Improving emergency department throughput: An outcomes evaluation of two additional models of care

ARTICLE IN PRESS International Emergency Nursing ■■ (2015) ■■–■■

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Improving emergency department throughput: An outcomes evaluation of two additional models of care Elizabeth Elder MN (Honours) MAdvancedPrac (Emergency Nursing) BN, BA, RN (Lecturer) a,*, Amy N.B. Johnston BSc(Hons), BN, MEd, PhD, RN (Research Fellow) b, Julia Crilly MEmerg Nurs(Hons), BN, RN, PhD (Associate Professor) b a

School of Nursing & Midwifery, Griffith Health, Gold Coast Campus, Griffith University, QLD 4222, Australia Department of Emergency Medicine & Griffith Health Institute, Gold Coast Hospital and Health Service & Griffith University, Southport, QLD 4215, Australia b

A R T I C L E

I N F O

Article history: Received 2 March 2015 Received in revised form 27 June 2015 Accepted 3 July 2015 Keywords: Emergency department Length of stay Physician at triage Medical assessment units Outcomes National Emergency Access Targets (NEAT)

A B S T R A C T

Objective: The aim of this study was to explore the impact of incorporating a physician at triage (PAT) and the implementation of a medical assessment unit (MAU) on emergency department (ED) patient throughput. Methods: A retrospective comparative analysis of two additional models of care (standard care, T1; PAT, T2 and PATplusMAU, T3) was undertaken. Patient presentations to a large public teaching hospital in SouthEast Queensland between 10th January 2013 and 25th February 2013, and the same time period in 2012, were included. The impact of these care models on ED length of stay and other outcomes (time to be seen by a clinician, time from bed request to ward transfer, meeting 4 hour transit targets, admission rates and the proportion of patients who did not wait) were compared. Results: Compared to standard care, ED length of stay appeared to decrease with the introduction of both models, but was only significantly decreased after PATplusMAU was implemented (2013; T1, 186 min; T2, 181 min; T3, 175 min: T1 vs T3, P < 0.001). Outcomes that improved included: time to be seen by a clinician, proportion of patients who did not wait; increase in meeting 4-hour length of stay target for both admitted and not-admitted patients. Conclusion: Placing a physician at triage and implementing a medical assessment unit were viable models of care that promoted patient flow and helped meet several time-sensitive health service targets. © 2015 Elsevier Ltd. All rights reserved.

1. Introduction Emergency Departments (EDs) are the primary admission point for unplanned admissions into hospitals. The number of presentations to EDs in Australia has increased substantially over time (Lowthian et al., 2012). During the year 2012–13 there were in excess of 6.7 million presentations to public EDs (Australian Institute of

Author Contribution: This work was completed as part of Masters of Nursing – Honours through Griffith University. Each member contributed to the manuscript in the following manner: Planning: EE, AJ, JC. Conduct: EE, AJ, JC. Reporting of work: EE, AJ, JC. Responsible for overall content as guarantor(s): EE, AJ, JC. * Corresponding author. School of Nursing & Midwifery, Griffith Health, Gold Coast Campus, Griffith University, QLD 4222, Australia. Tel.: +61 755528927; fax: +61 755528526. E-mail address: e.elder@griffith.edu.au (E. Elder).

Health and Welfare, 2013), with this number increasing to 7.4 million visits in 2014 (Austalian Institute of Health and Welfare, 2014). Increasing numbers of presentations to the ED results in ED and hospital crowding. The detrimental impact of ED crowding on health service delivery outcomes is well established. ED crowding, with increased ED length of stay (LOS) (Travers and Lee, 2006) increased ED wait times (Terris et al., 2004) and increased ambulance diversions (Bernstein et al., 2009), has been associated with increased mortality (Bernstein et al., 2009; Guttmann et al., 2011) and decreased staff and patient satisfaction (Wiler et al., 2010). The introduction of National Emergency Access Targets (NEAT) by the Australian Government in 2009 enhanced the drive for service delivery change. Requirements to meet time targets are further augmented by funding incentives such as activity-based funding schemes (Bell et al., 2014) as well the desire to improve outcomes for patients. Time targets are stratified by triage category according to the Australasian Triage Scale (ATS). The ATS is based on a five level (1– 5) numbering system; ATS category one patients (most critical)

http://dx.doi.org/10.1016/j.ienj.2015.07.001 1755-599X/© 2015 Elsevier Ltd. All rights reserved.

