scholarly journals Forecasting emergency department presentations

2007 ◽  
Vol 31 (1) ◽  
pp. 83 ◽  
Author(s):  
Robert Champion ◽  
Leigh D Kinsman ◽  
Geraldine A Lee ◽  
Kevin A Masman ◽  
Elizabeth A May ◽  
...  
Keyword(s):  
Emergency Department ◽  
Time Series ◽  
Time Series Analysis ◽  
Software Package ◽  
Exponential Smoothing ◽  
Statistical Forecasting ◽  
Forecasting Methods ◽  
Number Of Patients ◽  
Forecasting Performance ◽  
Optimal Forecasting

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 their own analysis to aid planning.

scholarly journals An interrupted time-series analysis of the impact of emergency department reconfiguration on regional emergency department trolley numbers in Ireland from 2005 to 2015

BMJ Open ◽  
2019 ◽  
Vol 9 (9) ◽  
pp. e029261 ◽  
Author(s):  
Brenda Lynch ◽  
John Browne ◽  
Claire Mary Buckley ◽  
Orla Healy ◽  
Paul Corcoran ◽  
...  
Keyword(s):  
Emergency Department ◽  
Time Series ◽  
Time Series Analysis ◽  
Interrupted Time Series Analysis ◽  
Series Analysis ◽  
North East ◽  
The North ◽  
Number Of Patients ◽  
Geographical Regions ◽  
The Impact

ObjectivesTo understand the impact of emergency department (ED) reconfiguration on the number of patients waiting for hospital beds on trolleys in the remaining EDs in four geographical regions in Ireland using time-series analysis.SettingEDs in four Irish regions; the West, North-East, South and Mid-West from 2005 to 2015.ParticipantsAll patients counted as waiting on trolleys in an ED for a hospital bed in the study hospitals from 2005 to 2015.InterventionThe system intervention was the reconfiguration of ED services, as determined by the Department of Health and Health Service Executive. The timing of these interventions varied depending on the hospital and region in question.ResultsThree of the four regions studied experienced a significant change in ED trolley numbers in the 12-month post-ED reconfiguration. The trend ratio before and after the intervention for these regions was as follows: North-East incidence rate ratio (IRR) 2.85 (95% CI 2.04 to 3.99, p<0.001), South IRR 0.68 (95% CI 0.51 to 0.89, p=0.006) and the Mid-West IRR 0.03 (95% 1.03 to 2.03, p=0.03). Two of these regions, the South and the Mid-West, displayed a convergence between the observed and expected trolley numbers in the 12-month post-reconfiguration. The North-East showed a much steeper increase, one that extended beyond the 12-month period post-ED reconfiguration.ConclusionsFindings suggest that the impacts of ED reconfiguration on regional level ED trolley trends were either non-significant or caused a short-term shock which converged on the pre-reconfiguration trend over the following 12 months. However, the North-East is identified as an exception due to increased pressures in one regional hospital, which caused a change in trend beyond the 12-month post reconfiguration.

scholarly journals A New Approach of Fuzzy-Wavelet Method’s Implementation in Time Series Analysis

2011 ◽  
Vol 5 (2) ◽  
Author(s):  
Seng Hansun ◽  
Subanar Subanar
Keyword(s):  
Time Series ◽  
Time Series Analysis ◽  
Soft Computing ◽  
Hybrid Model ◽  
Fuzzy Inference ◽  
Series Data ◽  
Wavelet Method ◽  
New Approach ◽  
Series Analysis ◽  
Forecasting Methods

      Abstract— Recently, many soft computing methods have been used and implemented in time series analysis. One of the methods is fuzzy hybrid model which has been designed and developed to improve the accuracy of time series prediction.      Popoola has developed a fuzzy hybrid model which using wavelet transformation as a pre-processing tool, and commonly known as fuzzy-wavelet method. In this thesis, a new approach of fuzzy-wavelet method has been introduced. If in Popoola’s fuzzy-wavelet, a fuzzy inference system is built for each decomposition data, then on the new approach only two fuzzy inference systems will be needed. By that way, the computation needed in time series analysis can be pressed.      The research is continued by making new software that can be used to analyze any given time series data based on the forecasting method applied. As a comparison there are three forecasting methods implemented on the software, i.e. fuzzy conventional method, Popoola’s fuzzy-wavelet, and the new approach of fuzzy-wavelet method. The software can be used in short-term forecasting (single-step forecast) and long-term forecasting. There are some limitation to the software, i.e. maximum data can be predicted is 300, maximum interval can be built is 7, and maximum transformation level can be used is 10. Furthermore, the accuracy and robustness of the proposed method will be compared to the other forecasting methods, so that can give us a brief description about the accuracy and robustness of the proposed method. Keywords—  fuzzy, wavelet, time series, soft computing

scholarly journals Impact of integrated care on trends in the rate of emergency department visits among older persons in Stockholm County: an interrupted time series analysis

