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Emergency Medicine Australasia (2014) 26, 549–555
doi: 10.1111/1742-6723.12300
ORIGINAL RESEARCH
Clearing emergency departments and clogging wards: National Emergency Access Target and the law of unintended consequences Marlon L PERERA,1 Alexander W DAVIES,1 Neiraja GNANESWARAN,1 Marian GILES,1 Danny LIEW,2 Peter RITCHIE3 and Steven TF CHAN1,4 1 Department of Surgery, Western Health, Melbourne, Victoria, Australia, 2Department of Statistics, Western Health, Melbourne, Victoria, Australia, 3Emergency Department, Western Health, Melbourne, Victoria, Australia, and 4Academic Surgery, The University of Melbourne, Melbourne, Victoria, Australia
Abstract Objective: To assess ED length of stay (EDLOS), access block, inpatient length of stay (IPLOS) and waiting times before and after the implementation of the National Emergency Access Target (NEAT). Methods: This was designed as a retrospective cohort study and data was collected from electronic patient management systems. The control group represented all emergency presentations between June 2011 and September 2011, 1 year prior to the introduction of NEAT. The study groups were assessed and included all ED presentations between June and September 2012 and 2013 respectively. Main outcome measures were waiting times, EDLOS, proportion of patients cleared from the ED within NEAT goals, hospital length of stay and hospital mortality rates. Results: A cumulative total of 76 935 patients were included in the study. During the course of the study, clearance from the ED within NEAT targets rose from 49.0% to 53.2% [relative
risk (RR) 1.09; 95% CI, 1.07–1.11; P < 0.001]. ED waiting times decreased from 1.05 h [interquartile range (IQR), 0.43–2.27] to 0.45 h (IQR, 0.17– 1.22) (P < 0.001) and time from bedrequest to ward access increased. Utilisation of emergency short stay units (SSU) increased significantly across the study period from 6.5% to 13.4% (P < 0.001). Rates of inpatient transfers increased eightfold (RR, 7.93; 95% CI, 5.98–10.51; P < 0.001) and IPLOS increased by 21% from 2.05 (IQR, 0.75–4.96) to 2.50 days (IQR, 1.12–4.99) (P < 0.001). Hospital mortality remained unchanged from 3.0% to 3.3% (RR, 1.10; 95% CI, 0.91–1.34; P = 0.311). Conclusions: At the current institution NEAT success has been guarded, likely secondary to availability of inpatient beds. The implementation of NEAT appears to have reduced emergency waiting times. These early results suggest concurrent a detrimental effect on IPLOS; however, some of this effect may be a result of a large increase in short stay admissions.
Correspondence: Dr Marlon L Perera, Department of Surgery, Western Hospital, Footscray, VIC 3012, Australia. Email: [email protected] Marlon L Perera, MBBS, BMedSci, Surgical Registrar; Alexander W Davies, MBBS, BMedSci, Resident Medical Officer; Neiraja Gnaneswaran, MBBS, BMedSci, Resident Medical Officer; Marian Giles, MBBS, BMedSci, Resident Medical Officer; Danny Liew, PhD, FRACP, Chair of Clinical Epidemiology; Peter Ritchie, MBBS, FACEM, Director of Emergency Medicine; Steven TF Chan, MBBS, PhD, FRACS, Professor of Surgery. Accepted 19 August 2014
Key findings • NEAT implementation was associated with reduced ED waiting times and access block. • Following NEAT, an increased number of admissions and utilisation of short stay units was observed. • Detrimental downstream effects including increased inter-unit transfers and prolonged inpatient length of stay were observed in the study groups.
Key words: crowding, emergency service, health services accessibility, length of stay.
Introduction Background EDs across Victoria face an increasing number of presentations from the community each year.1 ED overcrowding remains as a prominent issue, defined as the ‘situation where ED function is impeded primarily because the number of patients waiting to be seen, undergoing assessment and treatment or waiting for departure exceeds either the physical bed and/or staffing capacity of the emergency department’.2,3 The adverse consequences of overcrowding in EDs have been discussed extensively in the literature, demonstrating a significant association with poorer patient outcomes.4–9
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Overcrowding and associated adverse outcomes within Australian EDs have increased steadily over the past decade10 and in 2008, and estimated 1500 deaths per year Australia-wide were attributable to an overcrowded ED and access block.11 This finding was supported by Sprivulis et al. who reported an association with increased mortality and a composite measure of hospital and ED overcrowding.7 In 2000, the ‘4 h target’ was implemented in the National Health Service (NHS) in UK as an intervention to curb waiting periods and ‘inappropriate trolley waits’ for assessment and admission.12 Over the past decade, this has produced significant results, both negative and positive. Initial results of the NHS scheme were hailed as an ‘exemplary success’, most notably the timeliness of care in UK EDs.13 The Australian government introduced a similar scheme, the National Emergency Access Target (NEAT), to address the growing demand on Australia’s EDs. The NEAT’s objectives are of clinical service redesign, with the overall goal of improving patient flow through congested EDs to both decrease overcrowding and improve the quality of patient care.6–8,14–17 The NEAT report states that a patient’s ED length of stay (EDLOS) be kept under 4 h, with the EDLOS defined as the time of first presentation to the time of physical ED separation – by way of discharge, transfer or admission.14 In 2012, Victorian hospitals initially aimed for 72% of presentations to be cleared within the recommended 4 h, a target that was significantly higher than actual reported results of 66% 4 h clearance rates.18 The intended NEAT targets are increasing incrementally to 90% by 2015.14 A strict 90% cut-off has been decided as it should allow for the decision-making process to factor in clinical and non-clinical aspects in patient care, and provide a ‘buffer’ category for individuals who are most appropriate to be kept within the ED.19 As there are many factors for overcrowding in EDs, including patient complexity and a lack of inpatient beds,2,20 the initial government report outlining NEAT goals suggested an institutional change rather than an
isolated departmental change to reach the targets. Unfortunately, the introduction of this initiative has been marred by controversy – Australia’s NEAT are consistent with the UK’s ‘4 h rules’ that arrived at 4 h cut-off times without empirical evidence suggesting their efficacy. However, it should be noted that recently the British government has drastically modified the NHS ED targets for ‘lack of clinical justification’.21
Goals of this investigation Current accepted key performance indicators (KPIs) of ED accessibility include: EDLOS, proportion of patients cleared from the ED within NEAT goals, inpatient length of stay (IPLOS) and hospital mortality rates. In this study, we aim to assess the effects of NEAT on these ED KPIs and subsequent inpatient outcomes at a large metropolitan tertiary centre.
Methods Study design and setting We undertook a retrospective cohort study at Western Health, Victoria, Australia. Western Health represents a large peripheral health network, servicing a culturally diverse population of greater than 800 000. The health network has two primary facilities with large EDs at the Sunshine and Footscray Campuses. The Footscray and Sunshine EDs are exposed to roughly 35 000 and 65 000 emergency presentations per year respectively. Western Health has the availability of most surgical and medical subspecialities, intensive care and obstetrics. NEAT was implemented formally on 3 June 2012, and thus the current study reflects early outcomes of the government initiative. Six months prior to the implementation of NEAT, hospital-wide education processes were taken to increase awareness of the initiative. No major structural, staffing or administrative changes were made prior to the implementation.