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should be seen immediately, ATS category five patients are less emergent but should wait no more than 120 minutes to see a clinician (Hodge et al., 2013). Throughput delays, i.e. delays in the time from presentation to a decision being made to either admit or discharge a patient, can impact the overall LOS for a patient in the ED (Holroyd et al., 2007; Rowe et al., 2011). Various models of care which attempt to address the bottlenecking of patients in EDs during the input, throughput or output phases of the patient journey have been described (Asplin et al., 2003; Cheng et al., 2013; Ducharme et al., 2009; Imperato et al., 2012; Li et al., 2010; McNeill et al., 2009; Patel et al., 2012; Sullivan et al., 2014). Two models of care that may improve ED throughput are having a physician at (or in) triage (PAT/PIT) and the establishment of a medical assessment unit (MAU). PAT has been reported to impact on the ability to reduce ED LOS (Han et al., 2010; Imperato et al., 2012; Shetty et al., 2012), did not wait (DNW) rate (Cheng et al., 2013; Han et al., 2010; Shetty et al., 2012) and time to be seen (Terris et al., 2004) in hospital settings in Singapore (Travers and Lee, 2006), the United Kingdom (Terris et al., 2004), Ireland (Moloney et al., 2006), the United States of America (Han et al., 2010; Imperato et al., 2012), Canada (Cheng et al., 2013) and Australia (Bernstein et al., 2009). Medical assessment units are specifically designed units that enable specialist assessment and care of medical patients as well as facilitating care whilst patients wait for appropriate referrals to specialist and allied healthcare professionals (Li et al., 2010). Medical assessment units have been reported to improve patient flow, and decrease the number of hospital admissions (McNeill et al., 2011; Scott et al., 2009) and ED LOS (Moloney et al., 2006; Yoon et al., 2003) in hospital settings in Ireland, Canada and Australia (Sullivan et al., 2014). Previous studies evaluating these interventions have reported outcomes following implementation of individual (single) models of care. The stepwise establishment of these models (PAT and MAU) in one Australian public hospital provided an opportunity to investigate the impact (in a singular and bundled fashion) of these models of care on health service delivery outcomes. The research question guiding this study was ‘What is the impact of implementing two additional models of care (PAT and PATplusMAU) on patient and health service outcomes?’ 2. Methods A comparative retrospective study comparing two additional models of care (PAT and PATplusMAU) with standard care was conducted. The study site was a 570-bed regional, public teaching hospital that services both paediatric and adults from a local resident population of 494,501 people during the 2011 census (Gold Coast City Council, 2011). In 2013, the primary focus year for this study, this ED managed 74,277 presentations. The National Emergency Access Target (NEAT) strategy was introduced to the study site in July 2012. All presentations made to the ED over a six-week period in 2013 were included in the analysis. These data were divided into three blocks, each from a two-week period for comparison: T1: standard care (10th January 2013–25th January 2013), T2, PAT (26th January 2013–8th February 2013) and T3, PATplusMAU (9th February 2013–25th February 2013). Presentations to ED made during the same 6-week time period from the previous year (10th January 2012–25th February 2012) were also included to provide a comparative measure of annual variation. Data were extracted from the Emergency Department Information System (EDIS) by the hospital’s Health Informatics Directorate personnel and provided to the researchers in an Excel spreadsheet. Data included gender, age, date and time of presentation, mode of arrival, Australasian Triage Scale (ATS), reason for presentation, date and time of triage, date and time seen by a clinician, ED

International Classification of Disease codes, date and time of bed request, date and time of ED departure and discharge destination from ED. The PAT model of care involved utilisation (reallocation) of a medical officer from the existing ED staffing, who was allocated to the triage area to work in conjunction with one or two triage nurses. The aim of PAT was to provide a rapid medical assessment and, if need be, intervention, for patients on presentation to the ED. The PAT model was operational 24 hours each day. The MAU contained 28 beds and commenced admitting patients 2 weeks after the PAT model of care was implemented. The MAU was staffed by a pool of 51 full-time equivalent nurses and nine doctors (five senior doctors and four interns) from existing staffing. It also operated 24 hours per day. The aim of the MAU was to provide ongoing assessment and care of medically stable patients for up to 48 hours. After this time the patient was to be admitted for further care or discharged home. Outcomes evaluated for this study included: ED length of stay (LOS), ED LOS stratified by ATS category, re-presentation rates, NEAT compliance and discharge disposition. The specific definitions used within this study are outlined in Table 1. Data were checked for outliers, inaccuracies and missing data prior to re-coding. Data were analysed using Statistical Package for the Social Sciences (SPSS) version 22 (SPSS, 2014). Descriptive and inferential statistics were used to describe the samples and make comparisons across time frames (i.e. between 2012 and 2013 and between T1, standard care; T2, PAT and T3, PATplusMAU). Variables that were not normally distributed such as age, time to be seen and ED LOS were reported using median and interquartile range (IQR). To detect differences between time frames, chi-square tests were used for categorical data and Mann Whitney–U tests were used for non-parametrically distributed continuous data (e.g. ED LOS). Significance was set at P < 0.05. A total of 17,185 presentations from 2012 and 2013 were available for inclusion in the analysis. ED discharge diagnosis codes were ambiguously classified for 2040 presentations, precluding definitive analysis. These data were therefore excluded from the ‘presenting complaints’ analysis but used for all other analyses. ED LOS exceeded 3560 minutes (~2.5 days) for one presentation and one case did not have a departure time recorded; these two cases were excluded from the LOS analysis but used for all other analyses. Three cases were excluded from age analysis due to potential recording inaccuracies (recorded ages exceeded 110 years). One case did not

Table 1 Definitions of study outcomes. Time to see an Emergency Department (ED) health care professional: Time of arrival until time patient is seen by a diagnosing health care professional (Arkun et al., 2010). ED length of stay (LOS): Time of ED arrival to time of ED departure (Liew et al., 2003). Number of Admissions: The number of patients admitted into other wards within the hospital or transferred to other facilities (Arkun et al., 2010). Other facilities include but are not limited to nursing homes, other hospitals or external clinics. Did not wait (DNW) rate: The number (and proportion) of patients who presented to ED and left before medical assessment was provided (Gilligan et al., 2009). Re-presentations to the ED: Presentations made by the same patient back to the same ED within 48 hours (Moore et al., 2007). National emergency access target (NEAT) compliance rates: The percentage of patients assessed and discharged or admitted to ward environments within 4 hours of presenting to the ED (Hodge et al., 2013). NEAT compliance rates stratified by Australasian Triage Scale (ATS): The percentage of patients assessed, discharged or admitted to ward environments within 4 hours of presenting to the ED, stratified by ATS. ATS is the standard triage assessment tool utilised by Registered Nurses (RNs) within Australian EDs (Hodge et al., 2013).

