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Increased door to admission time is associated with prolonged throughput for ED patients discharged home
American Journal of Emergency Medicine 34 (2016) 1783–1787
Contents lists available at ScienceDirect
American Journal of Emergency Medicine journal homepage: www.elsevier.com/locate/ajem
Original Contribution
Increased door to admission time is associated with prolonged throughput for ED patients discharged home☆ Scott P. Krall, MD, MBA a, Jose Guardiola, PhD b, Peter B. Richman, MD, MBA a,⁎ a b
Department of Emergency Medicine, Texas A&M Health Science Center/Christus Spohn, Corpus Christi, TX Department of Mathematics, Texas A&M University-Corpus Christi, Corpus Christi, TX
a r t i c l e
i n f o
Article history: Received 17 January 2016 Received in revised form 9 May 2016 Accepted 1 June 2016
a b s t r a c t Background: Emergency Department (ED) service evaluations are typically based on surveys of discharged patients. Physicians/administrators benefit from data that quantifies system-based factors that adversely impact the experience of those who represent the survey cohort. Objective: While investigators have established that admitted patient boarding impacts overall ED throughput times, we sought to specifically quantify the relationship between throughput times for patients admitted (EDLOS) versus discharged home from the ED (DCLOS). Methods: We performed a prospective analysis of consecutive patient encounters at an inner-city ED. Variables collected: median daily DCLOS for ED patients, ED daily census, left without being seen (LWBS), median door to doctor, median room to doctor, and daily number admitted. Admitted patients divided into 2 groups based on daily median EDLOS for admits (b 6 hours, ≥6 hours). Continuous variables analyzed by t-tests. Multivariate regression utilized to identify independent effects of the co-variants on median daily DCLOS. Results: We analyzed 24,127 patient visits. ED patient DCLOS was longer for patients seen on days with prolonged EDLOS (193.7 minutes, 95%CI 186.7–200.7 vs. 152.8, 144.9–160.5, Pb .0001). Variables that were associated with increased daily median EDLOS for admits included: daily admits (P= 0.01), room to doctor time (Pb .01), number of patients that left without being seen (Pb .01). When controlling for the covariate daily census, differences in DCLOS remained significant for the ≥6 hours group (189.4 minutes, 95%CI 184.1–194.7 vs. 164.8, 155.7–173.9 (Pb .0001). Conclusion: Prolonged ED stays for admitted patients were associated with prolonged throughput times for patients discharged home from the ED. © 2016 Elsevier Inc. All rights reserved.
1. Introduction The emergency department has become the entry portal for the hospital as well as the final safety net for all patients in the United States healthcare system. As a consequence of these roles, EDs are becoming increasingly crowded (or overcrowded) with adverse consequences. McCusker linked administrative databases of over 670,000 patients and found that a 10% increase in emergency bed relative occupancy ratio was associated with a 3% increase of the following 30-day outcomes: deaths (for admitted and discharged patients), one or more return ED visits (among discharged patients), and hospital admission at first return visit with a “strong correlation between bed crowding and mortality among large emergency departments” [1]. Numerous other studies have shown similar negative effects on patient outcomes [2–10].
☆ Financial Disclosures/Conflicts: None. ⁎ Corresponding author at: Department of Emergency Medicine, Texas A&M Health Science Center/Christus Spohn, Corpus Christi, TX 78405. Tel.: +1 361 902 6762. E-mail address: [email protected] (P.B. Richman). http://dx.doi.org/10.1016/j.ajem.2016.06.002 0735-6757/© 2016 Elsevier Inc. All rights reserved.
In response to these concerns, the Institute of Medicine has recommended that hospitals reduce ED overcrowding and utilize tools such as queuing theory on admission process and 23 hour ED observation units for improving hospital efficiency [11]. The Centers for Medicare and Medicaid Services (CMS) and The Joint Commission (TJC) have begun to regulate the process with the following performance measure–TJC element LD.04.03.11 that states “the hospital measures and sets goals for mitigating and managing the boarding (the practice of holding patients in the ED or a temporary location for four or more hours after the decision to admit or transfer has been made) of patients who come through the emergency department” [12]. Researchers studying the area of ED patient flow have demonstrated that a variety of internal processes, external factors and the ED department size can impact the ED throughput [13–45]. Such studies of internal inputs have addressed department process changes, including the use of “fast track” areas and redesigning the location of nursing staff and physicians throughout the ED [21–28]. Investigators have also evaluated the effect of optimizing laboratory efficiencies including point of care testing as a means to reduce emergency department length of stay [29–34]. The other major factors in ED flow are hospital occupancy
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and boarding of in-patients in the ED, and, as previously noted, these have been shown to have an adverse impact on emergency department length of stay and outcomes for patients who are ultimately admitted to the hospital [16,17,20,35–39]. The ED is a complex system due to the uncontrolled nature of inputs. Patient factors that influence ED flow include variation in acuity, types of chief complaints and unpredictable surges in patient volume throughout a given day. Flow is also significantly influenced by hospital-based factors outside the direct domain of ED control. As customer service surveys for ED patients focus on patients who are discharged home, it is also important to understand the impact of patient boarding on flow for this large segment of the ED census. We conducted a prospective, observational study to test the hypothesis that extended EDLOS for admitted patients (boarding) would be associated with increased length of stay for those patients ultimately discharged from the ED (DCLOS), and quantify that effect in our institution. 2. Methods 2.1. Study design We performed a prospective, observational study to assess the relationship between throughput of patients discharged from the ED and boarding times for patients admitted to the hospital through the ED. 2.2. Study setting We conducted this study at an urban county hospital facility. The hospital is a designated level-2 trauma center by the American College of Surgeons covering a 12-county region. Physicians at our facility see 48,000 ED patients annually, with an admission rate of 17.5%. Twentyfive percent of those admissions are admitted to the intensive care units. Physician staffing in the main part of the ED consists of an attending physician and two emergency medicine residents (of variable levels of training) for a 24-hour period, an additional fast track area staffed 20 hours by an advanced practice practitioner. The radiology department is located directly behind the ED and is equipped with CT, ultrasound, as well as MRI capabilities. The Christus Spohn Institutional Review Board designated our study as exempt status prior to the initiation of data collection. 2.3. Population Consecutive patient encounters during the period from March 2013 to November 2013 were identified through query of our electronic medical record (Meditech Information Technologies, Inc, Westwood, MA). 