post in&shy;tervention means of daily time-interval measurements (28 days be&shy;fore and 42 days after each intervention) by using 1-way ANOVA.19

ITS analysis at multiple time points before and after interven&shy;tions were conducted in order to detect whether or not the dif&shy;ferent interventions had a significantly greater effect than any underlying trend.20-22 Autoregressive integrated moving average (ARIMA) models, using Box-Jenkins methodology,20 were used to evaluate whether relationships existed between time intervals (LOS, percentage leaving ED in under 4 hours, wait time to see provider) and the different interventions introduced. Time be&shy;tween interventions were longer than 3 months (with 3 measures points) to guarantee a stable mean, and moving averages were calculated if necessary to correct trend.20-22

The model was identified by determining the ARIMA model orders (p, d, q) using auto-correlation and partial auto-correla&shy;tion. Finally, the adequacy of the model was checked and statisti&shy;cal significance of the parameters was determined. The best type of impact was evaluated as previously described.20,21 The gradual permanent impact was used; gradual permanent pattern includes 2 parameters estimated from the ITS analysis, which allows us to evaluate the level of the disturbance event and its duration. Omega estimates the difference between the data series before and after the intervention; delta indicates the duration of any change and the rate of recovery if present. Then, omega (ω) indicates the overall shift where the sign represents the direction of the change, and delta (δ) represents the speed at which a grad&shy;ual increase or decay of these initial changes occurred over time. Both parameters should be statistically significant so as not end up with paradoxical conclusions.21 Omega and delta asymptotic parameters ± SD values are presented as well as their 95% CIs.

Statistica software (Statsoft, Tulsa, Oklahoma) was used for analysis. We deemed statistical differences to be significant for P <.05.


Characteristics of the Study Subjects

During the 6-year study period, 413,392 attendances were reg&shy;istered in the ED. Monthly ED attendances increased over the study period from 4540 ± 214 in 2005 to 5928 ± 299 in 2011 (+31%; 95% CI, 20%-42%; P <.001) (Figure). No significant change in admission rate (20.3%), transfer to intensive care (3.2% of the entire population), or patients’ typology (medicine [60%], surgery [36%], psychiatric [4%]; patients ≥75 years [10.5%]) was found during the study period.

Main Results

The trends for main TIMQIs and monthly number of visits are presented. The chronology of the different interventions studied is indicated as well.

During the 6-year study period, findings included decreases in trends for LOS (—191 minutes; 95% CI, –280 to –101; P <.001) and waiting time to provider (—142 minutes; 95% CI, –225 to –66; P <.001), and a significant increase in the trend for the per&shy;centage of patients leaving the ED in less than 4 hours (+24%; 95% CI, 12%-38%; P <.001).

Pre- and Post Intervention Analysis

Table 2 shows the results of ANOVA comparisons for the mea&shy;sured time intervals in the pre-/post intervention analysis. Hav&shy;ing an ED medical coordinator significantly reduced ED LOS, notably for high-acuity patients The interventions of having a dedicated physician for fast-tracking what is considered to be low-complexity also significantly improved the percentage of pa&shy;tients leaving the ED in less than 4 hours.

Interrupted Time Series Analysis

If we look at the progress of the measurements of the different time intervals in the Figure and Table 2, we can see improvements between post intervention measures and the following pre-inter&shy;vention, ie continuous improvement unrelated to interventions. Thus, another evaluation method was necessary to evaluate the impact of interventions over the study period. Table 3 shows the results of ITS models for ED TIMQIs.

We found that the interventions of an initial quality improve&shy;ment step and having an ED medical coordinator had a signifi&shy;cant impact on ED LOS, and that the interventions of moving to ED provisional structure and having 0 patients in the ED corri&shy;dors had a significant impact on the percentage of patients leav&shy;ing ED in less than 4 hours and on wait time to see the provider.


We report on a CQI program over a 6-year study period that included multiple planned managerial, organizational, and archi&shy;tectural interventions. Our study indicates that pre-/post inter&shy;vention studies offer a short-time evaluation (a 6-week period), whereas ITS analyses offer evaluations of a longer period. ITS analyses were desirable because flow and processes were contin&shy;uously evaluated and subsequently modified.

In the pre-/post intervention analysis, we measured ED LOS and wait time to see the provider according to acuity level, as well as the wait time to see triage nurse. Analysis of these mea&shy;surements helped to define the ED sectors or care pathways in which the various interventions might have had a quick impact. Thus, the intervention of having an ED medical coordinator al&shy;lowed for a significant ED LOS reduction, seemingly explained by the reduction of ED LOS for acuity levels 1 and 2—the most complex patients. In contrast, ED LOS improvement after the move to provisional structure was related to the reduction of ED LOS for acuity levels 4 and 5, showing the importance of the dedicated structure to the care of very low—complexity patients. Similarly, we found significant improvements in wait time to see the provider after the interventions of having an ED medical coordinator and moving to provisional structure. The first is ex&shy;plained by a reduction in the wait time to see the provider for the most complex patients, while the second is related to a more global improvement, but was much higher in patients of low and very low complexity, which indicates the importance of both spe&shy;cific clinical pathways.