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have a discharge disposition recorded; this case was excluded from discharge disposition analysis. Significant differences between 2012 and 2013 data were noted in several baseline variables (age group, triage category, day of week, mode of arrival, diagnosis), indicating a patient change/confounding factor occurred between 2012 and 2013. Due to these annual differences, the research team primarily used the 2 week pre-PAT in 2013 as the key baseline (control) data (termed ‘standard care’). Ethical approval to conduct this study was obtained from Griffith University Human Research Ethics Committee (NRS/19/14/ HREC), the Hospital and Health Service HREC (HREC/13/QGC/187) and Queensland Health Research Ethics and Governance Unit to access public health data. 3. Results 3.1. Patient demographics Characteristics of the patient presentations made to the ED during the 2013 6 week focus time frame are shown in Table 2. The median age was 34 years (IQR 20–56). The largest proportion of patients was in the 18–64 years age category (62.6%). The proportion of presentations by males and females across the 3 time frames was similar (male 50.3% and female 49.7%). Around 3000 presentations were made in each 2-week period. 3.2. ED presentation characteristics Breakdown of the number of presentations to ED, stratified by acuity, day of presentation to ED, mode of arrival to ED and International Classification of Diseases (ICD) code are displayed in Table 3. ATS category three contained the highest proportion (52.4%) of presentations made during the 2013 study period (T1–T3). The proportion of ATS five category presentations was smaller during T2 (1.8%) and T3 (2.2%) than during T1 (2.5%). There were significant differences in the proportion of presentations during the week (P < 0.001). Mondays, Saturdays and Sundays recorded the largest number of presentations. In 2013 most patients self-presented to ED (61.9%). Around one third arrived by ambulance across all three time frames (see Table 3). Discharge diagnoses for both the 2012 and 2013 data were categorised according to the ED ICD-10 system (ICD, 2010), with trauma-related diagnoses being the most common.

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period to the next in 2013 (T1, 186 min; T2, 181 min; T3, 175 min). Those requiring hospital admission in 2013 had longer ED LOS than those not requiring hospital admission (Admitted: 234 min vs. Not admitted: 146 min). ED LOS of stay for not admitted patient presentations during 2013 decreased over time (T1, 151 min; T2, 146 min; T3, 140 min, T1 vs T2 P = 0.027; T1 vs T3 P < 0.001; T2 vs T3 P = 0.120), as did ED LOS for presentations requiring hospital admission (T1, 239 min; T2, 238 min; T3, 225 min, T1 vs T2 P = 0.034; T1 vs T3 P < 0.001; T2 vs T3 P < 0.001). When analysed in terms of ATS, the median ED LOS decreased for ATS category 2 and 3 patient presentations but not for ATS 1, 4 or 5 presentations. ED outcomes (NEAT compliance rates, re-presentation rates and discharge disposition) are presented in Table 5. Overall NEAT compliance rate (i.e. the proportion of presentations discharged from ED within 4 h) was higher in 2013 vs 2012 (2012, 53.3% vs 2013, 73.0%; P < 0.001). In 2013, NEAT compliance was higher in T3 compared with T1 by 4.2% (T3; 75.5% vs T1; 71.3%; P < 0.001) and also higher in T3 compared with T2 (T3, 75.5% vs T2, 72.0%; P = 0.003). NEAT compliance for admitted patients improved between 2012 and 2013 by 30% (P < 0.001). In 2013, NEAT improved for admitted patients between T1 and T3 (P < 0.001) and T2 and T3 (P < 0.001) but not between T1 and T2 (P = 0.568). NEAT compliance for not admitted patients also improved from 2012 to 2013 by 18.7% (P < 0.001). In 2013, NEAT improved for not admitted patients between T1 vs T2 (P = 0.049), T2 vs T3 (P = 0.047) and T1 vs T3 (P < 0.001). In terms of discharge disposition from ED, the proportion of patients admitted increased by 7.7% from 2012 to 2013. During 2013, admission rate increased 6.1% (T1, 34.1%; T2, 38.2% and T3, 40.2%). The 17,185 presentations to ED during the study period included a number of patients who re-presented to the ED. In 2012, a total of 6658 people made 8258 ED presentations; in 2013, 7687 people made 8927 presentations. The proportion of re-presentations within 48 h, is sometimes used as a proxy marker of quality of care (Trivedy and Cooke, 2013). During 2013 re-presentation rate within 48 h increased over time. Regarding discharge disposition, the proportion of patients who did not wait (DNW) to see a clinician decreased (i.e. improved) between 2012 and 2013 by 2.8% (2012, 9.4% vs 2013, 6.6%) and also during the introduction of the new care models in 2013 with most improvement seen between T1 and T2 (T1, 7.5%; T2, 6.2%; T3, 6.1%). 4. Discussion

3.3. Emergency department outcomes Outcomes of ED patient presentations (time to be seen, median LOS and ED LOS by ATS) across each time period are presented in Table 4. The median ED LOS decreased between 2012 and 2013 (2012; 225 min vs 2013, 181 min; P < 0.001) and from one key time

This study found that the introduction of two additional models of care, placement of a physician at triage (PAT) and PAT in addition to the opening of a medical assessment unit (MAU), improved some clinical outcomes in the ED, including time to be seen, ED length of stay (LOS), NEAT compliance and DNW rates. Other

Table 2 Demographics of ED patient presentations. ED Demographic

Median age (IQR) Age group 0–17 18–64 65+ Gender Male Female

2012

2013

2012 vs 2013

2012 vs 2013

2013

n = 8250 (%)

n = 8932 (%)

χ2; df

P value

T1 n = 3120 (%)

12.82; 2

0.103 0.002

34 (21–56) 1529 (18.5) 5266 (63.8) 1455 (17.6) n = 8252 (48.0) 4117 (49.9) 4135 (50.1)

33 (20–56) 1834 (20.5) 5491 (61.5) 1607 (18.0) n = 8932 (52.0) 4490 (50.3) 4442 (49.7)

0.245; 1

34 (20–55)

T2 n = 2624 (%) 34 (20–57)

T3 n = 3188 (%) 33 (19–56)

628 (20.1) 1956 (62.7) 536 (17.2)

509 (19.4) 1618 (61.7) 497 (18.9)

697 (21.9) 1917 (60.1) 574 (18.0)

1531 (49.1) 1589 (50.9)

1341 (50.1) 1283 (48.9)

1618 (50.8) 1570 (49.2)

0.621

T1 vs T2 P value

T1 vs T3 P value

T2 vs T3 P value

0.587 0.214

0.323 0.103

0.133 0.065

0.124

0.182

0.789

Abbreviations: df: degrees of freedom; n: number (subgroup of sample population); vs: versus; χ2: chi square; %: percentage; bivariate analysis was utilised to compare time frames.