Methods and outcome measures Data were collected from the two EDs at Western Health, based at the
Footscray and Sunshine campuses. Three time periods were defined: pre NEAT implementation (Control period) and the post NEAT implementation (Study period 1). A further study period was collected 1 year following the introduction of the scheme (Study period 2). The Control period involved all ED presentations to the two Western Health EDs from 3 June to 3 September 2011, while Study period 1 and 2 involved all ED presentations from 3 June to 3 September 2012 and 2013 respectively. The 3 month duration of sample periods was selected arbitrarily. During the study periods, no other large-scale administrative changes were made within the health-care network. Following ethical approval (HREC QA2012.58), data was extracted through electronic medical records (EMR) and patient management systems (iPM, Computer Sciences Corporation, UK). Variables collected included patient demographics (age and gender), emergency episode data (presentation time, wait time, time to physician, time to admission, time to emergency separation and location of emergency separation) and associated inpatient data (inpatient team, transfer of treating team, discharge date, discharge location and mortality). Data were extracted to Microsoft Excel 2003 (Microsoft Corporation, Redmond, WA, USA). All terminology is used in accordance with the Australasian College for Emergency Medicine recommendations.3 Wait time (WT) was considered the time between initial presentation and review by a doctor. EDLOS was defined as the time between initial presentation to the time of physical emergency separation – whether discharged, transferred or admitted to inpatient wards. ‘Access block’ was defined as the percentage of patients requiring admission that failed access to an inpatient bed within 8 h of presentation. Admission-toward delay time (AWT) was defined as the time between bed request in the ED and the time of arrival on the accepting ward. At the current institution, bed request was only made on review and formal ‘acceptance’ by a treating team. IPLOS was defined as the time between formal admission and
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IMPACT OF THE NATIONAL EMERGENCY ACCESS TARGETS
TABLE 1.
Comparison of sample groups with patient demographics and basic ED episode information
Emergency Episode Data
Mean age Female Male Campus 1 Campus 2 ED Triage category 1 – Resuscitation 2 – Emergency 3 – Urgent 4 – Semi-Urgent 5 – Non Urgent 6 – DOA Emergency Separation Direct Admit Admit via SSU SSU Direct DC LOR Death EDLOS 0–4 h EDLOS >4 h
Control Group (2011)
Study Group 1 (2012)
Study Group 2 (2013)
40.0 (SD 26.4) 12 455 49.7% 12 615 50.3% 16 055 64.0% 9015 36.0%
41.1 (SD 26.6) 12 544 50.1% 12 481 49.9% 16 106 64.4% 8919 35.6%
40.1 (SD 26.8) 13 304 49.6% 13 536 50.4% 17 606 65.6% 9234 34.4%
85 2358 8557 12 359 1655 56
0.4% 9.4% 34.1% 49.3% 6.6% 0.2%
105 2504 8486 12 148 1728 54
0.4% 10.0% 33.9% 48.6% 6.9% 0.2%
110 2529 8781 13 427 1947 46
0.4% 9.4% 32.7% 50.0% 7.3% 0.2%
5993 496 1638 14 136 2787 20 12 279 12 791
23.9% 2.0% 6.5% 56.4% 11.1% 0.1% 49.0% 51.0%
5651 743 2334 13 442 2823 32 12 515 12 510
22.6% 3.0% 9.3% 53.7% 11.3% 0.1% 50.0% 50.0%
5958 618 3608 14 138 2483 35 14 292 12 548
22.2% 2.3% 13.4% 52.7% 9.3% 0.1% 53.2% 46.8%
P = 0.07 P = 0.51 P = 0.46 P = 0.76
P < 0.001
P < 0.001
DC, discharge; DOA, dead on arrival; EDLOS, ED length of stay; LOR, left against medical advice; SD, standard deviation; SSU, short stay unit.
separation from inpatient status, whether discharged or transferred. Patients admitted through the short stay unit (SSU) were not analysed as inpatient data unless they were subsequently admitted to the ward by an accepting team. Mortality data was collected for patients that were admitted through the ED and subsequently died during the single episode.
Statistical analysis Statistical analysis was completed on SPSS v20 (SPSS Inc, Chicago, IL, USA). All data was expressed in medians and interquartile range (IQR) unless otherwise specified. Continuous variables including waiting time, EDLOS, AWT and IPLOS were tested for distribution and deemed to be non-parametric. The Mann–Whitney U-test was used for comparing continuous variables relative risk (RR) calculated for estimates of proportions. Mantel-Haenszel linear-by-linear χ2 regression was used to examine the weekly trends of NEAT success rates and access block.
Results Characteristics of study subjects In total, 76 935 patients presented to the ED across all sample periods. Patient demographics and emergency episode data for each sample group are summarised in Table 1. There was no statistical difference between groups with respect to episode number, patient age, patient gender or emergency campus. Compared with the Control period, median WT was decreased in the study period 1 from 1.05 h (IQR, 0.43– 2.27) to 0.38 h (IQR, 0.13–1.13) (P < 0.001). This decline in the reduced waiting time was sustained in the following year, where median WT was 0.45 (IQR, 0.17–1.22). The reduction in the waiting time was reflected in a minor decline in the median EDLOS. From the control period in 2011, median EDLOS declined from 4.08 h (IQR 2.37– 6.88) to 4.00 h (IQR 2.30–6.82) in 2012 and finally 3.77 h (IQR 2.18–
6.42) in 2013 (P < 0.001). In the Control period, 49.0% of patients were cleared from the ED within the NEAT targets, compared with 50.0% of the 2012 data (RR, 1.02; 95% CI, 1.00–1.04; P = 0.05) and 53.2% in 2013 (RR 1.09; 95% CI, 1.07–1.11; P < 0.001). Over the 9 months of collected data, the proportions of ED presentations that met NEAT targets trended upwards significantly (P < 0.001) (Fig. 1). There was also no change in the frequency of access block in the early period following the implementation of NEAT, with overall proportions of 38.4% and 37.1% (RR, 0.97; 95% CI, 0.94– 1.00; P = 0.16) in the 2011 and 2012 periods, respectively. In 2013, there was a significant decline in the rates of access block, reducing to 24.0% (RR 0.81; 95% CI, 0.80– 0.83, P < 0.001). During the overall period of the study, the downward trend in access block reached statistical significance (P < 0.001) (Fig. 1). These results are summarised in Table 2.
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TABLE 2.
ML PERERA ET AL.
Comparison of sample groups process times Control Group
WT (h) EDLOS (h) AWT (h) IPLOS (days)
Study Group 1 (2012)
Study Group 2 (2013)
Median
IQR
Median
IQR
Median
IQR
1.05 4.08 1.75 2.05
(0.43–2.27) (2.37–6.88) (0.00–3.32) (0.75–4.96)
0.38 4.00 2.47 2.78
(0.13–1.13) (2.30–6.82) (1.40–3.85) (1.24–5.68)
0.45 3.77 2.60 2.50
(0.17–1.22) (2.18–6.42) (0.47–3.15) (1.12–4.99)
P value P < 0.001 P < 0.001 P < 0.001 P < 0.001
AWT, admission-to-ward delay time; EDLOS, ED length of stay; IPLOS, inpatient length of stay; IQR, interquartile range; WT, wait time.
