Demand and capacity planning in the emergency department: how to do it

Higginson, Ian; Whyatt, Justin; Silvester, Kate

doi:10.1136/emj.2009.087411

Cited by 19 publications

(21 citation statements)

References 10 publications

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“…It is inevitable that queues will develop when demand exceeds capacity, as occurs due to the variability inherent in the demand for ED services. 11 Although there is no single, internationally accepted definition for ED overcrowding, 12 it is recognised as the cause of inefficiencies. Overcrowding in the ED can arise because of the number, urgency or complexity of patients arriving, because of factors within the ED or because of the inability to 'move patients on' elsewhere, usually due to an inability to admit patients to an inpatient bed.…”

Section: Discussionmentioning

confidence: 99%

Emergency department waiting times: do the raw data tell the whole story?

Green¹,

Dawber²,

Masso³

et al. 2014

Aust. Health Review

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Objective. To determine whether there are real differences in emergency department (ED) performance between Australian states and territories.Methods. Cross-sectional analysis of 2009À10 attendances at an ED contributing to the Australian non-admitted patient ED care database. The main outcome measure was difference in waiting time across triage categories.Results. There were more than 5.8 million ED attendances. Raw ED waiting times varied by a range of factors including jurisdiction, triage category, geographic location and hospital peer group. All variables were significant in a model designed to test the effect of jurisdiction on ED waiting times, including triage category, hospital peer group, patient socioeconomic status and patient remoteness. When the interaction between triage category and jurisdiction entered the model, it was found to have a significant effect on ED waiting times (P < 0.001) and triage was also significant (P < 0.001). Jurisdiction was no longer statistically significant (P = 0.248 using all triage categories and 0.063 using only Australian Triage Scale 2 and 3).Conclusions. Although the Council of Australian Governments has adopted raw measures for its key ED performance indicators, raw waiting time statistics are misleading. There are no consistent differences in ED waiting times between states and territories after other factors are accounted for.What is known about the topic? The length of time patients wait to be treated after presenting at an ED is routinely used to measure ED performance. In national health agreements with the federal government, each state and territory in Australia is expected to meet waiting time performance targets for the five ED triage categories. The raw data indicate differences in performance between states and territories. What does this paper add? Measuring ED performance using raw data gives misleading results. There are no consistent differences in ED waiting times between the states and territories after other factors are taken into account. What are the implications for practitioners? Judgements regarding differences in performance across states and territories for triage waiting times need to take into account the mix of patients and the mix of hospitals.

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Section: Discussionmentioning

confidence: 99%

Emergency department waiting times: do the raw data tell the whole story?

Green¹,

Dawber²,

Masso³

et al. 2014

Aust. Health Review

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“…In emergency departments it has been reported that shortfalls in staffing may often be a result of variability in demand rather than an inability to meet average demand. 10 In the UK, the Royal Colleges 6,9 have endorsed the Birthrate Plus (BR+) 11 system as a planning tool to predict the number of midwives required on a labour ward because it takes demand and case mix (acuity) into account. By 2003, 101 maternity services across 117 sites in the UK had adopted BR+ for midwife staff planning, 12 and BR+ is still the method endorsed by the Royal College of Midwives.…”

Section: Introductionmentioning

confidence: 99%

Providing one‐to‐one care in labour. Analysis of ‘Birthrate Plus’ labour ward staffing in real and simulated labour ward environments

Allen

Thornton

2012

BJOG

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Objective To assess the ability of the 'Birthrate Plus' (BR+) midwife staffing system to cope with variability of workload on labour wards.Design Retrospective analysis of labour ward workload and computer simulation of labour wards.Setting The labour ward of a city hospital.Population A total of 5800 births (1 year).Methods The variation in births by time and day was analysed over a 1-year period. Three months of BR+ data were analysed for variation of workload by case mix. A computer simulation model was built to allow prediction of the impact of changing resource levels or shift patterns, and to forecast the impact of changing number of births per year.Main outcome measures Labour ward overloading (when either the number of women or the BR+ Workload Index exceeds the scheduled midwife availability).Results Labour ward overload occurred 37% of the time when applying the BR+ method. Underlying patterns of workload were present and simulation indicated that overload could be reduced by 15-25% if available resources were matched more closely to known patterns of workload. Simulation also indicated that smaller units are predicted to suffer from overload more often than larger units, and are more prone to severe overload.ConclusionsThe BR+ formula for midwife staffing leaves labour wards vulnerable to significant periods of overload. Matching resource levels to known patterns of workload may reduce the occurrence of overload. Simulation indicates that smaller units need higher relative staffing levels to provide the same level of 1:1 care to mothers in labour.Keywords Birthrate+, labor, personnel staffing and scheduling, quality improvement.Please cite this paper as: Allen M, Thornton S. Providing one-to-one care in labour. Analysis of 'BirthratePlus' labour ward staffing in real and simulated labour ward environments. BJOG 2013;120:100-107.

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“…Along these lines, other national health systems have focused on treating the demand variability to prevent and manage ED chaos (Higginson et al, 2011;Jones & Mitchell, 2006;Ong et al, 2009;Walley et al, 2006).…”

Section: Discussionmentioning

confidence: 99%

“…Sometimes, time constraints are essential. These special features must be considered when determining the best way to treat each ED case because, in addition to inter-arrival rates, it is necessary to know the distribution of patients according to levels of priority and clinical areas (Higginson et al, 2011).…”

Section: Discussionmentioning

confidence: 99%

“…However, the most surprising aspect is that these demand periods are highly similar regardless of geographical location (Figure 1; Canadian Institute for Health Information, 2005;Higginson et al, 2011;Ong et al, 2009;Walley et al, 2006). With this predicament in mind, this study seeks to achieve two objectives: (a) validate the existence of stable demand patterns in our ED, which are basic for reducing overload, and (b) know patient flow and its variability in detail with a view to adequate resource planning.…”

Section: Internal or Artificial Variabilitymentioning

confidence: 99%

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