2.4. Study process The ED had an evaluation process during the study period that is typical of most EDs. All walk-in patients checked in at the triage window, which was staffed by an ED technician. The patient signed in on a triage complaint form with their name, time of arrival, and chief complaint (time of arrival started the ED intervals). Then, a registered nurse evaluated the patient. A vast majority of the patients received an evaluation using the Emergency Severity Index (ESI) index based on the stability of the patient as well as need for evaluation [40]. If their complaint had potential severity, and/or if a room were available, the patient would be immediately assigned to an ED room. If no rooms were available, the triage nurse asked the patient to wait in the lobby until a room was available for further evaluation. Most patients that arrived by ambulance were immediately taken to a room for evaluation (in that situation, time of nurses initial triage started the ED intervals). The emergency physician, or advanced practice practitioner then assessed the patient to determine if further evaluation or consultation was necessary. The emergency physician determined if the patient needed to be admitted
to the hospital for further treatment (EDLOS for admits), or discharged home with discharge instructions for care at home (DCLOS). 2.5. Method of measurement Median daily time interval data was collected from the ED tracking system, Meditech©. This is a dynamic tracking system that requires the staff to time mark the following events: when the patient received a room (room), when the physician assessed the patient (doctor), and when the patient was discharged from the ED (dismiss). The variables collected included classic ED measures as defined by Welch et al [41] (Table 1). We chose those variables as they represented the ED measures with separate event points entered for every visit, and represented distinct ED measures in the flow of a patient through the ED. 2.6. Primary data analysis The data analysis includes summary statistics for the intervals analyzed. The interval data was divided into 2 groups based on the median daily EDLOS for admits. 6 hours was chosen as the dividing point to reflect 2 hours for the physician to make a decision to admit the patient and the additional 4 hours from the TJC element LD.04.03.11 for maximum acceptable hold interval in the emergency department. Mean intervals of the 2 groups, divided based on the daily median interval EDLOS for admits, were compared by t-tests. Subsequently, we utilized regression analysis to control for confounding variables. Our multiple regression model contained the following variables: DCLOS (dependent), EDLOS for admit group (b6 hours, N = 6 hours), daily census, LWBS (count), median daily room to doctor, median daily door to MD, and number of admissions. The number of admissions and median door to MD variables that were highly correlated were excluded to avoid multicollinearity. 3. Results Data were collated from 24,127 patient visits. There was an admission rate of 17.3% during the study period. Patient characteristics are presented in Table 2. Our emergency department has visits primarily by adults with only 2.66% of the census comprised of pediatric patients. The emergency severity index (ESI) level 3 and 4 categorization of patients was similar, but 21.36% of the visits did not receive the ESI designation in the electronic medical record. The department has a high
Table 1 Emergency department (ED) metrics and definitions [36]. ED Interval
Meditech Interval
Definitions
Room to doctor (doctor includes physician, resident or allied health professional) ED LOS for discharged patients (DCLOS)
Room to provider
Time it takes for a physician to see the patient in the room after the patient is placed in the room
Received to dismiss
The time interval in minutes between arrival time to physical discharge time The time interval in minutes between arrival time and physical departure of the patient from the ED treatment area Total number of patients that signed up for triage that day. All patients that are given a bed in the hospital (includes admissions and observation status) Any patient who leaves the ED before initiation of the MSE.
ED LOS for admitted patients (EDLOS for admits)
Total patients per day (Daily Census) Admit
Left without being seen (LWBS)
Received to dismiss (Admitted patients) New arrivals ADMIT
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Table 2 Department characteristics Census age distribution Adult: Pediatric: ESI triage categorization: ESI Level 1 ESI Level 2 ESI Level 3 ESI Level 4 ESI Level 5 ESI MISC. ESI (LNS or AMA) ESI not designated (or other) Admission types Psyche ICU Med/Surg Telemetry Observation
23,485 (97.33%) 642 (2.66%) 15 (0.06%) 948 (3.93%) 7968 (33.03%) 7909 (32.78%) 651 (2.70%) 562 (2.33%) 920 (3.81%) 5154 (21.36%) 966 (22.10%) 793 (18.14%) 1522 (34.82%) 532 (12.17%) 558 (12.76)
percentage of ICU admissions, and the institution does also function as a major psychiatric facility for the area. For the entire 6-month period the median daily census was 132 (95% CI, 128–133) and median daily number of admissions were 24 (95% CI, 23–24). The mean of the daily emergency department discharged patient median length of stay (ED DCLOS) was 182 minutes (95% CI, 176–189) and the mean daily emergency department median length of stay for admits (EDLOS for admits) was 404 minutes (95% CI, 393–414). Patient encounters were divided into 2 groups. Group one included patients with EDLOS for admit daily median less then 6 hours (6229 visits); group 2 included patients with median greater then 6 hours ED admit length of stay (17,898 visits). With respect to outcome parameters, ED DCLOS was 42 minutes longer for patients seen on days with prolonged EDLOS for admits (194 minutes; 95% CI, 187–201 vs 153, 145–160; Pb .0001). (see Table 3). Other variables that were associated with increased EDLOS for admits included: daily admits (P= .01), door to doctor (Pb .01) room to doctor (Pb .01), LWBS (Pb .01). The daily census averaged 10.6 more patients seen per day in the N 6 hour study group (134, 95% CI 131–137 vs. 123, 118–129; Pb .0001). Given the increased daily census in N 6 hour group we used multiple regression (R^2 was 0.49, overall Pb .001) to control for the covariate daily census and see if EDLOS for admits continued to affect ED DCLOS. The differences in DCLOS still remained significant at 24 minutes for the ≥ 6 hours. Group (189, minutes 95%CI 184–195 vs. 165, 156–174; (Pb .0001), see Figure for comparison of before and after regression. 4. Discussion The Institute of Medicine long declared that EDs are “overcrowded”, and in their report “Hospital-Based Emergency Care: At the Breaking Point” (2006) they described the adverse effects of ED overcrowding, including ambulance diversion and patient boarding in ED hallways while waiting for inpatient beds. They noted that this problem was exacerbated by the increase in ED visits nationally with a concomitant decrease in
Table 3 Outcome Metrics. Interval:
ADMIT LOS b6 hours
ADMIT LOS N6 hours.
P
Difference
ED DCLOS Total ED patients per day Door to doctor Room to doctor LWBS Admits (%)
152 123.4 23.5 11.2 2.9 21.7 (17.9%)
193.7 134 30.6 15.1 5.2 24 (18.1%)
Pb .01 Pb .01 Pb .01 P= .01 Pb .01 P= .01
41.7 10.6 7.1 3.9 2 2.3 (0.2%)
Figure. Comparison of DCLOS for admits, before and after covariant daily census control by multiple regression analysis.