Figure 1. (a) Proportion of patients cleared from ED within NEAT guidelines per week of each study period. Horizontal dotted line indicates current Victoria guide (b) Proportion of access block per week of each study period. GroupCat: ( ), 2011; ( ), 2012; ( ), 2013.
Disposition from the ED altered during the course of the study period. Increasing proportion of patients were transferred to SSUs, with proportions increasing from 6.5% to 15.7% (P < 0.001) over the course of the study. For patients admitted from the ED, time between initial review by the emergency team and bed request time increased during the study period from 1.35 h (IQR 0.86–2.65) to 2.05 h (IQR 1.02–3.28) in 2013 (P < 0.001). These patients experienced increased AWT, with a median AWT of 1.75 h (IQR 0.00–3.32) in 2011 to 2.60 h (IQR 0.47–3.15) in 2013 (P < 0.001). Of the admitted patients that were separated from the ED outside the NEAT targets, 45% experienced an AWT delay time of greater than 4 h.
Among patients who were admitted, the number of inpatient interunit transfers (among treating teams) within 48 h increased from 0.84% in the Control period to 7.1% in the 2012 period (RR, 7.93; 95% CI, 5.98– 10.51; P < 0.001). The increased rates of inpatient transfers remained elevated in the 2013 data. Median IPLOS also increased by 36% from 2.05 (IQR, 0.75–4.96) to 2.78 days (IQR, 1.24–5.68) (P < 0.001) in 2012. The elevated IPLOS remained significantly elevated in 2013, with median IPLOS of 2.50 (IQR 1.12–4.99) (P < 0.001). The progressive increase in median IPLOS following the introduction of NEAT reached statistical significance (Fig. 2). Inpatient mortality was unchanged over the periods
2011, 2012 and 2013: 3.3%, 3.7% and 3.7% respectively (RR, 1.13; 95% CI, 0.94–1.35; P = 0.18). Mortality rates as a proportion of admitted patients and patients transferred to SSU remained unchanged 2.61%, 2.74% and 2.39% respectively (RR 0.91; 95% CI, 0.76–1.10, P = 0.33). Inpatient outcomes are summarised in Table 3.
Discussion Our study examined early outcomes following the implementation of NEAT at two major Victorian EDs. Our results suggest a modest improvement in emergency episode times and KPIs. The introduction of NEAT to our institution appears to be associated
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IMPACT OF THE NATIONAL EMERGENCY ACCESS TARGETS
Figure 2.
Median IPLOS (days). GroupCat: (
with a detrimental effect on ‘downstream’ inpatient KPIs. Our early results of ED KPIs appear promising, with a significant 64% reduction in waiting time was observed following NEAT implementation. Unfortunately, Australian literature under the new scheme is sparse and the only comparable series arise from literature surrounding the UK’s 4 h rule. These studies are equivocal as to whether 4 h access targets actually improve time to seeing a clinician,4,22 despite the UK’s higher 4 h rule access targets. In our study sample, overall achievement of NEAT was achieved in 53% compared with 49% in the Control period, but the clinical significance of this change must be questioned. It should be noted NEAT’s successful implementation requires whole-hospital engagement,14,19 as ED separation is a function of ED efficiency and availability of inpatient beds. The current study illustrated the effect of delayed admissions to wards, accounting for 45% of patients that failed NEAT targets. During the study period, there was an increase in time to bed request and AWT. This change highlights a change in mentality of the emergency team, suggesting earlier referral to admitting teams and the potential of bed availability of inpatient beds obstructing emergency
), 2011; (
), 2012; (
), 2013.
separation. Thus, while a departmental change in practice can be observed in emergency key indicators, this result certainly is not reflected in NEAT success rates. Geelhoed and de Klerk8 and Maumill et al.23 reported the first Australian series under the 4 h scheme. Unfortunately, no early results of 4 h clearance rates have been published to date. However, it was observed that in only two of the three tertiary centres studied, access block dropped from 34% to 24% in the initial 3 months following the implementation of a similar rule. This suggests that these early findings may not be the best predictor of long term success. An interesting observation is that data analysed on the NHS’ 4 h rules suggested that UK hospitals also struggled to meet 4 h access targets. The ‘downstream’ effects of the NEAT have become apparent during the course of the current study. Rates of inpatient transfer-of-care and median IPLOS rose. However, it should be noted that SSU admission was not considered an inpatient episode for analytical purposes and thus, increased deposition to SSU from ED may inpart account for the increased IPLOS. The increased utilisation of SSU may have allowed patients of lower acuity and reduced IPLOS may have been treated in SSU rather than admitted to
the ward, suggesting a variation in patient complexity between control and study groups. Regardless, Geelhoed and de Klerk suggested that rushed care, decreased time for relevant investigations and inappropriate referrals are all possible consequences of a target-based approach to clinical decision-making.8 Our data suggest a significant increase in ‘rushed referrals’, with an eightfold increase in inpatient transfer of treating team within 48 h of presentation. Admissions under inappropriate teams may represent suboptimal care. The concept of impaired patient care has been voiced publicly by the Royal Australasian College of Surgeons, suggesting that emphasis may be placed on ED clinical flow rather than patient care.24 Mason et al. reported a large retrospective series under the NHS scheme and found a gross increase in patient disposition within 20 min prior to the 4 h target.4 They concluded that this was an unintended side effect of the access target, and that, ‘EDs are performing to the targets, but may not (be) improv(ing) overall care’.4 Inpatient mortality showed no significant change following the implementation of NEAT – a finding that is conflicting to recent literature. Geelhoed and de Klerk8 observed a significant reduction in mortality rates in the context of a 4 h rule and the drop in mortality was correlated with drop in the frequency of access block. However, the early results from that aforementioned study were not reflective of the overall drop over 24 months. The reported reduced mortality has been disputed, as the analysis did not take into account the increased number of admissions and resulting disproportionate increase in numbers of relatively well patients.25 As such, it has been suggested that outcome measures should focus on the risk of unexpected death.26 The findings of the current study report an increase in inpatient mortality, a finding that did not reach statistical significance. Increased admissions to the SSU during the study periods suggest variations in the cohort of admitted patients. This may potentially result in disproportionate numbers of relatively sicker patients admitted to the wards in the study periods and thus the
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TABLE 3.