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Characteristic

2013 n = 8932 (%)

83 (1.0) 1334 (16.2) 4474 (54.2) 2124 (25.7) 238 (2.9)

93 (1.0) 1571 (17.6) 4684 (52.4) 2387 (26.7) 197 (2.2)

1126 (13.6) 1198 (14.5) 1231 (14.9) 1154 (14.0) 1225 (14.8) 1222 (14.8) 1096 (13.3)

1423 (15.9) 1070 (12.0) 1043 (11.7) 1287 (14.4) 1291 (14.5) 1409 (15.8) 1409 (15.8)

5934 (71.9) 2318 (28.1)

6114 (68.5) 2818 (31.5)

2888 (35.0) 121 (1.5) 5238 (63.5) 5 (0.1)

3286 (36.8) 105 (1.2) 5532 (61.9) 9 (0.1)

2005 (24.3) 819 (9.9) 606 (7.3) 482 (5.8) 363 (4.4) 3977 (48.2)

2311 (25.9) 615 (6.9) 697 (7.8) 518 (5.8) 361 (4.0) 4430 (49.6)

χ2; df

17.10; 4

92.01; 6

24.50; 1

9.06; 3

55.96; 5

P value (2012 vs 2013)

2013 T1 n = 3120 (%)

T2 n = 2624 (%)

T3 n = 3188 (%)

26 (0.8) 560 (17.9) 1607 (51.5) 850 (27.2) 77 (2.5)

25 (1.0) 453 (17.3) 1396 (53.2) 702 (26.8) 48 (1.8)

42 (1.3) 558 (17.5) 1681 (52.7) 835 (26.2) 72 (2.3)

427 (13.7) 359 (11.5) 349 (11.2) 558 (17.9) 570 (18.3) 407 (13.0) 450 (14.4)

403 (15.4) 396 (15.1) 354 (13.5) 358 (13.6) 355 (13.5) 390 (14.9) 368 (14.0)

593 (18.6) 315 (9.9) 340 (10.7) 371 (11.6) 366 (11.5) 612 (19.2) 591 (18.5)

2263 (72.5) 857 (27.5)

1866 (71.1) 758 (28.9)

1985 (62.3) 1203 (37.7)

1121 (35.9) 34 (1.1) 1960 (62.8) 5 (0.2)

982 (37.4) 24 (0.9) 1617 (61.6) 1 (0.0)

1183 (37.1) 47 (1.5) 1955 (61.3) 3 (0.1)

831 (26.6) 249 (8.0) 236 (7.6) 165 (5.3) 127 (4.1) 1512 (48.5)

697 (26.6) 167 (6.4) 203 (7.7) 173 (6.6) 105 (4.0) 1279 (48.7)

783 (24.6) 199 (6.2) 258 (8.1) 180 (5.6) 129 (4.0) 1639 (51.4)

0.002

<0.001

<0.001

0.028

<0.001

T1 vs T2 P value

T1 vs T3 P value

T2 vs T3 P value

0.381

0.287

0.521

<0.001

<0.001

<0.001

0.233

<0.001

<0.001

0.300

0.317

0.221

0.095

0.023

0.244

Abbreviations: ATS: Australasian Triage Scale; DNW: did not wait; ICD: International Classification of Disease codes; QAS: Queensland Ambulance Service; QPS: Queensland Police Service; n: number (subgroup of sample population); vs: versus; χ2: chi square; %: percentage.

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ATS 1 (n = 176) 2 (n = 2905) 3 (n = 9158) 4 (n = 4511) 5 (n = 435) Day of the week totals Monday Tuesday Wednesday Thursday Friday Saturday Sunday Weekday/weekend Weekday Weekend Mode of arrival QAS QPS Self-presented Other ICD Trauma (Q65000-Q73160) DNW (Q32200) Cardiovascular (Q02000-Q02700) Gastrointestinal (Q17000-Q18270) Obstetric & Gynaecology (Q41000-Q41650) Other

2012 n = 8253 (%)

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Table 3 Characteristics of ED presentations.

Outcome

2012

2013

2012 vs 2013

2013

P value

T1

T2

T3

T1 vs T3 P value

T2 vs T3 P value

<0.001

<0.001

0.423

Median time to be seen (n) Median time to be seen in min (IQR)

n = 8210 27.00 (10.00–74.00)

n = 8895 25.00 (9.00–63.00)

<0.001

27.00 (10.00–70.00) (n = 3109)

Median ED LOS Median ED LOS – all patients min (IQR) Median ED LOS – not admitted patients min (IQR) Median ED LOS – admitted patients min (IQR) ED LOS divided by ATS

n = 8252 225.00 (126.00–372.00) 181.00 (105.00–291.00)

n = 8931 181.00 (108.00–254.00) 146.00 (87.00–215.00)

<0.001 <0.001

186.00 (113.00–267.00) 151.00 (89.25–224.00)

181.00 (109.00–262.00) 146.00 (86.00–211.00)

175.00 (103.00–239.00) 140.00 (83.00–205.00)

0.099 0.027

<0.001 <0.001

0.007 0.120

369.00 (243.00–541.75

234.00 (172.00–350.00)

<0.001

239.00 (184.00–369.00)

238.00 (177.00–365.00)

225.00 (153.00–320.00)

0.034

<0.001

<0.001

0.194

0.438

0.577

0.051

0.005

0.419

0.018

<0.001

0.010

0.278

0.832

0.194

0.585

0.465

0.913

ATS 1 Median ED LOS (IQR)

ATS 2 Median ED LOS (IQR)