overall inpatient beds [11]. While bringing attention to the issue and highlighting the consequences nearly a decade ago, the IOM did/has not provided significant metrics for quantifying overcrowding. As research in the area of ED overcrowding has grown, researchers have been able to rely on validated definitions and models of overcrowding in order to turn their attention towards quantifying/identifying bottlenecks in the chain of ED throughput [37,41,42]. Using computer simulation, Asaro et al analyzed 166,854 ED patient visits at a single center to demonstrate the respective effects of ED patient arrival rate, ED admission percentage, and inpatient bed utilization on crowding within the ED [37]. They found that there was a 12 minute increase in waiting times and a 15 minute increase in ED LOS (DCLOS) when the admission rate percentage changed from 21.7% to 27%. While Asaro et al’s study established the adverse impact of ED admission and hospital patient volume on ED flow at a large hospital, such data does not specifically provide metrics upon which process improvement may be quantified. Rather, it is more instructive to examine how longer ED LOS for admitted patients may impact the LOS for those ultimately discharged to home from the ED. White et al divided the ED Admit LOS (boarder burden) into quartiles and showed a 16 minute increase in ED LOS for discharged patients between the first quartile and the forth quartile, and when looking at the groups between 11:00 AM to 11:00 PM the throughput time increased by 57 minutes [39]. Our investigation is similar to White's attempts to quantify the association between longer times to receive hospital beds for admitted patients and the throughput time of those patients ultimately discharged from the ED [1–10,16–34,39]. We found after controlling for the covariate daily census, that there were significant differences in the length of stay for patients discharged home (DCLOS) when the length of ED stay for admitted patients was prolonged for more than 6 hours vs. shorter ED holding periods (189.4 minutes; 95%CI 184.1–194.7 vs. 164.8 minutes, 95%CI 155.7–173.9; (Pb .0001. Not surprisingly, prior research from ED and ambulatory centers has revealed that prolonged wait and throughput times can have an adverse impact not only on patients' satisfaction ratings with their experience but also on their perception of the quality of care that they receive [46,47]. Thus, emergency physicians frequently start their patient encounter with a “leg down” that has the potential to negatively influence their performance evaluations and even place them at risk of adverse medical-legal consequences. Hospital administrators typically hold ED medical directors and groups accountable for various patient throughput metrics and core measures of ED service focus on discharged patients length of stay. Our results suggest with respect to efforts to improve ED performance and service metrics, that leadership needs to focus equally, if not primarily, on improving the door to hospital bed time for patients admitted to the hospital through the ED.
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5. Limitations
References
Our study has several limitations that warrant discussion. While it is intuitive that the findings of our results would be generalizable to many other settings, i.e. prolonged ED stays for admitted patients would be associated with slower throughput for all patients, we conducted the study only at a single institution. Our facility represents an inner-city type setting with typical resource problems, including inadequate space in the ED and in-patient wards as well as RN staffing shortages throughout. The extent of the potential effect of patient holding is likely going to differ across different ED census sizes, admission rates, ED space, RN staffing patterns, and hospital size. We do have an unusually high rate of ICU admissions (25%), however, this largely reflects a lower threshold to admit patients of moderate acuity to the ICU setting based on nurse staffing/training patterns within our facility. Another limitation of our study is that we utilized daily medians in the analysis, which does not allow for analysis of the effect of triage surges (ED arrival variation) or daily complaint variation on throughput. We utilized daily medians for our various metrics so as to analyze the effects of our independent variables on a 24-hour period. In turn, this led to smoothing the impact of ED arrival surges and daily complaint variation on DCLOS. If we had utilized discrete patient data points, we could have evaluated data from smaller time segments (eg, 1- to 2-hour intervals) as well as assessing the influence of specific chief complaint variation on our outcome parameters. This limitation affects analysis of what happens with a surge over the smaller time intervals, ie, 1 to 2 hours and the downstream effect, while using daily medians (24 hours periods) allows for a demonstration of trends. Another area of concern that may influence the generalizability of our results is the degree to which an institution cares for and boards psychiatric patients. Psychiatric patients represent an important subset of ED patients nationally that have prolonged EDLOS and occupy a significant number of ED beds while awaiting inpatient treatment. At our institution, we do have delays in getting psychiatric patients transferred to inpatient treatment areas and they represent 9% of our ED patient population, with a 3.8% admission rate. As the process for psychiatric evaluations may be significantly prolonged for both admitted and discharged paitents, it is unclear how our results were impacted by this characteristics of our overall census. Another limitation of our study is the selection of variable inputs into our regression model and the potential to not account for other significant factors that may have influenced our results. We found that there was highly correlation for such metrics as door to MD and daily census. One of the variables not highly correlated was room to MD and while this is a segment in the sequence of ED patient throughput it is a very small segment and the interval varies with acuity of patients in the available ED rooms at a point in time and frequently does not track with door to doctor and/or ED daily census which track with difficulties in ED flow. Our results generate ideas for several areas of future investigation. Similar studies should be conducted to validate our results in other settings. We believe it is important for hospital administrators and ED medical directors alike to better understand which inputs for ED flow are outside the realm of ED physician influence. Going forward, researchers should also consider interventional studies to improve the flow of admitted patients while assessing its impact on throughput for those that are discharged home from the ED.