Comparison of admitted patients and subsequent inpatient outcomes
Inpatient episode data
Admissions Mean age Female Male Campus 1 Campus 2 ED priority of admissions 1 – Resuscitation 2 – Emergency 3 – Urgent 4 – Semi-Urgent 5 – Non Urgent Inpatient Separation Home Death LOR Transfer to other
Control Group (2011)
Study Group 1 (2012)
Study Group 2 (2013)
6489 53.3 (SD 27.4) 3090 47.6% 3399 52.4% 3377 52.0% 3112 48.0%
6394 55.0 (SD 27.1) 3057 47.8% 3337 52.2% 3198 50.0% 3196 50.0%
6576 54.5 (SD 26.3) 3136 47.7% 3440 52.3% 3393 51.6% 3183 48.4%
66 1094 2950 2234 89
1.01% 16.9% 45.5% 34.4% 1.4%
67 1257 2838 2100 78
1.04% 19.7% 44.4% 32.8% 1.2%
70 1161 2861 2263 108
1.06% 17.7% 43.5% 34.4% 1.6%
4981 212 90 1206
76.7% 3.3% 1.4% 18.6%
5071 239 74 1010
79.3% 3.7% 1.2% 15.8%
5130 243 78 1125
78.0% 3.7% 1.2% 17.1%
P = 0.02 P = 0.59 P = 0.71 P = 0.63
P = 0.13
LOR, left against medical advice; SD, standard deviation.
cohorts may not be directly comparable. Data surrounding comorbidities, such as Charlson scores,27 and unexpected death was not available and represents a limitation to the current study. There are several other limitations to our study. There are inherent flaws related to the observational and retrospective nature of the study and as such. The current study represents the experience of a single institution and thus the inferred focal effects of NEAT are based on weak evidence of possible causes. While no large-scale administrative changes were made during the study periods within the health network, it is difficult to quantify the effect of smaller local administrative changes on inpatient outcome measures. Study of a single institution also acts as a considerable strength, allowing scrutiny on microsystems and departments within the single institution. Data surrounding complexity of patients, unexpected mortality and hospital occupancy was not available for analysis and thus their respective impact on inpatient outcome measures was not assessed. Finally, longer control and study periods would be beneficial to the study methodology to account for seasonal or yearly variations in activity. Our results suggest
further, more comprehensive assessment is warranted. In conclusion, our study suggests the implementation of the NEAT requires a system change rather than an isolated departmental change for full effect. While the 4 h rule in other Australian institutions has resulted in positive outcomes, the results from our institution are not so optimistic. We have observed an improvement in waiting times and emergency length of stay. These findings have been associated with significant ‘downstream’ effects including an increased IPLOS; however, the increase in utilisation of SSU utilisation must be taken into consideration. Further, more in depth research is required to further explore these findings.
3.
4.
5.
Competing interests None declared.
6.
References 1. Lowthlan JA, Curtis AJ, Jolley DJ et al. Demand at the emergency department front door: 10-year trends in presentations. Med. J. Aust. 2012; 196: 128–32. 2. Fatovich DM, Hirsch RL. Entry overload, emergency department
7.
overcrowding, and ambulance bypass. Emerg. Med. J. 2003; 20: 406–9. Australasian College for Emergency Medicine. Australasian College for Emergency Medicine. Policy Document: Standard Terminology. Canberra: Australian Government Department of Health and Ageing, 2009. Mason S, Weber EJ, Coster J et al. Time patients spend in the emergency department: England’s 4-hour rule – a case of hitting the target but missing the point? Ann. Emerg. Med. 2011; 59: 341–9. Guttman A, Schul MJ, Vermeulen MJ et al. Association between waiting times and short term mortality and hospital admission after departure from emergency department: population based cohort study from Ontario, Canada. BMJ 2011; 342: d2983. Richardson DB. Increase in patient mortality at 10 days associated with emergency department overcrowding. Med. J. Aust. 2006; 184: 213– 16. Sprivulis PC, Da Silva J, Jacobs IG et al. The association between hospital overcrowding and mortality among patients admitted via Western Australian emergency departments. Med. J. Aust. 2006; 184: 208–12.
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8. Geelhoed GC, de Klerk NH. Emergency department overcrowding, mortality and the 4-hour rule in Western Australia. Med. J. Aust. 2012; 196: 122–6. 9. Singer AJ, Thode HC, Viccellio P et al. The association between length of emergency department boarding and mortality. Acad. Emerg. Med. 2011; 18: 1324–9. 10. Australian Government Department of Health and Ageing. The state of our public hospitals, June 2006. [Cited Sep 2012.] Available from URL: http://apo.org.au/node/8488 11. Forero R, Hillman K. Acess block and overcrowding: a literature review Australasian College for Emergency Medicine – Access Block Solutions Summit: University of New South Wales/The Simpson Centre for Health Services Research, 2008; 26. 12. Department of Health, UK. The NHS Plan: a plan for investment, a plan for reform. 2000. [Cited Sep 2014.] Available from URL: http://dera .ioe.ac.uk/4423/1/04055783.pdf 13. Alberti G. Transforming Emergency Care in England. London: Department of Health, 2004. [Cited Aug 2012.] Available from URL: http:// www.dh.gov.uk/en/Publicationsand statistics/Publications/Publications PolicyAndGuidance/DH_4091775 14. Emergency Care Institute of New South Wales. National Emergency Access Targets. Sydney 2012. [Cited Aug 2012.] Available from URL: http://www.ecinsw.com.au/neat 15. Forero R, McCarthy S, Hillman K. Access block and emergency
16.
17.
18.
19.
20.
21.
department overcrowding. Crit. Care 2011; 15: 216–22. Department of Health, Western Australia. Taking pressure off public hospitals (2008–2013). 2009. [Cited Aug 2012.] Available from URL: http:// www.federalfinancialrelations . gov . au / content / npa/health _payments/workforce-reform/ relieving-pressure/WA_IP.pdf Stokes B. Four Hour Rule Program Progress and Issues Review. Perth: Government of Western Australia, Australian Government Department of Health and Ageing, 2011. [Cited Sep 2012.] Available from URL: http://www.health.wa.gov.au/ publications/documents/Four HourRule_Review_Stokes.pdf Emergency Care Institute, NSW. NEAT – The Basics. August 2012. [Cited Sep 2012.] Available from URL: http://www.ecinsw.com.au/ NEAT-the-basics Baggoley C, Owler B, Grigg M et al. Expert panel – review of elective surgery and emergency access targets under the national partnership agreement on improving public hospital services – report to the Council of Australian Governments. Department of Health and Aging, 2011; 1–64. Derlet RW, Richards JR. Overcrowding in the nation’s emergency departments: complex causes and disturbing effects. Ann. Emerg. Med. 2000; 35: 63–8. Topping A, Campbell D. Waiting targets for accident and emergency to
22.
23.
24.
25.
26.
27.
be scrapped. The Guardian 10 June 2010. Jones P, Schimanski K. The four hour target to reduce emergency department ‘waiting time’: a systematic review of clinical outcomes. Emerg. Med. Australas. 2010; 22: 391– 8. Maumill L, Zlc M, Esson AA et al. The National Emergency Access Target (NEAT): can quality go with timeliness? Med. J. Aust. 2013; 198: 153–7. Royal Australasian College of Surgeons. Submission to the council of Australian Governments Expert Panel: four hour national access target in emergency departments. May 2011. [Cited Aug 2012.] Available from URL: https://www.surgeons.org/ media/307099/sbm_2011-05-24 _four_hour_rule.pdf Mitra B, Cameron PA. Emergency department overcrowding and mortality after the introduction of the 4-hour rule in Western Australia. Med. J. Aust. 2012; 196: 499, author reply 500. Mitra B, Cameron PA, Archer P et al. The association between time to disposition plan in the emergency department and in-hospital mortality of general medical patients. Intern. Med. J. 2012; 42: 444–50. Charlson ME, Pompei P, Ales KL et al. A new method of classifying prognostic comorbidity in longitudinal studies: development and validation. J. Chronic Dis. 1987; 40: 373–83.