ATS 3 Median ED LOS (IQR)

ATS 4 Median ED LOS (IQR)

ATS 5 Median ED LOS (IQR)

n = 83

n = 93

237.00 (154.00–343.00)

198.00 (125.00–259.00)

n = 1334

n = 1571

304.00 (189.00–449.00)

210.00 (147.00–290.00)

n = 4473

n = 4683

250.00 (149.00–401.00)

199.00 (127.00–284.00)

n = 2124

n = 2387

150.50 (87.00–255.00)

128.00 (74.00–203.00)

n = 238

n = 197

97.00 (47.00–188.50)

79.00 (38.00–127.00)

23.00 (8.00–59.00) (n = 2615)

24.00 (9.00–60.00) (n = 3171)

n = 26

n = 25

n = 42

0.018

198.00 (161.75–285.50)

161.00 (102.00–231.50)

201.00 (125.00–280.25)

n = 560

n = 453

n = 558

<0.001

216.50 (159.00–319.00)

210.00 (146.00–278.00

203.00 (140.00–277.25)

n = 1607

n = 1395

n = 1681

<0.001

<0.001

0.008

211.00 (135.00–298.00)

196.00 (126.00–292.00)

191.00 (120.00–261.00)

n = 850

n = 702

n = 835

129.00 (74.75–199.00)

132.00 (76.00–205.25

122.00 (70.00–203.00)

n = 77

n = 48

n = 72

85.00 (41.50–127.50)

70.00 (38.25–138.75)

83.00 (34.00–113.00)

Abbreviations: ATS: Australasian Triage Scale, ED: emergency department; IQR: interquartile range; LOS: length of stay; n: sample number (subgroup of sample population).

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T1 vs T2 P value

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Table 4 Emergency Department Outcomes: Time to see clinician and ED LOS.

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ARTICLE IN PRESS 0.271 <0.001 0.013

0.003 0.047 <0.001 <0.001 <0.001 <0.001 0.538 0.049 0.568

1656 (51.9) 1281 (40.2) 194 (6.1) 36 (1.1) 19 (0.6) 2 (0.1)

2406 (75.5) 1652 (86.6) 754 (58.9) n = 2835 (88.9) n = 178 (5.6)

1406 (53.6) 1002 (38.2) 162 (6.2) 40 (1.5) 10 (0.4) 4 (0.2)

1889 (72.0) 1366 (84.3) 523 (52.2) n = 2219 (84.6) n = 125 (4.8)

n = 8932 4814 (53.9) 3347 (37.5) 590 (6.6) 125 (1.4) 48 (0.5) 8 (0.1) n = 8251 4800 (58.2) 2456 (29.8) 777 (9.4) 129 (1.6) 79 (1.0) 10 (0.1)

143.46; 5

<0.001

1752 (56.2) 1064 (34.1) 234 (7.5) 49 (1.6) 19 (0.6) 2 (0.1)

2224 (71.3) 1682 (81.8) 542 (50.9) n = 2633 (84.4) n = 128 (4.1) <0.001 <0.001 < 0.001 719.87; 1 521.14; 1 527.51; 1 6519 (73.0) 4700 (84.2) 1819 (54.3) n = 7687 (86.1) n = 431 (4.8) 4396 (53.3) 3799 (65.5) 596 (24.3) n = 6658 (80.7) n = 396 (4.8)

NEAT compliance (n) NEAT compliance – all patient presentations (%) NEAT compliance – Not admitted (%) NEAT compliance – admitted (%) Number of people (% of total presentations) Number of re-presentations within 48 h of discharge (% of total presentations) Discharge disposition Home, usual residence (%) Admitted (%) DNW (%) Transfer to another facility (%) Died in ED (%) Capacity alert beds (over census beds) (%)

T2 vs T3 P value T1 vs T3 P value T1 vs T2 P value T3 n = 3188 (%) T2 n = 2624 (%) T1 n = 3120 (%)

2013

2012 vs 2013 P value 2012 vs 2013 χ2; df 2013 2012 Outcome

Table 5 Emergency Department Outcomes: NEAT compliance, re-presentations, discharge disposition.

Due to missing data, analysis based on: n = 8252 (2012) and n = 8931 (2013) presentations for NEAT compliance rates; analysis based on: n = 8252 (2012) and n = 8932 (2013) presentations for Discharge disposition. Abbreviations: df: degree of freedom; DNW: did not wait; ED: emergency department; IQR: interquartile range; LOS: length of stay; n: number (subgroup of sample population); NEAT: National Emergency Access Targets; %: percentage; χ2: chi square.