[1] McCusker J, Vadeboncoeur A, Levesque JF, Ciampi A, Belzile E. Increases in emergency department occupancy are associated with adverse 30-day outcomes. Acad Emerg Med 2014;21:1092–100. [2] Sprivulis PC, Da Silva JA, Jacobs IG, Frazer AR, Jelinek GA. The association between hospital over- crowding and mortality among patients admitted via western Australian emergency departments. Med J Aust 2006;184:208–12. [3] Richardson DB. Increase in patient mortality at 10 days associated with emergency department over- crowding. Med J Aust 2006;184:213–6. [4] Fee C, Weber EJ, Maak CA, Bacchetti P. Effect of emergency department crowding on time to antibiotics in patients admitted with community-acquired pneumonia. Ann Emerg Med 2007;50:501–5. [5] Schull MJ, Vermeulen M, Slaughter G, Morrison L, Daly P. Emergency department crowding and thrombolysis delays in acute myocardial infarction. Ann Emerg Med 2004;44:577–85. [6] Pines JM, Hollander JE. Emergency department crowding is associated with poor care for patients with severe pain. Ann Emerg Med 2008;51:1–5. [7] Mills AM, Shofer FS, Chen EH, Hollander JE, Pines JM. The association between emergency department crowding and analgesia administration in acute abdominal pain patients. Acad Emerg Med 2009;16:603–8. [8] Hwang U, Richardson L, Livote E, Harris B, Spencer N. Sean Morrison R emergency department crowding and decreased quality of pain care. Acad Emerg Med 2008; 15:1248–55. [9] Bernstein SL, Aronsky D, Duseja R, et al. The effect of emergency department crowding on clinically oriented outcomes. Acad Emerg Med 2009;16:1–10. [10] Pines JM, Pollack Jr CV, Diercks DB, Chang AM, Shofer FS, Hollander JE. The association between emergency department crowding and adverse cardiovascular outcomes in patients with chest pain. Acad Emerg Med 2009;16:617–25. [11] Institute of Medicine Hospital-Based Emergency Care: at the breaking point; 2006[released June 13, 2006]. [12] Centers for Medicare and Medicaid Services and The Joint Commission. (The revisions include enhancements and additions to “Leadership” (LD) Standard LD.04.03.11, known as “the patient flow standard,” and “Provision of Care, Treatment, and Services” (PC) Standard PC.01.01.01); 2012. [13] Hoffenberg S, Hill BH, Houry D. Does sharing process differences reduce patient length of stay in the emergency department? Acad Emerg Med 2001; 8(5):578. [14] Kyriacou DN, Ricketts V, Dyne PL, et al. A 5-year time study analysis of emergency department patient care efficiency. Ann Emerg Med 1999;34(3):326–35. [15] Schneider S, Zwemer F, Doniger A, et al. Rochester, New York: a decade of Emergency department overcrowding. Acad Emerg Med 2001;8(11):1044–50. [16] Krall SP, O'Connor R, Maerks L, Reese C. Hospital admission volume effect on emergency department length of stay intervals. Ann Emerg Med 2002;40(4):S17. [17] Forster AJ, Stiell I, Wells G, Lee AJ, van Walraven C. The effect of hospital occupancy on emergency department length of stay and patient disposition. Acad Emerg Med 2003;10:127–33. [18] Krall SP, Cornelius A, Addison JB. Hospital factors more heavily impact the variation in Total length of stay for emergency department patients than physician controlled factors. West J Emerg Med 2014;15(2):158–64 [PUBMED 24672604]. [19] Herring A, Wilper A, Himmelstein DU, Woolhandler S, Espinola JA, Brown DF, et al. Increasing length of stay among adult visits to U.S. emergency departments, 2001–2005. Acad Emerg Med 2009(16):609–16. [20] Krall SP, O'Connor RE, Maercks L. Higher inpatient medical surgical bed occupancy extends admitted patients stay. West J Emerg Med 2009;10(2):93–6. [21] Arya R. Wei g, McCoy JV, crane J, Ohman-Strickland P, Eisenstein RM, Decreasing length of stay in the emergency department with a split emergency severity index 3 patient flow model. Acad Emerg Med 2013;20:1171–9. [22] Krall SP, Addison B, Cornelius A, Richman P. Significant improvement in door-toroom time following redesign of emergency department workflow and infrastructure to accommodate triage surge and vertical patient care. Acad Emerg Med 2014;21(s1):s293. [23] Russ S, Jones I, Aronsky D, Dittus RS, Slovis C. Placing physician orders at triage: the effect of length of stay. Ann Emerg Med 2010;56:27–33. [24] Considine J, Kropman M, Kelly E. Winter C; The effect of emergency department fast track on emergency department length of stay: a case control study. Emerg Med J 2008;25:815–9. [25] Krall SP, Cornelius A, Addison JB. 12 hour physician at triage results in a decrease in the left not seen patients by 2.4 patients daily, however it also increased throughput times. Ann Emerg Med 2011;58(4S):p9A. [26] Rogg J, White BA, Biddinger PD, Chang Y, Brown DF. A long-term analysis of physician triage screening in the emergency department. Acad Emerg Med 2013;20: 374–80. [27] Han JH, France DJ, Levin SR, Jones ID, Storrow AB, Aronsky D. The effect of physician triage on emergency department length of stay. J Emerg Med 2012;2(39):227–33. [28] Holroyd BR, Bullard MJ, Latoszek K, Gordon D, Allen S, et al. Impact of a triage liaison physician on emergency department overcrowding and throughput: a randomized controlled trial. Acad Emerg Med 2007;14:702–8. [29] Kocher KE, Meurer WJ, Desmond JS, Nailamonthu BK. Effect of testing and treatment on emergency department length of stay using a National Database. Acad Emerg Med 2012;5(19):525–34. [30] Retezar R, Bessman E, Ding R, Zeger SL, McCarthy ML. The effect of triage diagnostics standing orders on emergency department treatment time. Ann Emerg Med 2010. [31] Holland LL, Smith LL, Blick KE. Reducing laboratory turnaround time outliers can reduce emergency department patient length of stay. Am J Clin Pathol 2005;124: 672–4.
6. Conclusions Prolonged ED stays for patients admitted to the hospital are associated with prolonged throughput times for patients discharged home from the ED. Core measures of ED service typically emphasize the length of stay for discharged patients. Our results suggest the need to focus equally, if not primarily on improving the door to hospital bed time for patients admitted to the hospital through the ED.
S.P. Krall et al. / American Journal of Emergency Medicine 34 (2016) 1783–1787 [32] Steindel SJ, Howanitz PJ. Physician satisfaction and emergency department laboratory test turnaround time: observations based on College of American Pathologists Qprobes studies. Arch Pathol Lab Med 2001;125:863–71. [33] Murray RP, Leroux M, Sabga E, Palatnich W, Ludwig L. Effect of point-of-care testing on length of stay in an adult emergency department. J Emerg Med 1999;17:811–4. [34] Fernandes CM, Worster A, Eva K, Hill S, McCallum C. Pneumatic tube delivery system for blood samples reduces turnaround times without affecting sample quality. J Emerg Nurs 2006;32:139–43. [35] Lucas R, Farley H, Twanmoh J, Urumov A. Olsen N, et al; Emergency department patient flow: the influence of hospital census variables on emergency department length of stay. Acad Emerg Med 2009;16:597–602. [36] Hoot NR, LeBlanc LJ, Jones ID, Levin SR, Zhou C. Forecasting emergency department crowding: a discrete event stimulation. Ann Emerg Med 2008;52:116–25. [37] Asaro PV, Lewis LM, Boxerman SB. The impact of input and output factors on emergency department throughput. Acad Emerg Med 2007;14:235–42. [38] Viccellio A, Santora C, Singer AJ, Thode Jr HC, Henry MC. The association between transfer of emergency department boarders to inpatient hallways and mortality: a 4-year experience. Ann Emerg Med 2009;54:487–91. [39] White BA, Biddinger PD, Chang Y, Grabowski B, Carignan S, Brown DFM. Boarding inpatients in the emergency department increases discharged patient length of stay. J Emerg Med 2013;44(1):230–5.
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[40] Wuerz RC, Milne LW, Eitel DR, Traverst D. Gilboy N; Reliability and validity of a new five-level triage instrument. Acad Emerg Med 2000;7:236–42. [41] Welch SJ, Stone-Griffith S, Brent Asplin B, Davidson SJ, Augustine J, et al. Emergency department operations dictionary: results of the second performance measures and benchmarking summit. Acad Emerg Med 2011;18:539–44. [42] Weiss SJ, Derlet R, Arndahl J, et al. Estimating the degree of emergency department overcrowding in academic medical centers: results of the national ED overcrowding study (NEDOCS). Acad Emerg Med 2004;11:38–50. [43] Saunders CE, Makens PK, LeBlanc LJ. Modeling emergency department operations using advanced computer simulation systems. Ann Emerg Med 1989; 18:134–40. [44] Patel PB, Combs MA, Vinson DR. Reduction of admit wait times: the effect of a leadership-based program. Acad Emerg Med 2014;21:266–73. [45] Schull MJ, Szalia JP, Schwartz B, Redelmeier DA. Emergency department overcrowding following systematic hospital restructuring: trends at twenty hospitals over ten years. Acad Emerg Med 2001;8:1037–43. [46] Trout A, Magnusson AR, Hedges JR. Patient satisfaction investigations and the emergency department: what does the literature say? Acad Emerg Med 2000;7(6): 695–709. [47] Bleustein C, Rothschild DB, Valen A, Valaitis E, Schweitzer L, Jones R, et al. Wait times, patient satisfaction scores, and the perception of care Manag Care 2014;20(5):393–400.