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Emergency Medicine Australasia (2014) 26, 549–555
doi: 10.1111/1742-6723.12300
ORIGINAL RESEARCH
Clearing emergency departments and clogging wards: National Emergency Access Target and the law of unintended consequences Marlon L PERERA,1 Alexander W DAVIES,1 Neiraja GNANESWARAN,1 Marian GILES,1 Danny LIEW,2 Peter RITCHIE3 and Steven TF CHAN1,4 1 Department of Surgery, Western Health, Melbourne, Victoria, Australia, 2Department of Statistics, Western Health, Melbourne, Victoria, Australia, 3Emergency Department, Western Health, Melbourne, Victoria, Australia, and 4Academic Surgery, The University of Melbourne, Melbourne, Victoria, Australia
Abstract Objective: To assess ED length of stay (EDLOS), access block, inpatient length of stay (IPLOS) and waiting times before and after the implementation of the National Emergency Access Target (NEAT). Methods: This was designed as a retrospective cohort study and data was collected from electronic patient management systems. The control group represented all emergency presentations between June 2011 and September 2011, 1 year prior to the introduction of NEAT. The study groups were assessed and included all ED presentations between June and September 2012 and 2013 respectively. Main outcome measures were waiting times, EDLOS, proportion of patients cleared from the ED within NEAT goals, hospital length of stay and hospital mortality rates. Results: A cumulative total of 76 935 patients were included in the study. During the course of the study, clearance from the ED within NEAT targets rose from 49.0% to 53.2% [relative
risk (RR) 1.09; 95% CI, 1.07–1.11; P < 0.001]. ED waiting times decreased from 1.05 h [interquartile range (IQR), 0.43–2.27] to 0.45 h (IQR, 0.17– 1.22) (P < 0.001) and time from bedrequest to ward access increased. Utilisation of emergency short stay units (SSU) increased significantly across the study period from 6.5% to 13.4% (P < 0.001). Rates of inpatient transfers increased eightfold (RR, 7.93; 95% CI, 5.98–10.51; P < 0.001) and IPLOS increased by 21% from 2.05 (IQR, 0.75–4.96) to 2.50 days (IQR, 1.12–4.99) (P < 0.001). Hospital mortality remained unchanged from 3.0% to 3.3% (RR, 1.10; 95% CI, 0.91–1.34; P = 0.311). Conclusions: At the current institution NEAT success has been guarded, likely secondary to availability of inpatient beds. The implementation of NEAT appears to have reduced emergency waiting times. These early results suggest concurrent a detrimental effect on IPLOS; however, some of this effect may be a result of a large increase in short stay admissions.
Correspondence: Dr Marlon L Perera, Department of Surgery, Western Hospital, Footscray, VIC 3012, Australia. Email: [email protected] Marlon L Perera, MBBS, BMedSci, Surgical Registrar; Alexander W Davies, MBBS, BMedSci, Resident Medical Officer; Neiraja Gnaneswaran, MBBS, BMedSci, Resident Medical Officer; Marian Giles, MBBS, BMedSci, Resident Medical Officer; Danny Liew, PhD, FRACP, Chair of Clinical Epidemiology; Peter Ritchie, MBBS, FACEM, Director of Emergency Medicine; Steven TF Chan, MBBS, PhD, FRACS, Professor of Surgery. Accepted 19 August 2014
Key findings • NEAT implementation was associated with reduced ED waiting times and access block. • Following NEAT, an increased number of admissions and utilisation of short stay units was observed. • Detrimental downstream effects including increased inter-unit transfers and prolonged inpatient length of stay were observed in the study groups.
Key words: crowding, emergency service, health services accessibility, length of stay.
Introduction Background EDs across Victoria face an increasing number of presentations from the community each year.1 ED overcrowding remains as a prominent issue, defined as the ‘situation where ED function is impeded primarily because the number of patients waiting to be seen, undergoing assessment and treatment or waiting for departure exceeds either the physical bed and/or staffing capacity of the emergency department’.2,3 The adverse consequences of overcrowding in EDs have been discussed extensively in the literature, demonstrating a significant association with poorer patient outcomes.4–9
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Overcrowding and associated adverse outcomes within Australian EDs have increased steadily over the past decade10 and in 2008, and estimated 1500 deaths per year Australia-wide were attributable to an overcrowded ED and access block.11 This finding was supported by Sprivulis et al. who reported an association with increased mortality and a composite measure of hospital and ED overcrowding.7 In 2000, the ‘4 h target’ was implemented in the National Health Service (NHS) in UK as an intervention to curb waiting periods and ‘inappropriate trolley waits’ for assessment and admission.12 Over the past decade, this has produced significant results, both negative and positive. Initial results of the NHS scheme were hailed as an ‘exemplary success’, most notably the timeliness of care in UK EDs.13 The Australian government introduced a similar scheme, the National Emergency Access Target (NEAT), to address the growing demand on Australia’s EDs. The NEAT’s objectives are of clinical service redesign, with the overall goal of improving patient flow through congested EDs to both decrease overcrowding and improve the quality of patient care.6–8,14–17 The NEAT report states that a patient’s ED length of stay (EDLOS) be kept under 4 h, with the EDLOS defined as the time of first presentation to the time of physical ED separation – by way of discharge, transfer or admission.14 In 2012, Victorian hospitals initially aimed for 72% of presentations to be cleared within the recommended 4 h, a target that was significantly higher than actual reported results of 66% 4 h clearance rates.18 The intended NEAT targets are increasing incrementally to 90% by 2015.14 A strict 90% cut-off has been decided as it should allow for the decision-making process to factor in clinical and non-clinical aspects in patient care, and provide a ‘buffer’ category for individuals who are most appropriate to be kept within the ED.19 As there are many factors for overcrowding in EDs, including patient complexity and a lack of inpatient beds,2,20 the initial government report outlining NEAT goals suggested an institutional change rather than an
isolated departmental change to reach the targets. Unfortunately, the introduction of this initiative has been marred by controversy – Australia’s NEAT are consistent with the UK’s ‘4 h rules’ that arrived at 4 h cut-off times without empirical evidence suggesting their efficacy. However, it should be noted that recently the British government has drastically modified the NHS ED targets for ‘lack of clinical justification’.21
Goals of this investigation Current accepted key performance indicators (KPIs) of ED accessibility include: EDLOS, proportion of patients cleared from the ED within NEAT goals, inpatient length of stay (IPLOS) and hospital mortality rates. In this study, we aim to assess the effects of NEAT on these ED KPIs and subsequent inpatient outcomes at a large metropolitan tertiary centre.
Methods Study design and setting We undertook a retrospective cohort study at Western Health, Victoria, Australia. Western Health represents a large peripheral health network, servicing a culturally diverse population of greater than 800 000. The health network has two primary facilities with large EDs at the Sunshine and Footscray Campuses. The Footscray and Sunshine EDs are exposed to roughly 35 000 and 65 000 emergency presentations per year respectively. Western Health has the availability of most surgical and medical subspecialities, intensive care and obstetrics. NEAT was implemented formally on 3 June 2012, and thus the current study reflects early outcomes of the government initiative. Six months prior to the implementation of NEAT, hospital-wide education processes were taken to increase awareness of the initiative. No major structural, staffing or administrative changes were made prior to the implementation.