BMJ Open ◽  
2020 ◽  
Vol 10 (6) ◽  
pp. e036182
Author(s):  
Megan Doheny ◽  
Janne Agerholm ◽  
Nicola Orsini ◽  
Pär Schön ◽  
Bo Burström
Keyword(s):  
Emergency Department ◽  
Time Series ◽  
Time Series Analysis ◽  
Integrated Care ◽  
Older Persons ◽  
Interrupted Time Series ◽  
Interrupted Time Series Analysis ◽  
Stockholm County ◽  
Series Analysis ◽  
Ed Visits

ObjectiveTo investigate the association between the implementation of an integrated care (IC) system in Norrtälje municipality and changes in trends of the rate of emergency department (ED) visits.DesignInterrupted time series analysis from 2000 to 2015.SettingStockholm County.ParticipantsAll inhabitants 65+ years in Stockholm County on 31 December of each study year.InterventionIC was established by combining the funding, administration and delivery of health and social care for older persons in Norrtälje municipality, within Stockholm County.OutcomeRates of hospital-based ED visits.ResultsIC was associated with a decrease in the rate of ED visits (incidence rate ratio: 0.997, 95% CI 0.995 to 0.998) among inhabitants 65+ years in Norrtälje. However, the rate of ED visits remained higher in Norrtälje than the rest of Stockholm in the preintervention and postintervention periods. Stratified analyses showed that IC was associated with a decline in the trend of the rate of ED visits among those 65–79 years, the lowest income group and born outside of Sweden. However, there was no significant decrease in the trend among those 80+ years.ConclusionThe implementation of IC was associated with a modest change in the trend of ED visits in Norrtälje, though the rate of ED visits remained higher than in the rest of Stockholm. Changes in the composition of the population and contextual changes may have impacted our findings. Further research, using other outcome measures is needed to assess the impact of IC on healthcare utilisation.

scholarly journals Advising an Appropriate Forecasting Method for a Snacks Item (Biscuit) Manufacture Company in Bangladesh

2018 ◽  
Vol 66 (1) ◽  
pp. 55-58
Author(s):  
Nandita Barman ◽  
M Babul Hasan ◽  
Md Nayan Dhali
Keyword(s):  
Time Series ◽  
Inventory Management ◽  
Linear Trend ◽  
Exponential Smoothing ◽  
Demand And Supply ◽  
Forecasting Methods ◽  
Forecasting Method ◽  
Short Term Forecasting ◽  
Exponential Smoothing Method ◽  
Manufacture Company

In this paper, we study the most appropriate short-term forecasting methods for the newly launched biscuit factory produces different types of biscuits. One of them is nut-orange twisted biscuits. As it is a newly launched biscuit factory, it does not use any scientific method to find future demand of their products to produce for the purpose of sales. Having an error free production as well as a good inventory management we try to find an appropriate forecasting method for the sets of data we analyzed for that specific production. Several forecasting methods of time series forecasting such as the Moving Averages, Linear Regression with Time, Exponential Smoothing, Holt‘s Method, Holt-Winter‘s Method etc. can be applied to estimate the demand and supply for these companies. This paper focuses on selecting an appropriate forecasting technique for the newly launched biscuit company. For this, we analyze Exponential Smoothing method as used to time series. We observe from the empirical results of the analysis that if the data has no trend as well as seasonality, Exponential Smoothing Forecasting Method processes as the most appropriate forecasting method for the factory. If the data experiences linear trend in it then Holt’s Forecasting Method processes as the most appropriate forecasting method for the sets of data we analyzed. Dhaka Univ. J. Sci. 66(1): 55-58, 2018 (January)

Sign in / Sign up

Export Citation Format

Share Document