Contents lists available at ScienceDirect
American Journal of Emergency Medicine journal homepage: www.elsevier.com/locate/ajem
Original Contribution
Increased door to admission time is associated with prolonged throughput for ED patients discharged home☆ Scott P. Krall, MD, MBA a, Jose Guardiola, PhD b, Peter B. Richman, MD, MBA a,⁎ a b
Department of Emergency Medicine, Texas A&M Health Science Center/Christus Spohn, Corpus Christi, TX Department of Mathematics, Texas A&M University-Corpus Christi, Corpus Christi, TX
a r t i c l e
i n f o
Article history: Received 17 January 2016 Received in revised form 9 May 2016 Accepted 1 June 2016
a b s t r a c t Background: Emergency Department (ED) service evaluations are typically based on surveys of discharged patients. Physicians/administrators benefit from data that quantifies system-based factors that adversely impact the experience of those who represent the survey cohort. Objective: While investigators have established that admitted patient boarding impacts overall ED throughput times, we sought to specifically quantify the relationship between throughput times for patients admitted (EDLOS) versus discharged home from the ED (DCLOS). Methods: We performed a prospective analysis of consecutive patient encounters at an inner-city ED. Variables collected: median daily DCLOS for ED patients, ED daily census, left without being seen (LWBS), median door to doctor, median room to doctor, and daily number admitted. Admitted patients divided into 2 groups based on daily median EDLOS for admits (b 6 hours, ≥6 hours). Continuous variables analyzed by t-tests. Multivariate regression utilized to identify independent effects of the co-variants on median daily DCLOS. Results: We analyzed 24,127 patient visits. ED patient DCLOS was longer for patients seen on days with prolonged EDLOS (193.7 minutes, 95%CI 186.7–200.7 vs. 152.8, 144.9–160.5, Pb .0001). Variables that were associated with increased daily median EDLOS for admits included: daily admits (P= 0.01), room to doctor time (Pb .01), number of patients that left without being seen (Pb .01). When controlling for the covariate daily census, differences in DCLOS remained significant for the ≥6 hours group (189.4 minutes, 95%CI 184.1–194.7 vs. 164.8, 155.7–173.9 (Pb .0001). Conclusion: Prolonged ED stays for admitted patients were associated with prolonged throughput times for patients discharged home from the ED. © 2016 Elsevier Inc. All rights reserved.
1. Introduction The emergency department has become the entry portal for the hospital as well as the final safety net for all patients in the United States healthcare system. As a consequence of these roles, EDs are becoming increasingly crowded (or overcrowded) with adverse consequences. McCusker linked administrative databases of over 670,000 patients and found that a 10% increase in emergency bed relative occupancy ratio was associated with a 3% increase of the following 30-day outcomes: deaths (for admitted and discharged patients), one or more return ED visits (among discharged patients), and hospital admission at first return visit with a “strong correlation between bed crowding and mortality among large emergency departments” [1]. Numerous other studies have shown similar negative effects on patient outcomes [2–10].
☆ Financial Disclosures/Conflicts: None. ⁎ Corresponding author at: Department of Emergency Medicine, Texas A&M Health Science Center/Christus Spohn, Corpus Christi, TX 78405. Tel.: +1 361 902 6762. E-mail address: [email protected] (P.B. Richman). http://dx.doi.org/10.1016/j.ajem.2016.06.002 0735-6757/© 2016 Elsevier Inc. All rights reserved.
In response to these concerns, the Institute of Medicine has recommended that hospitals reduce ED overcrowding and utilize tools such as queuing theory on admission process and 23 hour ED observation units for improving hospital efficiency [11]. The Centers for Medicare and Medicaid Services (CMS) and The Joint Commission (TJC) have begun to regulate the process with the following performance measure–TJC element LD.04.03.11 that states “the hospital measures and sets goals for mitigating and managing the boarding (the practice of holding patients in the ED or a temporary location for four or more hours after the decision to admit or transfer has been made) of patients who come through the emergency department” [12]. Researchers studying the area of ED patient flow have demonstrated that a variety of internal processes, external factors and the ED department size can impact the ED throughput [13–45]. Such studies of internal inputs have addressed department process changes, including the use of “fast track” areas and redesigning the location of nursing staff and physicians throughout the ED [21–28]. Investigators have also evaluated the effect of optimizing laboratory efficiencies including point of care testing as a means to reduce emergency department length of stay [29–34]. The other major factors in ED flow are hospital occupancy
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and boarding of in-patients in the ED, and, as previously noted, these have been shown to have an adverse impact on emergency department length of stay and outcomes for patients who are ultimately admitted to the hospital [16,17,20,35–39]. The ED is a complex system due to the uncontrolled nature of inputs. Patient factors that influence ED flow include variation in acuity, types of chief complaints and unpredictable surges in patient volume throughout a given day. Flow is also significantly influenced by hospital-based factors outside the direct domain of ED control. As customer service surveys for ED patients focus on patients who are discharged home, it is also important to understand the impact of patient boarding on flow for this large segment of the ED census. We conducted a prospective, observational study to test the hypothesis that extended EDLOS for admitted patients (boarding) would be associated with increased length of stay for those patients ultimately discharged from the ED (DCLOS), and quantify that effect in our institution. 2. Methods 2.1. Study design We performed a prospective, observational study to assess the relationship between throughput of patients discharged from the ED and boarding times for patients admitted to the hospital through the ED. 2.2. Study setting We conducted this study at an urban county hospital facility. The hospital is a designated level-2 trauma center by the American College of Surgeons covering a 12-county region. Physicians at our facility see 48,000 ED patients annually, with an admission rate of 17.5%. Twentyfive percent of those admissions are admitted to the intensive care units. Physician staffing in the main part of the ED consists of an attending physician and two emergency medicine residents (of variable levels of training) for a 24-hour period, an additional fast track area staffed 20 hours by an advanced practice practitioner. The radiology department is located directly behind the ED and is equipped with CT, ultrasound, as well as MRI capabilities. The Christus Spohn Institutional Review Board designated our study as exempt status prior to the initiation of data collection. 2.3. Population Consecutive patient encounters during the period from March 2013 to November 2013 were identified through query of our electronic medical record (Meditech Information Technologies, Inc, Westwood, MA). 