Methods and outcome measures Data were collected from the two EDs at Western Health, based at the
Footscray and Sunshine campuses. Three time periods were defined: pre NEAT implementation (Control period) and the post NEAT implementation (Study period 1). A further study period was collected 1 year following the introduction of the scheme (Study period 2). The Control period involved all ED presentations to the two Western Health EDs from 3 June to 3 September 2011, while Study period 1 and 2 involved all ED presentations from 3 June to 3 September 2012 and 2013 respectively. The 3 month duration of sample periods was selected arbitrarily. During the study periods, no other large-scale administrative changes were made within the health-care network. Following ethical approval (HREC QA2012.58), data was extracted through electronic medical records (EMR) and patient management systems (iPM, Computer Sciences Corporation, UK). Variables collected included patient demographics (age and gender), emergency episode data (presentation time, wait time, time to physician, time to admission, time to emergency separation and location of emergency separation) and associated inpatient data (inpatient team, transfer of treating team, discharge date, discharge location and mortality). Data were extracted to Microsoft Excel 2003 (Microsoft Corporation, Redmond, WA, USA). All terminology is used in accordance with the Australasian College for Emergency Medicine recommendations.3 Wait time (WT) was considered the time between initial presentation and review by a doctor. EDLOS was defined as the time between initial presentation to the time of physical emergency separation – whether discharged, transferred or admitted to inpatient wards. ‘Access block’ was defined as the percentage of patients requiring admission that failed access to an inpatient bed within 8 h of presentation. Admission-toward delay time (AWT) was defined as the time between bed request in the ED and the time of arrival on the accepting ward. At the current institution, bed request was only made on review and formal ‘acceptance’ by a treating team. IPLOS was defined as the time between formal admission and
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TABLE 1.
Comparison of sample groups with patient demographics and basic ED episode information
Emergency Episode Data
Mean age Female Male Campus 1 Campus 2 ED Triage category 1 – Resuscitation 2 – Emergency 3 – Urgent 4 – Semi-Urgent 5 – Non Urgent 6 – DOA Emergency Separation Direct Admit Admit via SSU SSU Direct DC LOR Death EDLOS 0–4 h EDLOS >4 h
Control Group (2011)
Study Group 1 (2012)
Study Group 2 (2013)
40.0 (SD 26.4) 12 455 49.7% 12 615 50.3% 16 055 64.0% 9015 36.0%
41.1 (SD 26.6) 12 544 50.1% 12 481 49.9% 16 106 64.4% 8919 35.6%
40.1 (SD 26.8) 13 304 49.6% 13 536 50.4% 17 606 65.6% 9234 34.4%
85 2358 8557 12 359 1655 56
0.4% 9.4% 34.1% 49.3% 6.6% 0.2%
105 2504 8486 12 148 1728 54
0.4% 10.0% 33.9% 48.6% 6.9% 0.2%
110 2529 8781 13 427 1947 46
0.4% 9.4% 32.7% 50.0% 7.3% 0.2%
5993 496 1638 14 136 2787 20 12 279 12 791
23.9% 2.0% 6.5% 56.4% 11.1% 0.1% 49.0% 51.0%
5651 743 2334 13 442 2823 32 12 515 12 510
22.6% 3.0% 9.3% 53.7% 11.3% 0.1% 50.0% 50.0%
5958 618 3608 14 138 2483 35 14 292 12 548
22.2% 2.3% 13.4% 52.7% 9.3% 0.1% 53.2% 46.8%
P = 0.07 P = 0.51 P = 0.46 P = 0.76
P < 0.001
P < 0.001
DC, discharge; DOA, dead on arrival; EDLOS, ED length of stay; LOR, left against medical advice; SD, standard deviation; SSU, short stay unit.
separation from inpatient status, whether discharged or transferred. Patients admitted through the short stay unit (SSU) were not analysed as inpatient data unless they were subsequently admitted to the ward by an accepting team. Mortality data was collected for patients that were admitted through the ED and subsequently died during the single episode.
Statistical analysis Statistical analysis was completed on SPSS v20 (SPSS Inc, Chicago, IL, USA). All data was expressed in medians and interquartile range (IQR) unless otherwise specified. Continuous variables including waiting time, EDLOS, AWT and IPLOS were tested for distribution and deemed to be non-parametric. The Mann–Whitney U-test was used for comparing continuous variables relative risk (RR) calculated for estimates of proportions. Mantel-Haenszel linear-by-linear χ2 regression was used to examine the weekly trends of NEAT success rates and access block.
Results Characteristics of study subjects In total, 76 935 patients presented to the ED across all sample periods. Patient demographics and emergency episode data for each sample group are summarised in Table 1. There was no statistical difference between groups with respect to episode number, patient age, patient gender or emergency campus. Compared with the Control period, median WT was decreased in the study period 1 from 1.05 h (IQR, 0.43– 2.27) to 0.38 h (IQR, 0.13–1.13) (P < 0.001). This decline in the reduced waiting time was sustained in the following year, where median WT was 0.45 (IQR, 0.17–1.22). The reduction in the waiting time was reflected in a minor decline in the median EDLOS. From the control period in 2011, median EDLOS declined from 4.08 h (IQR 2.37– 6.88) to 4.00 h (IQR 2.30–6.82) in 2012 and finally 3.77 h (IQR 2.18–
6.42) in 2013 (P < 0.001). In the Control period, 49.0% of patients were cleared from the ED within the NEAT targets, compared with 50.0% of the 2012 data (RR, 1.02; 95% CI, 1.00–1.04; P = 0.05) and 53.2% in 2013 (RR 1.09; 95% CI, 1.07–1.11; P < 0.001). Over the 9 months of collected data, the proportions of ED presentations that met NEAT targets trended upwards significantly (P < 0.001) (Fig. 1). There was also no change in the frequency of access block in the early period following the implementation of NEAT, with overall proportions of 38.4% and 37.1% (RR, 0.97; 95% CI, 0.94– 1.00; P = 0.16) in the 2011 and 2012 periods, respectively. In 2013, there was a significant decline in the rates of access block, reducing to 24.0% (RR 0.81; 95% CI, 0.80– 0.83, P < 0.001). During the overall period of the study, the downward trend in access block reached statistical significance (P < 0.001) (Fig. 1). These results are summarised in Table 2.
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TABLE 2.
ML PERERA ET AL.
Comparison of sample groups process times Control Group
WT (h) EDLOS (h) AWT (h) IPLOS (days)
Study Group 1 (2012)
Study Group 2 (2013)
Median
IQR
Median
IQR
Median
IQR
1.05 4.08 1.75 2.05
(0.43–2.27) (2.37–6.88) (0.00–3.32) (0.75–4.96)
0.38 4.00 2.47 2.78
(0.13–1.13) (2.30–6.82) (1.40–3.85) (1.24–5.68)
0.45 3.77 2.60 2.50
(0.17–1.22) (2.18–6.42) (0.47–3.15) (1.12–4.99)
P value P < 0.001 P < 0.001 P < 0.001 P < 0.001
AWT, admission-to-ward delay time; EDLOS, ED length of stay; IPLOS, inpatient length of stay; IQR, interquartile range; WT, wait time.