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outcomes, such as re-presentation rate and admission rate, did not improve. This suggests that the care models (particularly when bundled together) were valuable and effective for enhancing some aspects of throughput of patients in this ED. The results of the study reflected the general trend reported in the literature of increasing numbers of presentations to EDs, typically by patients with increasing clinical acuity, increasingly by ambulance and with a range of ICD conditions (Crilly et al., 2014). This upwards trend confirms that ED patient load remains an important and ongoing clinical issue (Braitberg, 2007; Derlet and Richards, 2000; Jayaprakash et al., 2009) particularly for admitted patients (Crilly et al., 2011) and so alternate and supplemental models of care need to continually be considered. Incorporating a physician in the triage process improved (i.e. reduced) ED LOS compared with standard care. Although this was not a statistically significant difference in ED LOS, this apparent reduction in ED LOS can be of clinical importance in promoting ED throughput. This finding reflects the results of previous studies (Bernstein et al., 2009; Han et al., 2010; Imperato et al., 2012; Terris et al., 2004; Travers and Lee, 2006) where ED LOS was also reduced by 11 min–60 min with the introduction of a PAT process. Variations in the length of ED LOS seen amongst studies may relate to how the PAT model was staffed. In this study, medical staff were drawn from the existing medical officer pool. Larger reductions in ED LOS using a PAT model were typically reported with the use of an additional medical officer (Imperato et al., 2012). Another, less reported on but useful, indicator of effective service delivery is re-presentation rate, as patients returning to the ED for further care or assessment places additional pressure on the system by increasing patient volume, occupancy levels (Goldman et al., 2006) and admissions rate (Sauvin et al., 2013). Re-presentation rate in this study was reflective of other Australian literature (at around 5%) (Robinson and Lam, 2013) and international figures ranging from 5.5% (Wu et al., 2010) to 6.8% (Cardin et al., 2003). The reduction in patients who did not wait to be treated (by 1.3%) following the introduction of the PAT model was comparable with two other studies that also report significant decreases in DNW (1.5%: Cheng et al., 2013 and 1.1%; Shetty et al., 2012) following the implementation of PAT-type models of care. The study conducted by Cheng et al. (2013) also included an extra nurse in the model of care to carry out activities such as medication and laboratory orders, therefore potentially impacting on the overall DNW rate. This finding is important, given that DNW rate is one of several key performance indicators used by the Queensland government and hospital administrators to measure the efficiency of an ED (Queensland Government, n.d.). The implementation of the MAU, 2 weeks after the PAT had been in operation, provided an opportunity to understand the effect of this ‘bundled system’. Some further improvements occurred when this combined PATplusMAU model was operational. The PATplusMAU model positively impacted patient flow and throughput, for patients of moderately higher acuity (ATS 2) and for those requiring hospital admission. Overall ED LOS was reduced by an additional 6 min. There was also a further modest increase in NEAT compliance, for both non-admitted and admitted patients. Previous research has indicated improvements in outcomes such as ED LOS and the number of patients waiting to be transferred from ED to hospital wards following the implementation of MAUs; however, such study did not present specific time differences (Scott et al., 2009). Government documents also note that introduction of an MAU reduces ED LOS; however, these also do not record the size or proportion of those time savings (NSW Health, n.d.). Absolute ED LOS was examined and was also stratified by ATS category. Given the acuity associated with an ATS 1 presentation, it seemed less likely that such care models would impact on the LOS of these acutely unwell patients. Indeed, ATS category 3

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patients, who made up the majority of the presenting population across all three time frames, showed the most substantial improvement in ED LOS (see Table 4). Although the PAT system alone also reduced ED LOS for ATS category 3 patients (see Table 4), the most noticeable reduction in ED LOS occurred following the introduction of the PATplusMAU model of care. This suggests that although PAT is beneficial in reducing ED LOS, PATplusMAU is able to achieve further reduction in ED LOS and improve ED flow. Further evaluation of the impact of MAU on ED LOS at other health care facilities could help to inform the body of knowledge around this outcome in a wider context. Previous studies have identified that MAUs decrease general hospital admissions by as much as 15% (Henley et al., 2006). Under the PATplusMAU model of care (T3) hospital admissions increased by 6.1% from standard care (T1). In this study no differentiation was made between hospital ward admissions or MAU admission when analysing the data; all admission data were combined and analysed as one. This may be one reason for this difference in admission rate between studies. There was an improvement in the time until patients were seen by a clinician after presenting to the ED with the implementation of the PATplusMAU (see Table 4). The opening of the MAU saw this outcome reduce by a median of 3 min (T1 compared with T3). Although this may seem small, across 6308 patients, it is an important flow regulator. The proportion of patients who DNW also decreased following the implementation of the PATplusMAU. The majority of the literature currently discusses the impact of MAU on hospital admission and occupancy levels (Moloney et al., 2006), hospital LOS (Henley et al., 2006; Yoon et al., 2003), patient wait times (FitzGerald et al., 2010) and the overall cost effectiveness of MAU (Henley et al., 2006), ED LOS (Scott et al., 2009) and time to specialist medical review (Conway et al., 2014) as opposed to time to be seen in the ED and ED DNW rates. The reduction in the median time to be seen and the decreased proportion of DNW patient presentations shown in this study suggests that the MAU was effective in terms of improving patient flow. However, further investigation of the impact on MAU alone, on health service outcomes such as time to be seen, ED wait times and the proportion of patients who DNW is warranted to ensure continued improvements in health service delivery. 5. Limitations There were several limitations with this study. This study was retrospective in nature and was reliant on the accuracy of each clinician’s prospective data entry. The models of care evaluated in this study were implemented during the southern hemisphere summer months (i.e. January and February) and in a mixed adult/paediatric ED; thus seasonal influences may have impacted on this study’s findings but were not considered. Given this, undertaking larger scale analysis covering broader time frames would be beneficial. Furthermore, there was no adjustment for other potentially confounding factors. The use of advanced analytic techniques such as regression analysis and modelling can enhance the interrogation of complex data such as these, to optimise the use of prospectively collected data as others have done previously (Considine et al., 2006; Sakr et al., 2003). Although re-presentation rates for each twoweek time frame were determined manually, the flow-on effect into the 48 hours of the following time frames was not considered in the analysis (i.e. the number of re-presentations during that time). This was due to complications around coding and re-coding of the data. To gain a more accurate picture of the impact of PAT and PATplusMAU on re-presentation rates, these time frames should also be specifically considered in future studies. The broader generalisability of this study’s findings is constrained by these limitations.