2.4. Study process The ED had an evaluation process during the study period that is typical of most EDs. All walk-in patients checked in at the triage window, which was staffed by an ED technician. The patient signed in on a triage complaint form with their name, time of arrival, and chief complaint (time of arrival started the ED intervals). Then, a registered nurse evaluated the patient. A vast majority of the patients received an evaluation using the Emergency Severity Index (ESI) index based on the stability of the patient as well as need for evaluation [40]. If their complaint had potential severity, and/or if a room were available, the patient would be immediately assigned to an ED room. If no rooms were available, the triage nurse asked the patient to wait in the lobby until a room was available for further evaluation. Most patients that arrived by ambulance were immediately taken to a room for evaluation (in that situation, time of nurses initial triage started the ED intervals). The emergency physician, or advanced practice practitioner then assessed the patient to determine if further evaluation or consultation was necessary. The emergency physician determined if the patient needed to be admitted
to the hospital for further treatment (EDLOS for admits), or discharged home with discharge instructions for care at home (DCLOS). 2.5. Method of measurement Median daily time interval data was collected from the ED tracking system, Meditech©. This is a dynamic tracking system that requires the staff to time mark the following events: when the patient received a room (room), when the physician assessed the patient (doctor), and when the patient was discharged from the ED (dismiss). The variables collected included classic ED measures as defined by Welch et al [41] (Table 1). We chose those variables as they represented the ED measures with separate event points entered for every visit, and represented distinct ED measures in the flow of a patient through the ED. 2.6. Primary data analysis The data analysis includes summary statistics for the intervals analyzed. The interval data was divided into 2 groups based on the median daily EDLOS for admits. 6 hours was chosen as the dividing point to reflect 2 hours for the physician to make a decision to admit the patient and the additional 4 hours from the TJC element LD.04.03.11 for maximum acceptable hold interval in the emergency department. Mean intervals of the 2 groups, divided based on the daily median interval EDLOS for admits, were compared by t-tests. Subsequently, we utilized regression analysis to control for confounding variables. Our multiple regression model contained the following variables: DCLOS (dependent), EDLOS for admit group (b6 hours, N = 6 hours), daily census, LWBS (count), median daily room to doctor, median daily door to MD, and number of admissions. The number of admissions and median door to MD variables that were highly correlated were excluded to avoid multicollinearity. 3. Results Data were collated from 24,127 patient visits. There was an admission rate of 17.3% during the study period. Patient characteristics are presented in Table 2. Our emergency department has visits primarily by adults with only 2.66% of the census comprised of pediatric patients. The emergency severity index (ESI) level 3 and 4 categorization of patients was similar, but 21.36% of the visits did not receive the ESI designation in the electronic medical record. The department has a high
Table 1 Emergency department (ED) metrics and definitions [36]. ED Interval
Meditech Interval
Definitions
Room to doctor (doctor includes physician, resident or allied health professional) ED LOS for discharged patients (DCLOS)
Room to provider
Time it takes for a physician to see the patient in the room after the patient is placed in the room
Received to dismiss
The time interval in minutes between arrival time to physical discharge time The time interval in minutes between arrival time and physical departure of the patient from the ED treatment area Total number of patients that signed up for triage that day. All patients that are given a bed in the hospital (includes admissions and observation status) Any patient who leaves the ED before initiation of the MSE.
ED LOS for admitted patients (EDLOS for admits)
Total patients per day (Daily Census) Admit
Left without being seen (LWBS)
Received to dismiss (Admitted patients) New arrivals ADMIT
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Table 2 Department characteristics Census age distribution Adult: Pediatric: ESI triage categorization: ESI Level 1 ESI Level 2 ESI Level 3 ESI Level 4 ESI Level 5 ESI MISC. ESI (LNS or AMA) ESI not designated (or other) Admission types Psyche ICU Med/Surg Telemetry Observation
23,485 (97.33%) 642 (2.66%) 15 (0.06%) 948 (3.93%) 7968 (33.03%) 7909 (32.78%) 651 (2.70%) 562 (2.33%) 920 (3.81%) 5154 (21.36%) 966 (22.10%) 793 (18.14%) 1522 (34.82%) 532 (12.17%) 558 (12.76)
percentage of ICU admissions, and the institution does also function as a major psychiatric facility for the area. For the entire 6-month period the median daily census was 132 (95% CI, 128–133) and median daily number of admissions were 24 (95% CI, 23–24). The mean of the daily emergency department discharged patient median length of stay (ED DCLOS) was 182 minutes (95% CI, 176–189) and the mean daily emergency department median length of stay for admits (EDLOS for admits) was 404 minutes (95% CI, 393–414). Patient encounters were divided into 2 groups. Group one included patients with EDLOS for admit daily median less then 6 hours (6229 visits); group 2 included patients with median greater then 6 hours ED admit length of stay (17,898 visits). With respect to outcome parameters, ED DCLOS was 42 minutes longer for patients seen on days with prolonged EDLOS for admits (194 minutes; 95% CI, 187–201 vs 153, 145–160; Pb .0001). (see Table 3). Other variables that were associated with increased EDLOS for admits included: daily admits (P= .01), door to doctor (Pb .01) room to doctor (Pb .01), LWBS (Pb .01). The daily census averaged 10.6 more patients seen per day in the N 6 hour study group (134, 95% CI 131–137 vs. 123, 118–129; Pb .0001). Given the increased daily census in N 6 hour group we used multiple regression (R^2 was 0.49, overall Pb .001) to control for the covariate daily census and see if EDLOS for admits continued to affect ED DCLOS. The differences in DCLOS still remained significant at 24 minutes for the ≥ 6 hours. Group (189, minutes 95%CI 184–195 vs. 165, 156–174; (Pb .0001), see Figure for comparison of before and after regression. 4. Discussion The Institute of Medicine long declared that EDs are “overcrowded”, and in their report “Hospital-Based Emergency Care: At the Breaking Point” (2006) they described the adverse effects of ED overcrowding, including ambulance diversion and patient boarding in ED hallways while waiting for inpatient beds. They noted that this problem was exacerbated by the increase in ED visits nationally with a concomitant decrease in
Table 3 Outcome Metrics. Interval:
ADMIT LOS b6 hours
ADMIT LOS N6 hours.
P
Difference
ED DCLOS Total ED patients per day Door to doctor Room to doctor LWBS Admits (%)
152 123.4 23.5 11.2 2.9 21.7 (17.9%)
193.7 134 30.6 15.1 5.2 24 (18.1%)
Pb .01 Pb .01 Pb .01 P= .01 Pb .01 P= .01
41.7 10.6 7.1 3.9 2 2.3 (0.2%)
Figure. Comparison of DCLOS for admits, before and after covariant daily census control by multiple regression analysis.