Figure 1. (a) Proportion of patients cleared from ED within NEAT guidelines per week of each study period. Horizontal dotted line indicates current Victoria guide (b) Proportion of access block per week of each study period. GroupCat: ( ), 2011; ( ), 2012; ( ), 2013.
Disposition from the ED altered during the course of the study period. Increasing proportion of patients were transferred to SSUs, with proportions increasing from 6.5% to 15.7% (P < 0.001) over the course of the study. For patients admitted from the ED, time between initial review by the emergency team and bed request time increased during the study period from 1.35 h (IQR 0.86–2.65) to 2.05 h (IQR 1.02–3.28) in 2013 (P < 0.001). These patients experienced increased AWT, with a median AWT of 1.75 h (IQR 0.00–3.32) in 2011 to 2.60 h (IQR 0.47–3.15) in 2013 (P < 0.001). Of the admitted patients that were separated from the ED outside the NEAT targets, 45% experienced an AWT delay time of greater than 4 h.
Among patients who were admitted, the number of inpatient interunit transfers (among treating teams) within 48 h increased from 0.84% in the Control period to 7.1% in the 2012 period (RR, 7.93; 95% CI, 5.98– 10.51; P < 0.001). The increased rates of inpatient transfers remained elevated in the 2013 data. Median IPLOS also increased by 36% from 2.05 (IQR, 0.75–4.96) to 2.78 days (IQR, 1.24–5.68) (P < 0.001) in 2012. The elevated IPLOS remained significantly elevated in 2013, with median IPLOS of 2.50 (IQR 1.12–4.99) (P < 0.001). The progressive increase in median IPLOS following the introduction of NEAT reached statistical significance (Fig. 2). Inpatient mortality was unchanged over the periods
2011, 2012 and 2013: 3.3%, 3.7% and 3.7% respectively (RR, 1.13; 95% CI, 0.94–1.35; P = 0.18). Mortality rates as a proportion of admitted patients and patients transferred to SSU remained unchanged 2.61%, 2.74% and 2.39% respectively (RR 0.91; 95% CI, 0.76–1.10, P = 0.33). Inpatient outcomes are summarised in Table 3.
Discussion Our study examined early outcomes following the implementation of NEAT at two major Victorian EDs. Our results suggest a modest improvement in emergency episode times and KPIs. The introduction of NEAT to our institution appears to be associated
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Figure 2.
Median IPLOS (days). GroupCat: (
with a detrimental effect on ‘downstream’ inpatient KPIs. Our early results of ED KPIs appear promising, with a significant 64% reduction in waiting time was observed following NEAT implementation. Unfortunately, Australian literature under the new scheme is sparse and the only comparable series arise from literature surrounding the UK’s 4 h rule. These studies are equivocal as to whether 4 h access targets actually improve time to seeing a clinician,4,22 despite the UK’s higher 4 h rule access targets. In our study sample, overall achievement of NEAT was achieved in 53% compared with 49% in the Control period, but the clinical significance of this change must be questioned. It should be noted NEAT’s successful implementation requires whole-hospital engagement,14,19 as ED separation is a function of ED efficiency and availability of inpatient beds. The current study illustrated the effect of delayed admissions to wards, accounting for 45% of patients that failed NEAT targets. During the study period, there was an increase in time to bed request and AWT. This change highlights a change in mentality of the emergency team, suggesting earlier referral to admitting teams and the potential of bed availability of inpatient beds obstructing emergency
), 2011; (
), 2012; (
), 2013.
separation. Thus, while a departmental change in practice can be observed in emergency key indicators, this result certainly is not reflected in NEAT success rates. Geelhoed and de Klerk8 and Maumill et al.23 reported the first Australian series under the 4 h scheme. Unfortunately, no early results of 4 h clearance rates have been published to date. However, it was observed that in only two of the three tertiary centres studied, access block dropped from 34% to 24% in the initial 3 months following the implementation of a similar rule. This suggests that these early findings may not be the best predictor of long term success. An interesting observation is that data analysed on the NHS’ 4 h rules suggested that UK hospitals also struggled to meet 4 h access targets. The ‘downstream’ effects of the NEAT have become apparent during the course of the current study. Rates of inpatient transfer-of-care and median IPLOS rose. However, it should be noted that SSU admission was not considered an inpatient episode for analytical purposes and thus, increased deposition to SSU from ED may inpart account for the increased IPLOS. The increased utilisation of SSU may have allowed patients of lower acuity and reduced IPLOS may have been treated in SSU rather than admitted to
the ward, suggesting a variation in patient complexity between control and study groups. Regardless, Geelhoed and de Klerk suggested that rushed care, decreased time for relevant investigations and inappropriate referrals are all possible consequences of a target-based approach to clinical decision-making.8 Our data suggest a significant increase in ‘rushed referrals’, with an eightfold increase in inpatient transfer of treating team within 48 h of presentation. Admissions under inappropriate teams may represent suboptimal care. The concept of impaired patient care has been voiced publicly by the Royal Australasian College of Surgeons, suggesting that emphasis may be placed on ED clinical flow rather than patient care.24 Mason et al. reported a large retrospective series under the NHS scheme and found a gross increase in patient disposition within 20 min prior to the 4 h target.4 They concluded that this was an unintended side effect of the access target, and that, ‘EDs are performing to the targets, but may not (be) improv(ing) overall care’.4 Inpatient mortality showed no significant change following the implementation of NEAT – a finding that is conflicting to recent literature. Geelhoed and de Klerk8 observed a significant reduction in mortality rates in the context of a 4 h rule and the drop in mortality was correlated with drop in the frequency of access block. However, the early results from that aforementioned study were not reflective of the overall drop over 24 months. The reported reduced mortality has been disputed, as the analysis did not take into account the increased number of admissions and resulting disproportionate increase in numbers of relatively well patients.25 As such, it has been suggested that outcome measures should focus on the risk of unexpected death.26 The findings of the current study report an increase in inpatient mortality, a finding that did not reach statistical significance. Increased admissions to the SSU during the study periods suggest variations in the cohort of admitted patients. This may potentially result in disproportionate numbers of relatively sicker patients admitted to the wards in the study periods and thus the
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TABLE 3.