Additionally, we found that the percentage of patients leaving the ED in under 4 hours was improved later, while ED LOS en&shy;hancement occurred earlier. These data suggest that both quality and performance indicators probably evaluate different process&shy;es in ED organization, and that both should be measured after any intervention. We did not find a significant reduction in wait time to see triage nurse after any intervention, except for having an ED medical coordinator; nevertheless, we saw a steady im&shy;provement in this measure between interventions. We consider that, while wait time to see the ED provider is dependent on the dynamics of medical teams, the wait time to see the triage nurse depends on the nurse’s team.

Our new policy of having 0 patients in the ED corridors, which occurred with the move to the provisional structure, had significant impacts on different time intervals despite the smaller space. Certain features can explain the measurements associated with this intervention. First, the provisional ED structure had a medical bay in the triage area, which did not exist in the old&shy;er premises. This bay was used to manage very low—complexity (acuity level 5) patients, serving as a fast track for these patients. The impact of this intervention, therefore, appears to be linked to the acceleration in treatment of very low–complexity patients with an overall impact on ED main TIMQIs. Contemporary with the move, the ED staff was working on correcting patient neglect in the ED as defined by the French Health Authority.23 We found that this occurred frequently, with patients in the corridors being one of its main indications.

Organizational improvements must be instituted to eliminate patient neglect in the ED. We considered combatting patient ne&shy;glect in the ED and having the goal of there being 0 patients in the ED corridor as complementary and a reachable strategy and objective. The ED team stuck to this objective, which also valo&shy;rized their function and was inspired by patient-centered care.24 It has been suggested that an increase in the percentage of pa&shy;tients leaving the ED in less than 4 hours leads to there being 0 patients in the corridors of some British EDs.5,25 In our study, the 0 patients in the ED corridor objective led to an increase in the percentage of patients leaving the ED in under 4 hours, and a reduction in the waiting time before seeing a medical provider.

In fact, the reality of patients being in the corridors at several points during their stay in the ED indicated small defects in the organization of care. We were able to fix some with specific pro&shy;cesses, such as direct patient’s installation in beds or cubicles. In other cases, though, no processes were specified, yet it appears that providers took on the goal of having 0 patients in the ED corridor and figured out ways to avoid that scenario. From our experience, it was the ability to share the value we placed on hav&shy;ing 0 patients in the ED corridor that permitted the reorganiza&shy;tion of the patient processes. This was achieved by the ED team without a specified intervention of the CQI team. We believe this is close to models of “learning organization” or “knowledge management,”26 and a culture of improvement,27,28 that have been initiated early in the CQI program.

Permanent ED structures have more spacious premises and a total number of beds/cubicles. The absence of significant im&shy;pact of the intervention (to move to a permanent ED structure) is concordant with previously published studies which have re&shy;ported29,30 that the number of beds in an ED has no significant impact on ED LOS. We believe that organizational and process management features are more important than the number of beds.

The ITS analyses allowed us to evaluate the complex relation&shy;ships among the different interventions and, thus, define their impact and sustainability. In the present study, we identified “permanent gradual” as the best impact type: the increase or de&shy;crease due to the intervention is gradual, and the final permanent impact becomes evident only after some time. By analyzing the trend line for the time period after an intervention, we noticed a significant difference on the trend line for the time period leading up to the intervention. Furthermore, the ITS analysis offers a new approach to evaluate the cumulative effect of specific inter&shy;ventions and to evaluate the strength of each one in an overall CQI program.

In our model, significant values for the 2 parameters (delta and omega) indicates a gradual and permanent evolution of the stud&shy;ied parameter. ED LOS model found significant values follow&shy;ing interventions: initial quality improvement step, having an ED medical coordinator. The percentage of patients leaving the ED in less than 4 hours and wait time to see medical provider models show that only ED move to the provisional structure of having 0 patient in the ED corridors, as omega and delta have significant values. Even if some interventions were not associated with sig&shy;nificant level and slope changes, we think that our study indicates the importance of the cumulative value of successive interven&shy;tions within the framework of continuous quality improvement policy, rather than the individual results of every intervention.

