Measuring and Forecasting Emergency Department Crowding in Real Time

Hoot, Nathan R.; Zhou, Chuan; Jones, Ian D.; Aronsky, Dominik

doi:10.1016/j.annemergmed.2007.01.017

Cited by 129 publications

(103 citation statements)

References 62 publications

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“…Recent research shows that ED occupancy predicts overcrowding as well as these workload measures [Hoot et al 2007] and correlates well with the staff's perception of overcrowding [McCarthy et al 2008]. …”

Section: Threshold Policiesmentioning

confidence: 99%

Centralized vs. Decentralized Ambulance Diversion: A Network Perspective

Deo

Gurvich²

2011

SSRN Journal

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Section: Threshold Policiesmentioning

confidence: 99%

Centralized vs. Decentralized Ambulance Diversion: A Network Perspective

Deo

Gurvich²

2011

SSRN Journal

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“…The recent literature discusses numerous methods ranging from multivariable regression analysis to nonlinear techniques, discrete event simulation, and neural networks. 11,19,27,[30][31][32][33][34][35] As reported, all of these approaches function reasonably well in providing short-term forecasts of various lengths for a variety of ED operational characteristics. However, many of these models may use proprietary software and often require input of many operational variables, some from outside of the ED, to generate their forecasts.…”

Section: Discussionmentioning

confidence: 99%

“…It has been shown that the measure of ED bed occupancy performs no worse than more complex scores such as EDWIN in identifying ED crowding. [25][26][27] Another important consideration in the development of this study was how to interpret the ED bed occupancy metric: should it be treated as a continuous metric or should a threshold approach be used, in which either an ED is crowded or it is not? While some important ED performance metrics may become abnormal at an easily discerned threshold occupancy level, a crowding metric that can supply a universally applicable threshold of ''this ED is now crowded'' remains to be developed.…”

Section: Discussionmentioning

confidence: 99%

Forecasting Models of Emergency Department Crowding

Schweigler

Desmond

McCarthy

et al. 2009

Academic Emergency Medicine

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Objectives: The authors investigated whether models using time series methods can generate accurate short-term forecasts of emergency department (ED) bed occupancy, using traditional historical averages models as comparison.Methods: From July 2005 through June 2006, retrospective hourly ED bed occupancy values were collected from three tertiary care hospitals. Three models of ED bed occupancy were developed for each site: 1) hourly historical average, 2) seasonal autoregressive integrated moving average (ARIMA), and 3) sinusoidal with an autoregression (AR)-structured error term. Goodness of fits were compared using log likelihood and Akaike's Information Criterion (AIC). The accuracies of 4-and 12-hour forecasts were evaluated by comparing model forecasts to actual observed bed occupancy with root mean square (RMS) error. Sensitivity of prediction errors to model training time was evaluated, as well. Results:The seasonal ARIMA outperformed the historical average in complexity adjusted goodness of fit (AIC). Both AR-based models had significantly better forecast accuracy for the 4-and the 12-hour forecasts of ED bed occupancy (analysis of variance [ANOVA] p < 0.01), compared to the historical average. The AR-based models did not differ significantly from each other in their performance. Model prediction errors did not show appreciable sensitivity to model training times greater than 7 days.Conclusions: Both a sinusoidal model with AR-structured error term and a seasonal ARIMA model were found to robustly forecast ED bed occupancy 4 and 12 hours in advance at three different EDs, without needing data input beyond bed occupancy in the preceding hours. ACADEMIC EMERGENCY MEDICINE 2009; 16:301-308 ª 2009 by the Society for Academic Emergency MedicineKeywords: crowding, forecasting, emergency service, hospital, operations research E mergency department (ED) overcrowding has become a significant problem throughout the United States, leading to possible increased health care costs, causing raised stress levels among staff and patients in EDs, and most importantly, adversely affecting patient outcomes. [1][2][3][4][5][6][7][8][9] One aspect of the problem is the difficulty of anticipating the timing and magnitude of overcrowded conditions. The ability to predict crowded conditions, especially hour by hour, could substantially impact ED operations. To this end, we evaluate how time series-based models perform in short-term forecasting of ED occupancy.Traditionally, ED operations directors have found historical averages to be reliable and accurate for longterm forecasts of ED behavior. For example, a director might use the average ED bed occupancy on Monday evenings at 21:00 over the past 2 years to determine how many staff should be working in the ED at that time. However, short-term forecasting of ED bed occupancy, such as might be useful for calling in additional staff or opening up hospital beds, is likely to need more accurate forecasting techniques.Several authors have looked to time series techniques, such ...

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“…[15] Other ED studies focused on analyzing nurse and physician staffing, patient length of stay (LOS), quality, and crowding. [16][17][18] This study is distinct in examining the effects of improving patient throughput (i.e., reducing dwell time) on temporal patterns of crowding at both an urban and community ED.…”

Section: Introductionmentioning

confidence: 99%

Relieving emergency department crowding: Simulating the effects of improving patient flow over time

Hamrock¹,

Paige

Parks³

et al. 2014

JHA

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Background: Emergency Departments (ED) are challenged with excess demand for services and inadequate system capacity. Crowding at two independent EDs within a health system prompted an examination of the potential effects of improving patient throughput. The objective of this study was to determine the effects of reducing ED dwell time on temporal patterns of patient flow and demand for ED resources. Methods: Separate discrete event simulation (DES) models were developed for the EDs of a 1,000-bed urban medical center and a 560-bed community medical center using patient flow information. These models characterized the effects of reducing patient dwell time on ED care area census (i.e., staffing needs), waiting room census, total length of stay (LOS) and waiting time. Dwell time was defined as the time interval from when a patient entered the main ED care area to when the patient exited the ED by discharge or hospital admission. Total LOS is defined as the entire time interval from ED from arrival to exit (including waiting time). Results: DES results for each site demonstrate how natural patient arrivals and common hospital admission processes generate common temporal patterns of decreased crowding. Improving flow translates to most substantial reductions in waiting time and waiting room census during evening hours (17:00 to 22:00 hours). Significant effects on ED care area census and staffing demands are lagged, not occurring until overnight hours (2:00 to 8:00 hours). We reduced patient dwell time in 5% increments within the urban ED (16.2 min) and community ED (13.5 min) from 5% to 15%. For example, a 10% decrease in dwell time at the urban ED (32.4 min) and community ED (27.0 min) resulted in respective decreases in evening waiting room census by 49% (10.8 patients) and 26% (3.5 patients) during evening hours and ED care area census by 16% (3.6 patients) and 11% (2.0 patients) overnight. Conclusions: DES results suggest that increasing ED efficiency will most significantly decrease delays experienced by evening arrivals and provide opportunities to decrease care area census and reduce staff overnight.

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Measuring and Forecasting Emergency Department Crowding in Real Time

Cited by 129 publications

References 62 publications

Centralized vs. Decentralized Ambulance Diversion: A Network Perspective

Centralized vs. Decentralized Ambulance Diversion: A Network Perspective

Forecasting Models of Emergency Department Crowding

Relieving emergency department crowding: Simulating the effects of improving patient flow over time

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