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

Wiler, Jennifer L.; Griffey, Richard T.; Olsen, Tava Lennon

doi:10.1111/j.1553-2712.2011.01135.x

Cited by 134 publications

(88 citation statements)

References 73 publications

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“…A recent overview in [17] outlines this variety in terms of resulting research questions. Many of the existing papers in this area study reasons for excessive patient waits and discuss strategies to overcome crowded emergency departments [25,30,34,36]. Patient flows and overcrowding of departments appear to be common themes in recent years [12], both of which resonate with the purpose of our work.…”

Section: Pathway Redesign In Emergency Departmentssupporting

confidence: 57%

Redesigning the diagnostic pathway for chest pain patients in emergency departments

Rachuba

Salmon

Zhelev

et al. 2017

Health Care Manag Sci

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Patients presenting with chest pain at an emergency department in the United Kingdom receive troponin tests to assess the likelihood of an acute myocardial infarction (AMI). Until recently, serial testing with two blood samples separated by at least six hours was necessary in order to analyse the change in troponin levels over time. New high-sensitivity troponin tests, however, allow the inter-test time to be shortened from six to three hours. Recent evidence also suggests that the new generation of troponin tests can be used to rule out AMI on the basis of a single test if patients at low risk of AMI present with very low cardiac troponin levels more than three hours after onset of worst pain. This paper presents a discrete event simulation model to assess the likely impact on the number of hospital admissions if emergency departments adopt strategies for serial and single testing based on the use of high-sensitivity troponin. Data sets from acute trusts in the South West of England are used to quantify the resulting benefits.

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Section: Pathway Redesign In Emergency Departmentssupporting

confidence: 57%

Redesigning the diagnostic pathway for chest pain patients in emergency departments

Rachuba

Salmon

Zhelev

et al. 2017

Health Care Manag Sci

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“…It may be noted that the MAPE of 11% for daily arrivals in ED2 is within the range (4.2-14.4%) reported by Wargon et al [24] in a review of models for forecasting the number of daily ED visits, thereby suggesting that a worsening of the MAPE from 11% (daily) to 49% (hourly) may not be unusual. Part of the reason for the worse MAPE for hourly arrivals likely is that the averaging approach of the models means that they underestimate the level of short-term variability and the size of occasional surges [15].…”

Section: Accuracy Of the Forecastsmentioning

confidence: 99%

“…Of the several approaches to modeling ED operations we choose linear regression and time-series analysis, both recommended by Wiler et al [15]. Accurate forecasts of hourly patient arrivals and ED occupancy will support clinicians in counteracting crowding.…”

Section: Introductionmentioning

confidence: 99%

Forecasting Hourly Patient Visits in the Emergency Department to Counteract Crowding

Hertzum¹

2017

Open Ergonomics. J.

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Background:Emergency Department (ED) crowding is a frequent problem that causes prolonged waiting and increased risk of adverse events. While the number of daily and monthly patient arrivals can be forecasted with good accuracy, ED clinicians need hourly forecasts in their ongoing scheduling and rescheduling of their work. Objective:We aim to assess whether the hour-by-hour evolution in patient arrivals and ED occupancy can be accurately forecasted using calendar variables. Method:We obtained data about the patient visits at four Danish EDs from January 2012 to January 2015, a total of 393717 ED visits. The data for 2012-2014 were used to create linear regression models, autoregressive integrated moving average (ARIMA) models, andfor purposes of comparison -naïve models of hourly patient arrivals and ED occupancy. Using the models, patient arrivals and ED occupancy were forecasted for every hour of January 2015. Results:Hourly patient arrivals were forecasted with a mean percentage error of 47-58% (regression), 49-58% (ARIMA), and 60-76% (naïve). Increasing the forecasting interval decreased the mean percentage error. ED occupancy was forecasted with better accuracy by ARIMA than regression models. With ARIMA the mean percentage error of the forecasts of the hourly ED occupancy was 69-73% for three of the EDs and 101% for the last ED. Factors beyond calendar variables might possibly have improved the models of ED occupancy, provided that information about these factors had been consistently available. Conclusion:Hourly patient arrivals can be forecasted with decent accuracy. Forecasts of hourly ED occupancy are less accurate and their accuracy varies more across EDs.

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“…Waiting means a time that patients spend in ED (9). Protraction of staying time in hospital and ED occupies the beds, consumes medical staffs' time and has a negative impact on the process of new admissions, and makes the departments chaotic (10). Based on gained results from the present study, about 17.2% of studied times were in holidays and we did not observe a meaningful difference between holidays, working days, level of crowdedness and activities.…”

Section: Discussionmentioning

confidence: 73%

Evaluation of Overcrowding of Emergency Department in Imam Reza Hospital in 2015 by Implementing 2 Scales: NEDOCS and EDWIN

Rahmani

Rezazadeh

Ala

et al. 2017

Iran Red Crescent Med J

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Background: The reason behind patients' overcrowding in emergency departments (ED) may be due to several factors such as great number of referring patients or inaccessibility of facilities or hospital beds. Objectives: The present study aimed at evaluating the overcrowding of ED in Tabriz Imam Reza hospital in 2015 using 2 scales: National Emergency Overcrowding Scale (NEDOCS), and Emergency Department Work Index (EDWIN). Methods: This was a cross-sectional descriptive study conducted in the ED of Imam Reza hospital affiliated to Tabriz University of Medical Sciences, Iran, in a one-year period (Jan-Dec 2015). Convenience sampling method was used for data collection. During the study, the researchers randomly selected 10 days out of every month and started to collect index variables 4 times a day at 6 AM, 12 noon, 6 PM, and 12 midnight. In total, 488 samples were gathered and both NEDOCS and EDWIN were implemented for each sample. Then, the frequency of overcrowding in the emergency department was reported based on the obtained results. Results: Based on NEDOCS, ED was extremely busy, but not overcrowded in 51 cases (10.5%), it was overcrowded in 298 (61.1%) cases, severely overcrowded in 138 cases (28.3%), and it was busy only in one case (0.2%). However, based on the EDWIN scale, the ED was active but manageable in 91 cases (18.6%), very busy in 36 cases (7.4%), and extremely busy in the remaining 361 cases (74%). Conclusions: EDWIN scale depicted August, July, December, June, and April to be, respectively, the most overcrowded months. This finding revealed EDWIN scale to perform better when trying to have an overall assessment of ED during the whole year, which helps us have clear-cut results for analysis and policy making in managing EDs.

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

Cited by 134 publications

References 73 publications

Redesigning the diagnostic pathway for chest pain patients in emergency departments

Redesigning the diagnostic pathway for chest pain patients in emergency departments

Forecasting Hourly Patient Visits in the Emergency Department to Counteract Crowding

Evaluation of Overcrowding of Emergency Department in Imam Reza Hospital in 2015 by Implementing 2 Scales: NEDOCS and EDWIN

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