Our results suggest that the progressive establishment of spe&shy;cific care pathways, often called fast tracks, for low- (acuity level 4) and very low- (acuity level 5) complexity patients, was a deter&shy;mining factor in the overall strategy of quality and performance improvement, which confirms previous publications.13,31 This es&shy;tablishment began with the triage nurse who streams the patients into 2 different sectors; the availability of a bay in the triage area; the move to our permanent ED structure that included stream&shy;ing the patients into 3 pods; and finally with the assignment of a dedicated physician for fast-tracking low-complexity patients.1


This study has several limitations. First, even though we analyzed some successive interventions and described a number of these, other factors probably exist that can influence ED quality indica&shy;tors. Second, because there is no single consensus on ED quality indicators, we measured ED time intervals, which are currently the most recommended performance measurement.32 Third, we did not include parameters that could affect the performance of an ED, such as ED or hospital census variables. Finally, the abili&shy;ty to replicate the interventions in other EDs is unknown.


Our study demonstrates that ED performance and quality, eval&shy;uated by using time-interval measurements, may be improved by a CQI program based on Lean thinking. Our results confirm the importance of CQI and Lean thinking as methods of analyzing processes, management, and redesign of EDs. The study indi&shy;cates the importance of complex and successive interventions including managerial, organizational, and architectural features. Early and progressive changes in the process and organization may have driven subsequent improvements. The establishment of a fast track for low- and very low—complexity patients, as well as a culture of change and improvement among the ED staff, appear to us to be very significant. Furthermore, we showed that before-and-after comparison studies are insufficient to evaluate the effect of multiple complex interventions over long time peri&shy;ods, and that ITS analysis is a relevant approach method.

Author Affiliations: Assistance Publique-Hôpitaux de Paris, Uni&shy;versity Hospital Bichat-Claude Bernard, Emergency Department (EC, CC, MW, RH, MR, LC, GJ, JB), Paris, France; Université Paris Diderot, Sorbonne Paris Cité (EC, JB), Paris, France; Study Group for Efficiency and Quality of Emergency Depart&shy;ments and Non-Scheduled Activities Departments (EC, CC, MW, RH, MR, LC, GJ, JB), Paris, France.

Funding Source: None.

Author Disclosures: The authors report no relationship or financial interest with any entity that would pose a conflict of interest with the subject matter of this article.

Authorship Information: Concept and design (EC, CC, MW); ac&shy;quisition of data (EC, CC, MW, RH, MR, LC, GJ, JB); analysis and interpretation of data (EC, CC, MW, RH, MR, LC, GJ, JB); drafting of the manuscript (EC, CC, MW, RH, MR, LC, GJ, JB); critical revision of the manuscript for important intellectual con&shy;tent (EC, CC, MW, RH, MR, LC, GJ, JB); statistical analysis (EC, CC, MW); provision of study materials or patients (EC, CC, MW, RH, MR, LC, GJ, JB); obtaining funding (not applicable); admin&shy;istrative, technical, or logistic support (EC, CC, MW, RH, MR, LC, GJ, JB); and supervision (EC, CC, MW).

Address correspondence to: Pr. Enrique Casalino, Service d’Accueil des Urgences, Hôpital Bichat-Claude Bernard, 46 rue Henri Hu&shy;chard, 75018 Paris, France. E-mail:


1. Hwang U, McCarthy ML, Aronsky D, et al. Measures of crowding in the emergen&shy;cy department: a systematic review. Acad Emerg Med. 2011;18(5):527-538.

2. Hoot NR, Epstein SK, Allen TL, et al. Forecasting emergency department crowd&shy;ing: an external, multicenter evaluation. Ann Emerg Med. 2009;54(4):514-522.e19.

3. Welch SJ, Asplin BR, Stone-Griffith S, Davidson SJ, Augustine J, Schuur J; Emer&shy;gency Department Benchmarking Alliance. Emergency department operational metrics, measures and definitions: results of the Second Performance Measures and Benchmarking Summit. Ann Emerg Med. 2011;58(1):33-40.

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

5. Mason S, Weber EJ, Coster J, Freeman J, Locker T. Time patients spend in the emergency department: England’s 4-hour rule--a case of hitting the target but miss&shy;ing the point? Ann Emerg Med. 2012;59(5):341-349.

6. Access block and overcrowding in emergency departments. Australasian Col&shy;lege for Emergency Medicine website.;ment/56688d18-4f4c-467a-bba3-704d994d9f2d/Access-Block-2004-literature-re&shy;view.aspx Accessed November 20, 2015.

7. Casalino E, Choquet C, Bernard J, et al. Predictive variables of an emergency department quality and performance indicator: a 1-year prospective, observational, cohort study evaluating hospital and emergency census variables and emergency department time interval measurements. Emerg Med J. 2013;30(8):638-645.