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6. Conclusion Given the plethora of literature available indicating the deleterious impacts of ED crowding on patient outcomes, improving access and throughput is important, with patient safety remaining paramount. Hospitals remain under pressure to meet performance targets. Findings from this study indicated that both the PAT and, to a further extent, PATplusMAU, are viable models of care that can improve patient flow outcomes such as NEAT, particularly for patients requiring hospital admission. Conflict of interest None declared. References Arkun, A., Briggs, W.M., Patel, S., Datillo, P.A., Bove, J., Birkhahn, R.H., 2010. Emergency department crowding: factors influencing flow. Western Journal of Emergency Medicine. 11, 10–15. Asplin, B.R., Magid, D.J., Rhodes, K.V., Solberg, L.I., Lurie, N., Camargo, C.A., Jr., 2003. A conceptual model of emergency department crowding. Annals of Emergency Medicine. 42, 173–180. Austalian Institute of Health and Welfare, 2014. WELFARE, A. I. O. H. A. (Ed.), Australian Hospital Statistics 2013–14: Emergency Department Care. Health Services Series no. 58. Cat. no. HSE 153. AIHW, Canberra. Australian Institute of Health and Welfare, 2013. Australian Hospital Statistics 2012–13 Emergency Department Care. Health Services Series no. 52. Cat. no. HSE 142. AIHW, Canberra. Bell, A., Crilly, J., Williams, G., Wylie, K., Toloo, G.S., Burke, J., et al., 2014. Funding emergency care: Australian style. Emergency Medicine Australasia. 26, 408–410. Bernstein, S.L., Aronsky, D., Duseja, R., Epstein, S.K., Handel, D.A., Hwang, U., et al., 2009. The effect of emergency department crowding on clinically oriented outcomes. Academic Emergency Medicine. 16, 1–10. Braitberg, G., 2007. Emergency department overcrowding: dying to get in? The Medical Journal of Australia. 187, 624–625. Cardin, S., Afilalo, M., Lang, E., Collet, J.P., Colacone, A., Tselios, C., et al., 2003. Intervention to decrease emergency department crowding: does it have an effect on return visits and hospital readmissions? Annals of Emergency Medicine. 41, 173–185. Cheng, I., Lee, J., Mittmann, N., Tyberg, J., Ramagnano, S., Kiss, A., et al., 2013. Implementing wait-time reductions under Ontario government benchmarks (Pay-for-Results): a Cluster Randomized Trial of the Effect of a Physician-Nurse Supplementary Triage Assistance team (MDRNSTAT) on emergency department patient wait times. BMC Emergency Medicine. 13, 17. Considine, J., Martin, R., Smit, D.V., Winter, C., Jenkins, J., 2006. Emergency nurse practitioner care and emergency department patient flow: case–control study. Emergency Medicine Australasia. 18, 385–390. Conway, R., O’riordan, D., Silke, B., 2014. Long-term outcome of an AMAU – a decade’s experience. QJM: Monthly Journal of the Association of Physicians. 107, 43–49. Crilly, J., Keijzers, G., Krahn, D., Steele, M., Green, D., Freeman, J., 2011. The impact of a temporary medical ward closure on Emergency Department and hospital service delivery outcomes. Quality Management in Health Care. 20 (4), 322–333. Crilly, J., Keijzers, G.B., Tippett, V.C., O’dwyer, J.A., Wallis, M.C., Lind, J.F., et al., 2014. Expanding emergency department capacity: a multisite study. Australian Health Review. 38, 278–287. Derlet, R.W., Richards, J.R., 2000. Overcrowding in the nation’s emergency departments: complex causes and disturbing effects. Annals of Emergency Medicine. 35, 63–68. Ducharme, J., Alder, R.J., Pelletier, C., Murray, D., Tepper, J., 2009. The impact on patient flow after the integration of nurse practitioners and physician assistants in 6 Ontario emergency departments. CJEM: Canadian Journal of Emergency Medical Care. 11, 455–461. FitzGerald, G., Jelinek, G.A., Scott, D., Gerdtz, M., 2010. Emergency department triage revisited. Emergency Medicine Journal. 27, 86–92. Gilligan, P., Joseph, D., Winder, S., Keffee, F.O., Oladipo, O., Ayodele, T., et al., 2009. DNW—“Did Not Wait” or “Demographic Needing Work”: a study of the profile of patients who did not wait to be seen in an Irish emergency department. Emergency Medicine Journal. 26, 780–782. Gold Coast City Council (2011) Gold coast city census population highlights [Online]. http://www.goldcoast.qld.gov.au/documents/bf/gold-coast-census-2011.pdf. Goldman, R.D., Ong, M., Macpherson, A., 2006. Unscheduled return visits to the pediatric emergency department-one-year experience. Pediatric Emergency Care. 22, 545–549. Guttmann, A., Schull, M., Vermeulen, M.J., Stukel, T.A., 2011. Association between waiting times and short term mortality and hospital admission after departure from emergency department: population based cohort study from Ontario, Canada. BMJ (Clinical Research Ed.). 342, d2983. Han, J.H., France, D.J., Levin, S.R., Jones, I., Storrow, A.B., Aronsky, D., 2010. The effect of physician triage on emergency department length of stay. The Journal of Emergency Medicine. 39, 227–233.

Please cite this article in press as: Elizabeth Elder, Amy N.B. Johnston, Julia Crilly, Improving emergency department throughput: An outcomes evaluation of two additional models of care, International Emergency Nursing (2015), doi: 10.1016/j.ienj.2015.07.001

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E. Elder et al./International Emergency Nursing ■■ (2015) ■■–■■

