Forecasting the demand on accident and emergency departments in health districts in the trent region

Milner, P C

doi:10.1002/sim.4780071007

Cited by 27 publications

(8 citation statements)

References 3 publications

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“…These frequently include autoregression, moving average regression, exponential smoothing, and other variants and combinations of these techniques; a description of these techniques has been previously published 24 . A handful of studies have used various time series modeling techniques with some success in long‐ and short‐term prediction of ED work load or census 39–43 . This method provides a fair estimate that can be useful for tracking trends or anticipating workload, but by their nature, such averages often fail to capture the level of short‐term variability that may be important in operations management in the setting of surge conditions 43 …”

Section: Overview Of Common Approaches Tomodeling Ed Crowdingmentioning

confidence: 99%

Review of Modeling Approaches for Emergency Department Patient Flow and Crowding Research

Wiler

Griffey²,

Olsen³

2011

Academic Emergency Medicine

134

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Emergency department (ED) crowding is an international phenomenon that continues to challenge operational efficiency. Many statistical modeling approaches have been offered to describe, and at times predict, ED patient load and crowding. A number of formula-based equations, regression models, timeseries analyses, queuing theory-based models, and discrete-event (or process) simulation (DES) models have been proposed. In this review, we compare and contrast these modeling methodologies, describe the fundamental assumptions each makes, and outline the potential applications and limitations for each with regard to usability in ED operations and in ED operations and crowding research.

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Section: Overview Of Common Approaches Tomodeling Ed Crowdingmentioning

confidence: 99%

Review of Modeling Approaches for Emergency Department Patient Flow and Crowding Research

Wiler

Griffey²,

Olsen³

2011

Academic Emergency Medicine

134

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“…Most published studies using time series were based on seasonal factors only and were developed for forecasting overall demand for ED services [ 2 - 7 ]. Since there is wide variation in disease severity and acuity among patients presenting at the ED, clinical services and resources required will likewise vary considerably.…”

Section: Introductionmentioning

confidence: 99%

Forecasting daily attendances at an emergency department to aid resource planning

et al. 2009

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BackgroundAccurate forecasting of emergency department (ED) attendances can be a valuable tool for micro and macro level planning.MethodsData for analysis was the counts of daily patient attendances at the ED of an acute care regional general hospital from July 2005 to Mar 2008. Patients were stratified into three acuity categories; i.e. P1, P2 and P3, with P1 being the most acute and P3 being the least acute. The autoregressive integrated moving average (ARIMA) method was separately applied to each of the three acuity categories and total patient attendances. Independent variables included in the model were public holiday (yes or no), ambient air quality measured by pollution standard index (PSI), daily ambient average temperature and daily relative humidity. The seasonal components of weekly and yearly periodicities in the time series of daily attendances were also studied. Univariate analysis by t-tests and multivariate time series analysis were carried out in SPSS version 15.ResultsBy time series analyses, P1 attendances did not show any weekly or yearly periodicity and was only predicted by ambient air quality of PSI > 50. P2 and total attendances showed weekly periodicities, and were also significantly predicted by public holiday. P3 attendances were significantly correlated with day of the week, month of the year, public holiday, and ambient air quality of PSI > 50.After applying the developed models to validate the forecast, the MAPE of prediction by the models were 16.8%, 6.7%, 8.6% and 4.8% for P1, P2, P3 and total attendances, respectively. The models were able to account for most of the significant autocorrelations present in the data.ConclusionTime series analysis has been shown to provide a useful, readily available tool for predicting emergency department workload that can be used to plan staff roster and resource planning.

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“…In a UK study, Milner 6 used time series analysis to forecast the annual demand for emergency services in the Trent region. While the notation we use here is not Milner' s, it is designed to assist those who may wish to read his papers.…”

Section: Literature Reviewmentioning

confidence: 99%

“…An important forecasting strategy 6 is to update forecasts as more data become available. We would expect that better forecasts will be obtained if more recent data are available to model.…”

mentioning

confidence: 99%

Forecasting emergency department presentations

Champion¹,

Kinsman²,

Lee³

et al. 2007

Aust. Health Review

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Objective: To forecast the number of patients who will present each month at the emergency department of a hospital in regional Victoria. Methods:The data on which the forecasts are based are the number of presentations in the emergency department for each month from 2000 to 2005. The statistical forecasting methods used are exponential smoothing and Box-Jenkins methods as implemented in the software package SPSS version 14.0 (SPSS Inc, Chicago, Ill, USA). Results:For the particular time series, of the available models, a simple seasonal exponential smoothing model provides optimal forecasting performance. Forecasts for the first five months in 2006 compare well with the observed attendance data. Conclusions:Time series analysis is shown to provide a useful, readily available tool for predicting emergency department demand. The approach and lessons from this experience may assist other hospitals and emergency departments to conduct THE ABILITY TO PREDICT the demand for attendance at the emergency department (ED) of a hospital is valuable -at a micro level for planning rosters for staff in ED, and at a macro level for financial and strategic planning for the hospital. Also, ED attendances are indicators for many aspects of health care in the community, and hence, such predictions may be useful for regional health care planning.There are many factors that will influence the attendance rate at an ED. Indeed, in England, Milner et al found "startling" variation in ED presentation patterns in 190 health districts. 1 Farmer and Emami 2 also noted regional variation in demand for health services in England and the What does this study add?This study develops a model for forecasting the number of presentations each month at the emergency department of a hospital in regional Victoria. The forecasts provided by the model are shown to compare very well with the actual outcomes. The study demonstrates how time series analysis can be used for forecasting, at least in the short term, the demand for emergency services in a hospital. What are the implications for practice?Every hospital is unique. However, the forecasting methods and approach in this paper could be applied in other hospitals to improve resource allocation and strategic planning.

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Forecasting the demand on accident and emergency departments in health districts in the trent region

Cited by 27 publications

References 3 publications

Review of Modeling Approaches for Emergency Department Patient Flow and Crowding Research

Review of Modeling Approaches for Emergency Department Patient Flow and Crowding Research

Forecasting daily attendances at an emergency department to aid resource planning

Forecasting emergency department presentations

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