overall inpatient beds [11]. While bringing attention to the issue and highlighting the consequences nearly a decade ago, the IOM did/has not provided significant metrics for quantifying overcrowding. As research in the area of ED overcrowding has grown, researchers have been able to rely on validated definitions and models of overcrowding in order to turn their attention towards quantifying/identifying bottlenecks in the chain of ED throughput [37,41,42]. Using computer simulation, Asaro et al analyzed 166,854 ED patient visits at a single center to demonstrate the respective effects of ED patient arrival rate, ED admission percentage, and inpatient bed utilization on crowding within the ED [37]. They found that there was a 12 minute increase in waiting times and a 15 minute increase in ED LOS (DCLOS) when the admission rate percentage changed from 21.7% to 27%. While Asaro et al’s study established the adverse impact of ED admission and hospital patient volume on ED flow at a large hospital, such data does not specifically provide metrics upon which process improvement may be quantified. Rather, it is more instructive to examine how longer ED LOS for admitted patients may impact the LOS for those ultimately discharged to home from the ED. White et al divided the ED Admit LOS (boarder burden) into quartiles and showed a 16 minute increase in ED LOS for discharged patients between the first quartile and the forth quartile, and when looking at the groups between 11:00 AM to 11:00 PM the throughput time increased by 57 minutes [39]. Our investigation is similar to White's attempts to quantify the association between longer times to receive hospital beds for admitted patients and the throughput time of those patients ultimately discharged from the ED [1–10,16–34,39]. We found after controlling for the covariate daily census, that there were significant differences in the length of stay for patients discharged home (DCLOS) when the length of ED stay for admitted patients was prolonged for more than 6 hours vs. shorter ED holding periods (189.4 minutes; 95%CI 184.1–194.7 vs. 164.8 minutes, 95%CI 155.7–173.9; (Pb .0001. Not surprisingly, prior research from ED and ambulatory centers has revealed that prolonged wait and throughput times can have an adverse impact not only on patients' satisfaction ratings with their experience but also on their perception of the quality of care that they receive [46,47]. Thus, emergency physicians frequently start their patient encounter with a “leg down” that has the potential to negatively influence their performance evaluations and even place them at risk of adverse medical-legal consequences. Hospital administrators typically hold ED medical directors and groups accountable for various patient throughput metrics and core measures of ED service focus on discharged patients length of stay. Our results suggest with respect to efforts to improve ED performance and service metrics, that leadership needs to focus equally, if not primarily, on improving the door to hospital bed time for patients admitted to the hospital through the ED.
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5. Limitations
References
Our study has several limitations that warrant discussion. While it is intuitive that the findings of our results would be generalizable to many other settings, i.e. prolonged ED stays for admitted patients would be associated with slower throughput for all patients, we conducted the study only at a single institution. Our facility represents an inner-city type setting with typical resource problems, including inadequate space in the ED and in-patient wards as well as RN staffing shortages throughout. The extent of the potential effect of patient holding is likely going to differ across different ED census sizes, admission rates, ED space, RN staffing patterns, and hospital size. We do have an unusually high rate of ICU admissions (25%), however, this largely reflects a lower threshold to admit patients of moderate acuity to the ICU setting based on nurse staffing/training patterns within our facility. Another limitation of our study is that we utilized daily medians in the analysis, which does not allow for analysis of the effect of triage surges (ED arrival variation) or daily complaint variation on throughput. We utilized daily medians for our various metrics so as to analyze the effects of our independent variables on a 24-hour period. In turn, this led to smoothing the impact of ED arrival surges and daily complaint variation on DCLOS. If we had utilized discrete patient data points, we could have evaluated data from smaller time segments (eg, 1- to 2-hour intervals) as well as assessing the influence of specific chief complaint variation on our outcome parameters. This limitation affects analysis of what happens with a surge over the smaller time intervals, ie, 1 to 2 hours and the downstream effect, while using daily medians (24 hours periods) allows for a demonstration of trends. Another area of concern that may influence the generalizability of our results is the degree to which an institution cares for and boards psychiatric patients. Psychiatric patients represent an important subset of ED patients nationally that have prolonged EDLOS and occupy a significant number of ED beds while awaiting inpatient treatment. At our institution, we do have delays in getting psychiatric patients transferred to inpatient treatment areas and they represent 9% of our ED patient population, with a 3.8% admission rate. As the process for psychiatric evaluations may be significantly prolonged for both admitted and discharged paitents, it is unclear how our results were impacted by this characteristics of our overall census. Another limitation of our study is the selection of variable inputs into our regression model and the potential to not account for other significant factors that may have influenced our results. We found that there was highly correlation for such metrics as door to MD and daily census. One of the variables not highly correlated was room to MD and while this is a segment in the sequence of ED patient throughput it is a very small segment and the interval varies with acuity of patients in the available ED rooms at a point in time and frequently does not track with door to doctor and/or ED daily census which track with difficulties in ED flow. Our results generate ideas for several areas of future investigation. Similar studies should be conducted to validate our results in other settings. We believe it is important for hospital administrators and ED medical directors alike to better understand which inputs for ED flow are outside the realm of ED physician influence. Going forward, researchers should also consider interventional studies to improve the flow of admitted patients while assessing its impact on throughput for those that are discharged home from the ED.