8. Kellerman AL. Crisis in the emergency department. N Engl J Med. 2006;355(13):1300-1303.

9. Pines JM, McCarthy ML. Executive summary: interventions to improve quality in the crowded emergency department. Acad Emerg Med. 2011;18(12):1229-1233.

10. Duckett S, Nijssen-Jordan C. Using quality improvement methods at the system level to improve hospital emergency department treatment times. Qual Manag Health Care. 2012;21(1):29-33.

11. Holden RJ. Lean Thinking in emergency departments: a critical review. Ann Emerg Med. 2011;57(3):265-278.

12. Urgent Matters toolkit. George Washington University School of Medicine & Health Sciences website. Accessed November 20, 2015.

13. Considine J, Kropman M, Kelly E, Winter C. Effect of emergency department fast track on emergency department length of stay: a case-control study. Emerg Med J. 2008;25(12):815-819.

14. White BA, Brown DF, Sinclair J, et al. Supplemented Triage and Rapid Treat&shy;ment (START) improves performance measures in the emergency department. J Emerg Med. 2012;42(3):322-328.

15. Baumlin KM, Shapiro JS, Weiner C, Gottlieb B, Chawla N, Richardson LD. Clinical information system and process redesign improves emergency department efficiency. Jt Comm J Qual Patient Saf. 2010;36(4):179-185.

16. Kelly JJ, Thallner E, Broida RI, et al. Emergency medicine quality improvement and patient safety curriculum. Acad Emerg Med. 2010;17(suppl 2):e110-e129.

17. Kyriacou DN, Ricketts V, Dyne PL, McCollough MD, Talan DA. A 5-year time study analysis of emergency department patient care efficiency. Ann Emerg Med. 1999;34(3):326-335.

18. Garcia Sullivan P. “No Pain, No Gain”: Improving Hospital Performance Through Re&shy;engineering. New York, NY: New York University Robert F. Wagner Graduate School of Public Service; 2003.

19. Bewick V, Cheek L, Ball J. Statistics review 9: one-way analysis of variance. Crit Care. 2004;8(2):130-136.

20. Interrupted time series analyses. Cochrane Effective Practice and Organisation of Care (EPOC) Group website. org/files/uploads/21%20Interrupted%20time%20series%20analyses%202013%20 08%2012_2.pdf. Published 2013. Accessed August 31, 2014.

21. McDowall D, McCleary R, Meidinger EE, Hay RA Jr. Interrupted Time Series Anal&shy;ysis. Thousand Oaks, CA: Sage Publications, Inc; 1980.

22. Ramsay CR, Matowe L, Grilli R, Grimshaw JM, Thomas RE. Interrupted time series designs in health technology assessment: lessons from two systematic reviews of behavior change strategies. Int J Technol Assess Health Care. 2003;19(4):613-623.

23. Compagnon C, Ghadi V. La maltraitance « ordinaire » dans les établissements de santé [The «ordinary» abuse in health facilities]. Haute Autorité de Santé web&shy;site.;port_ghadi_compagnon_2009.pdf. Published 2009. Accessed November 20, 2015.

24. Bardes CL. Defining “patient-centered medicine.” N Engl J Med. 2012;366(9):782- 783.

25. Weber EJ, Mason S, Carter A, Hew RL. Emptying the corridors of shame: orga&shy;nizational lessons from England’s 4-hour emergency throughput target. Ann Emerg Med. 2011;57(2):79-88.e1.

26. Kothari A, Hovanec N, Hastie R, Sibbald S. Lessons from the business sector for successful knowledge management in health care: a systematic review. BMC Health Serv Res. 2011;11:173.

27. Clark DM, Silvester K, Knowles S. Lean management systems: creating a culture of continuous quality improvement. J Clin Pathol. 2013;66(8):638-643.

28. Ramirez B, West DJ, Costell MM. Development of a culture of sustainability in health care organizations. J Health Organ Manag. 2013;27(5):665-672.

29. Khare RK, Powell ES, Reinhardt G, Lucenti M. Adding more beds to the emergency department or reducing admitted patient boarding times: which has a more significant influence on emergency department congestion? Ann Emerg Med. 2009;53(5):575-585.

30. Han JH, Zhou C, France DJ, et al. The effect of emergency department expan&shy;sion on emergency department overcrowding. Acad Emerg Med. 2007;14(4):338-343.

31. Oredsson S, Jonsson H, Rognes J, et al. A systematic review of triage-related interventions to improve patient flow in emergency departments. Scand J Trauma Resusc Emerg Med. 2011;19:43.

32. Sørup CM, Jacobsen P, Forberg JL. Evaluation of emergency department per&shy;formance—a systematic review on recommended performance and quality-in-care measures. Scand J Trauma Resusc Emerg Med. 2013;21:62.