Comparison of admitted patients and subsequent inpatient outcomes
Inpatient episode data
Admissions Mean age Female Male Campus 1 Campus 2 ED priority of admissions 1 – Resuscitation 2 – Emergency 3 – Urgent 4 – Semi-Urgent 5 – Non Urgent Inpatient Separation Home Death LOR Transfer to other
Control Group (2011)
Study Group 1 (2012)
Study Group 2 (2013)
6489 53.3 (SD 27.4) 3090 47.6% 3399 52.4% 3377 52.0% 3112 48.0%
6394 55.0 (SD 27.1) 3057 47.8% 3337 52.2% 3198 50.0% 3196 50.0%
6576 54.5 (SD 26.3) 3136 47.7% 3440 52.3% 3393 51.6% 3183 48.4%
66 1094 2950 2234 89
1.01% 16.9% 45.5% 34.4% 1.4%
67 1257 2838 2100 78
1.04% 19.7% 44.4% 32.8% 1.2%
70 1161 2861 2263 108
1.06% 17.7% 43.5% 34.4% 1.6%
4981 212 90 1206
76.7% 3.3% 1.4% 18.6%
5071 239 74 1010
79.3% 3.7% 1.2% 15.8%
5130 243 78 1125
78.0% 3.7% 1.2% 17.1%
P = 0.02 P = 0.59 P = 0.71 P = 0.63
P = 0.13
LOR, left against medical advice; SD, standard deviation.
cohorts may not be directly comparable. Data surrounding comorbidities, such as Charlson scores,27 and unexpected death was not available and represents a limitation to the current study. There are several other limitations to our study. There are inherent flaws related to the observational and retrospective nature of the study and as such. The current study represents the experience of a single institution and thus the inferred focal effects of NEAT are based on weak evidence of possible causes. While no large-scale administrative changes were made during the study periods within the health network, it is difficult to quantify the effect of smaller local administrative changes on inpatient outcome measures. Study of a single institution also acts as a considerable strength, allowing scrutiny on microsystems and departments within the single institution. Data surrounding complexity of patients, unexpected mortality and hospital occupancy was not available for analysis and thus their respective impact on inpatient outcome measures was not assessed. Finally, longer control and study periods would be beneficial to the study methodology to account for seasonal or yearly variations in activity. Our results suggest
further, more comprehensive assessment is warranted. In conclusion, our study suggests the implementation of the NEAT requires a system change rather than an isolated departmental change for full effect. While the 4 h rule in other Australian institutions has resulted in positive outcomes, the results from our institution are not so optimistic. We have observed an improvement in waiting times and emergency length of stay. These findings have been associated with significant ‘downstream’ effects including an increased IPLOS; however, the increase in utilisation of SSU utilisation must be taken into consideration. Further, more in depth research is required to further explore these findings.
3.
4.
5.
Competing interests None declared.
6.
References 1. Lowthlan JA, Curtis AJ, Jolley DJ et al. Demand at the emergency department front door: 10-year trends in presentations. Med. J. Aust. 2012; 196: 128–32. 2. Fatovich DM, Hirsch RL. Entry overload, emergency department
7.
overcrowding, and ambulance bypass. Emerg. Med. J. 2003; 20: 406–9. Australasian College for Emergency Medicine. Australasian College for Emergency Medicine. Policy Document: Standard Terminology. Canberra: Australian Government Department of Health and Ageing, 2009. Mason S, Weber EJ, Coster J et al. Time patients spend in the emergency department: England’s 4-hour rule – a case of hitting the target but missing the point? Ann. Emerg. Med. 2011; 59: 341–9. Guttman A, Schul MJ, Vermeulen MJ et al. Association between waiting times and short term mortality and hospital admission after departure from emergency department: population based cohort study from Ontario, Canada. BMJ 2011; 342: d2983. Richardson DB. Increase in patient mortality at 10 days associated with emergency department overcrowding. Med. J. Aust. 2006; 184: 213– 16. Sprivulis PC, Da Silva J, Jacobs IG et al. The association between hospital overcrowding and mortality among patients admitted via Western Australian emergency departments. Med. J. Aust. 2006; 184: 208–12.
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8. Geelhoed GC, de Klerk NH. Emergency department overcrowding, mortality and the 4-hour rule in Western Australia. Med. J. Aust. 2012; 196: 122–6. 9. Singer AJ, Thode HC, Viccellio P et al. The association between length of emergency department boarding and mortality. Acad. Emerg. Med. 2011; 18: 1324–9. 10. Australian Government Department of Health and Ageing. The state of our public hospitals, June 2006. [Cited Sep 2012.] Available from URL: http://apo.org.au/node/8488 11. Forero R, Hillman K. Acess block and overcrowding: a literature review Australasian College for Emergency Medicine – Access Block Solutions Summit: University of New South Wales/The Simpson Centre for Health Services Research, 2008; 26. 12. Department of Health, UK. The NHS Plan: a plan for investment, a plan for reform. 2000. [Cited Sep 2014.] Available from URL: http://dera .ioe.ac.uk/4423/1/04055783.pdf 13. Alberti G. Transforming Emergency Care in England. London: Department of Health, 2004. [Cited Aug 2012.] Available from URL: http:// www.dh.gov.uk/en/Publicationsand statistics/Publications/Publications PolicyAndGuidance/DH_4091775 14. Emergency Care Institute of New South Wales. National Emergency Access Targets. Sydney 2012. [Cited Aug 2012.] Available from URL: http://www.ecinsw.com.au/neat 15. Forero R, McCarthy S, Hillman K. Access block and emergency
16.
17.
18.
19.
20.
21.
department overcrowding. Crit. Care 2011; 15: 216–22. Department of Health, Western Australia. Taking pressure off public hospitals (2008–2013). 2009. [Cited Aug 2012.] Available from URL: http:// www.federalfinancialrelations . gov . au / content / npa/health _payments/workforce-reform/ relieving-pressure/WA_IP.pdf Stokes B. Four Hour Rule Program Progress and Issues Review. Perth: Government of Western Australia, Australian Government Department of Health and Ageing, 2011. [Cited Sep 2012.] Available from URL: http://www.health.wa.gov.au/ publications/documents/Four HourRule_Review_Stokes.pdf Emergency Care Institute, NSW. NEAT – The Basics. August 2012. [Cited Sep 2012.] Available from URL: http://www.ecinsw.com.au/ NEAT-the-basics Baggoley C, Owler B, Grigg M et al. Expert panel – review of elective surgery and emergency access targets under the national partnership agreement on improving public hospital services – report to the Council of Australian Governments. Department of Health and Aging, 2011; 1–64. Derlet RW, Richards JR. Overcrowding in the nation’s emergency departments: complex causes and disturbing effects. Ann. Emerg. Med. 2000; 35: 63–8. Topping A, Campbell D. Waiting targets for accident and emergency to
22.
23.
24.
25.
26.
27.
be scrapped. The Guardian 10 June 2010. Jones P, Schimanski K. The four hour target to reduce emergency department ‘waiting time’: a systematic review of clinical outcomes. Emerg. Med. Australas. 2010; 22: 391– 8. Maumill L, Zlc M, Esson AA et al. The National Emergency Access Target (NEAT): can quality go with timeliness? Med. J. Aust. 2013; 198: 153–7. Royal Australasian College of Surgeons. Submission to the council of Australian Governments Expert Panel: four hour national access target in emergency departments. May 2011. [Cited Aug 2012.] Available from URL: https://www.surgeons.org/ media/307099/sbm_2011-05-24 _four_hour_rule.pdf Mitra B, Cameron PA. Emergency department overcrowding and mortality after the introduction of the 4-hour rule in Western Australia. Med. J. Aust. 2012; 196: 499, author reply 500. Mitra B, Cameron PA, Archer P et al. The association between time to disposition plan in the emergency department and in-hospital mortality of general medical patients. Intern. Med. J. 2012; 42: 444–50. Charlson ME, Pompei P, Ales KL et al. A new method of classifying prognostic comorbidity in longitudinal studies: development and validation. J. Chronic Dis. 1987; 40: 373–83.
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