[1] McCusker J, Vadeboncoeur A, Levesque JF, Ciampi A, Belzile E. Increases in emergency department occupancy are associated with adverse 30-day outcomes. Acad Emerg Med 2014;21:1092–100. [2] Sprivulis PC, Da Silva JA, Jacobs IG, Frazer AR, Jelinek GA. The association between hospital over- crowding and mortality among patients admitted via western Australian emergency departments. Med J Aust 2006;184:208–12. [3] Richardson DB. Increase in patient mortality at 10 days associated with emergency department over- crowding. Med J Aust 2006;184:213–6. [4] Fee C, Weber EJ, Maak CA, Bacchetti P. Effect of emergency department crowding on time to antibiotics in patients admitted with community-acquired pneumonia. Ann Emerg Med 2007;50:501–5. [5] Schull MJ, Vermeulen M, Slaughter G, Morrison L, Daly P. Emergency department crowding and thrombolysis delays in acute myocardial infarction. Ann Emerg Med 2004;44:577–85. [6] Pines JM, Hollander JE. Emergency department crowding is associated with poor care for patients with severe pain. Ann Emerg Med 2008;51:1–5. [7] Mills AM, Shofer FS, Chen EH, Hollander JE, Pines JM. The association between emergency department crowding and analgesia administration in acute abdominal pain patients. Acad Emerg Med 2009;16:603–8. [8] Hwang U, Richardson L, Livote E, Harris B, Spencer N. Sean Morrison R emergency department crowding and decreased quality of pain care. Acad Emerg Med 2008; 15:1248–55. [9] Bernstein SL, Aronsky D, Duseja R, et al. The effect of emergency department crowding on clinically oriented outcomes. Acad Emerg Med 2009;16:1–10. [10] Pines JM, Pollack Jr CV, Diercks DB, Chang AM, Shofer FS, Hollander JE. The association between emergency department crowding and adverse cardiovascular outcomes in patients with chest pain. Acad Emerg Med 2009;16:617–25. [11] Institute of Medicine Hospital-Based Emergency Care: at the breaking point; 2006[released June 13, 2006]. [12] Centers for Medicare and Medicaid Services and The Joint Commission. (The revisions include enhancements and additions to “Leadership” (LD) Standard LD.04.03.11, known as “the patient flow standard,” and “Provision of Care, Treatment, and Services” (PC) Standard PC.01.01.01); 2012. [13] Hoffenberg S, Hill BH, Houry D. Does sharing process differences reduce patient length of stay in the emergency department? Acad Emerg Med 2001; 8(5):578. [14] Kyriacou DN, Ricketts V, Dyne PL, et al. A 5-year time study analysis of emergency department patient care efficiency. Ann Emerg Med 1999;34(3):326–35. [15] Schneider S, Zwemer F, Doniger A, et al. Rochester, New York: a decade of Emergency department overcrowding. Acad Emerg Med 2001;8(11):1044–50. [16] Krall SP, O'Connor R, Maerks L, Reese C. Hospital admission volume effect on emergency department length of stay intervals. Ann Emerg Med 2002;40(4):S17. [17] Forster AJ, Stiell I, Wells G, Lee AJ, van Walraven C. The effect of hospital occupancy on emergency department length of stay and patient disposition. Acad Emerg Med 2003;10:127–33. [18] Krall SP, Cornelius A, Addison JB. Hospital factors more heavily impact the variation in Total length of stay for emergency department patients than physician controlled factors. West J Emerg Med 2014;15(2):158–64 [PUBMED 24672604]. [19] Herring A, Wilper A, Himmelstein DU, Woolhandler S, Espinola JA, Brown DF, et al. Increasing length of stay among adult visits to U.S. emergency departments, 2001–2005. Acad Emerg Med 2009(16):609–16. [20] Krall SP, O'Connor RE, Maercks L. Higher inpatient medical surgical bed occupancy extends admitted patients stay. West J Emerg Med 2009;10(2):93–6. [21] Arya R. Wei g, McCoy JV, crane J, Ohman-Strickland P, Eisenstein RM, Decreasing length of stay in the emergency department with a split emergency severity index 3 patient flow model. Acad Emerg Med 2013;20:1171–9. [22] Krall SP, Addison B, Cornelius A, Richman P. Significant improvement in door-toroom time following redesign of emergency department workflow and infrastructure to accommodate triage surge and vertical patient care. Acad Emerg Med 2014;21(s1):s293. [23] Russ S, Jones I, Aronsky D, Dittus RS, Slovis C. Placing physician orders at triage: the effect of length of stay. Ann Emerg Med 2010;56:27–33. [24] Considine J, Kropman M, Kelly E. Winter C; The effect of emergency department fast track on emergency department length of stay: a case control study. Emerg Med J 2008;25:815–9. [25] Krall SP, Cornelius A, Addison JB. 12 hour physician at triage results in a decrease in the left not seen patients by 2.4 patients daily, however it also increased throughput times. Ann Emerg Med 2011;58(4S):p9A. [26] Rogg J, White BA, Biddinger PD, Chang Y, Brown DF. A long-term analysis of physician triage screening in the emergency department. Acad Emerg Med 2013;20: 374–80. [27] Han JH, France DJ, Levin SR, Jones ID, Storrow AB, Aronsky D. The effect of physician triage on emergency department length of stay. J Emerg Med 2012;2(39):227–33. [28] Holroyd BR, Bullard MJ, Latoszek K, Gordon D, Allen S, et al. Impact of a triage liaison physician on emergency department overcrowding and throughput: a randomized controlled trial. Acad Emerg Med 2007;14:702–8. [29] Kocher KE, Meurer WJ, Desmond JS, Nailamonthu BK. Effect of testing and treatment on emergency department length of stay using a National Database. Acad Emerg Med 2012;5(19):525–34. [30] Retezar R, Bessman E, Ding R, Zeger SL, McCarthy ML. The effect of triage diagnostics standing orders on emergency department treatment time. Ann Emerg Med 2010. [31] Holland LL, Smith LL, Blick KE. Reducing laboratory turnaround time outliers can reduce emergency department patient length of stay. Am J Clin Pathol 2005;124: 672–4.
6. Conclusions Prolonged ED stays for patients admitted to the hospital are associated with prolonged throughput times for patients discharged home from the ED. Core measures of ED service typically emphasize the length of stay for discharged patients. Our results suggest the need to focus equally, if not primarily on improving the door to hospital bed time for patients admitted to the hospital through the ED.
S.P. Krall et al. / American Journal of Emergency Medicine 34 (2016) 1783–1787 [32] Steindel SJ, Howanitz PJ. Physician satisfaction and emergency department laboratory test turnaround time: observations based on College of American Pathologists Qprobes studies. Arch Pathol Lab Med 2001;125:863–71. [33] Murray RP, Leroux M, Sabga E, Palatnich W, Ludwig L. Effect of point-of-care testing on length of stay in an adult emergency department. J Emerg Med 1999;17:811–4. [34] Fernandes CM, Worster A, Eva K, Hill S, McCallum C. Pneumatic tube delivery system for blood samples reduces turnaround times without affecting sample quality. J Emerg Nurs 2006;32:139–43. [35] Lucas R, Farley H, Twanmoh J, Urumov A. Olsen N, et al; Emergency department patient flow: the influence of hospital census variables on emergency department length of stay. Acad Emerg Med 2009;16:597–602. [36] Hoot NR, LeBlanc LJ, Jones ID, Levin SR, Zhou C. Forecasting emergency department crowding: a discrete event stimulation. Ann Emerg Med 2008;52:116–25. [37] Asaro PV, Lewis LM, Boxerman SB. The impact of input and output factors on emergency department throughput. Acad Emerg Med 2007;14:235–42. [38] Viccellio A, Santora C, Singer AJ, Thode Jr HC, Henry MC. The association between transfer of emergency department boarders to inpatient hallways and mortality: a 4-year experience. Ann Emerg Med 2009;54:487–91. [39] White BA, Biddinger PD, Chang Y, Grabowski B, Carignan S, Brown DFM. Boarding inpatients in the emergency department increases discharged patient length of stay. J Emerg Med 2013;44(1):230–5.
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[40] Wuerz RC, Milne LW, Eitel DR, Traverst D. Gilboy N; Reliability and validity of a new five-level triage instrument. Acad Emerg Med 2000;7:236–42. [41] Welch SJ, Stone-Griffith S, Brent Asplin B, Davidson SJ, Augustine J, et al. Emergency department operations dictionary: results of the second performance measures and benchmarking summit. Acad Emerg Med 2011;18:539–44. [42] Weiss SJ, Derlet R, Arndahl J, et al. Estimating the degree of emergency department overcrowding in academic medical centers: results of the national ED overcrowding study (NEDOCS). Acad Emerg Med 2004;11:38–50. [43] Saunders CE, Makens PK, LeBlanc LJ. Modeling emergency department operations using advanced computer simulation systems. Ann Emerg Med 1989; 18:134–40. [44] Patel PB, Combs MA, Vinson DR. Reduction of admit wait times: the effect of a leadership-based program. Acad Emerg Med 2014;21:266–73. [45] Schull MJ, Szalia JP, Schwartz B, Redelmeier DA. Emergency department overcrowding following systematic hospital restructuring: trends at twenty hospitals over ten years. Acad Emerg Med 2001;8:1037–43. [46] Trout A, Magnusson AR, Hedges JR. Patient satisfaction investigations and the emergency department: what does the literature say? Acad Emerg Med 2000;7(6): 695–709. [47] Bleustein C, Rothschild DB, Valen A, Valaitis E, Schweitzer L, Jones R, et al. Wait times, patient satisfaction scores, and the perception of care Manag Care 2014;20(5):393–400.