Henley, J., Bennett, C., Williamson, J., Scott, I., 2006. Standards for Medical Assessment and Planning Units in Public and Private Hospitals. Internal Medicine Society of Australia and New Zealand. Hodge, A., Hugman, A., Varndell, W., Howes, K., 2013. A review of the quality assurance processes for the Australasian Triage Scale (ATS) and implications for future practice. Australasian Emergency Nursing Journal : AENJ. 16, 21–29. Holroyd, B.R., Bullard, M.J., Latoszek, K., Gordon, D., Allen, S., Tam, S., et al., 2007. Impact of a triage liaison physician on emergency department overcrowding and throughput: a randomized controlled trial. Academic Emergency Medicine. 14, 702–708. ICD (2010) International Classification of Disease and Related Health Problems (10th Revision) [Online]. http://apps.who.int/classifications/icd10/browse/2010/en#/V accessed 15/09/2014. Imperato, J., Morris, D.S., Binder, D., Fischer, C., Patrick, J., Sanchez, L.D., et al., 2012. Physician in triage improves emergency department patient throughput. Internal and Emergency Medicine. 7, 457–462. Jayaprakash, N., O’sullivan, R., Bey, T., Ahmed, S.S., Lotfipour, S., 2009. Crowding and delivery of healthcare in emergency departments: the European perspective. The Western Journal of Emergency Medicine. 10, 233–239. Li, J.Y., Yong, T.Y., Bennett, D.M., O’brien, L.T., Roberts, S., Hakendorf, P., et al., 2010. Outcomes of establishing an acute assessment unit in the general medical service of a tertiary teaching hospital. The Medical Journal of Australia. 192, 384–387. Liew, D., Kennedy, M.P., 2003. Emergency department length of stay independently predicts excess inpatient length of stay. Medical Journal of Australia. 179, 524–526. Lowthian, J.A., Curtis, A.J., Jolley, D., Stoelwinder, J.U., McNeil, J.J., Cameron, P.A., 2012. Demand at the emergency department front door: 10-year trends in presentations. The Medical Journal of Australia. 196, 128–132. McNeill, G.B., Brahmbhatt, D.H., Prevost, A.T., Trepte, N.J.B., 2009. What is the effect of a consultant presence in an acute medical unit? Clinical Medicine. 9, 214–218. McNeill, G.B., Brand, C., Clark, K., Jenkins, G., Scott, I., Thompson, C.H., et al., 2011. Optimizing care for acute medical patients: the Australasian Medical Assessment Unit Survey. Internal Medicine Journal. 41, 19–26. Moloney, E.D., Bennett, K., O’riordan, D., Silke, B., 2006. Emergency department census of patients awaiting admission following reorganisation of an admissions process. Emergency Medicine Journal. 23, 363–367. Moore, G., Gerdtz, M., Manias, E., Hepworth, G., Dent, A., 2007. Socio-demographic and clinical characteristics of re-presentation to an Australian inner-city emergency department: implications for service delivery. BMC Public Health. 7, 320. NSW Health, n.d. Medical Assessment Unit: Operational Guide. Audience: General Managers, Service Managers, Clinical Managers. Patel, H., Celenza, A., Watters, T., 2012. Effect of nurse initiated X-rays of the lower limb on patient transit time through the emergency department. Australasian Emergency Nursing Journal: AENJ. 15, 229–234.

Queensland Government (n.d.) Emergency Department Indicator Descriptions [Online]. http://www.performance.health.qld.gov.au/hospitalperformance/ed -indicators.aspx?hospital=99999 accessed 27/10/2014. Robinson, K., Lam, B., 2013. Early emergency department representations. Emergency Medicine Australasia. 25, 140–146. Rowe, B.H., Guo, X., Villa-Roel, C., Schull, M., Holroyd, B.R., Bullard, M.J., et al., 2011. The role of triage liaison physicians on mitigating overcrowding in emergency departments: a systematic review. Academic Emergency Medicine. 18, 111– 120. Sakr, M., Kendall, R., Angus, J., Saunders, A., Nicholl, J., Wardrope, J., 2003. Emergency nurse practitioners: a three part study in clinical and cost effectiveness. Emergency Medicine Journal. 20, 158–163. Sauvin, G., Freund, Y., Saidi, K., Riou, B., Hausfater, P., 2013. Unscheduled return visits to the emergency department: consequences for triage. Academic Emergency Medicine. 20, 33–39. Scott, I., Vaughan, L., Bell, D., 2009. Effectiveness of acute medical units in hospitals: a systematic review. International Journal for Quality in Health Care: Journal of the International Society for Quality in Health Care. 21, 397–407. Shetty, A., Gunja, N., Byth, K., Vukasovic, M., 2012. Senior Streaming Assessment Further Evaluation after Triage zone: a novel model of care encompassing various emergency department throughput measures. Emergency Medicine Australasia. 24, 374–382. SPSS, 2014. Statistics for Windows [Computer Software], Version 22. SPSS Inc., Chicago, IL. Sullivan, C.M., Staib, A., Flores, J., Aggarwal, L., Scanlon, A., Martin, J.H., et al., 2014. Aiming to be NEAT: safely improving and sustaining access to emergency care in a tertiary referral hospital. Australian Health Review. 38 (5), 564–574. Terris, J., Leman, P., O’connor, N., Wood, R., 2004. Making an IMPACT on emergency department flow: improving patient processing assisted by consultant at triage. Emergency Medicine Journal. 21, 537–541. Travers, J., Lee, F., 2006. Avoiding prolonged waiting time during busy periods in the emergency department: is there a role for the senior emergency physician in triage? European Journal of Emergency Medicine. 13, 342. Trivedy, C.R., Cooke, M.W., 2013. Unscheduled return visits (URV) in adults to the emergency department (ED): a rapid evidence assessment policy review. Emergency Medicine Journal. 32 (4), 324–329. Wiler, J.L., Gentle, C., Halfpenny, J.M., Heins, A., Mehrotra, A., Mikhail, M.G., et al., 2010. Optimizing emergency department front-end operations. Annals of Emergency Medicine. 55, 142–160. Wu, C.-L., Wang, F.-T., Chiang, Y.-C., Chiu, Y.-F., Lin, T.-G., Fu, L.-F., et al., 2010. Unplanned emergency department revisits within 72 hours to a secondary teaching referral hospital in Taiwan. The Journal of Emergency Medicine. 38, 512–517. Yoon, P., Steiner, I.P., Reinhardt, G., 2003. Analysis of factors influencing length of stay in the emergency department. CJEM: Canadian Journal of Emergency Medical Care. 5, 155.

Please cite this article in press as: Elizabeth Elder, Amy N.B. Johnston, Julia Crilly, Improving emergency department throughput: An outcomes evaluation of two additional models of care, International Emergency Nursing (2015), doi: 10.1016/j.ienj.2015.07.001