The Impact of Inpatient Boarding on ED Efficiency: A Discrete-Event Simulation Study

In this study, a discrete-event simulation approach was used to model Emergency Department’s (ED) patient flow to investigate the effect of inpatient boarding on the ED efficiency in terms of the National Emergency Department Crowding Scale (NEDOCS) score and the rate of patients who leave without being seen (LWBS). The decision variable in this model was the boarder-released-ratio defined as the ratio of admitted patients whose boarding time is zero to all admitted patients. Our analysis shows that the Overcrowded+ (a NEDOCS score over 100) ratio decreased from 88.4% to 50.4%, and the rate of LWBS patients decreased from 10.8% to 8.4% when the boarder-released-ratio changed from 0% to 100%. These results show that inpatient boarding significantly impacts both the NEDOCS score and the rate of LWBS patient and this analysis provides a quantification of the impact of boarding on emergency department patient crowding

Extended dynamic partial-overlapping batch means estimators for steady-state simulations

Estimating the variance of the sample mean from a stochastic process is essential in assessing the quality of using the sample mean to estimate the population mean, which is the fundamental question in simulation experiments. Most existing studies for estimating the variance of the sample mean from simulation output assume that the simulation run length is known in advance. An interesting and open question is how to estimate the variance of the sample mean with limited memory space, reasonable computation time, and good statistical properties such as small mean-squared-error (mse), without knowing the simulation run length a priori. This paper proposes a finite-memory algorithm that satisfies the above good estimation criteria. Our findings show that the proposed algorithm improves over its competitors in terms of the mse criterion.Simulation Variance of the sample mean Mean-squared-error

Displaying statistical point estimators: The leading-digit procedure

We propose a procedure for reporting a statistical point estimator and its precision for statistical experiments such as simulation experiments. Based on three criteria —loss of statistical information, number of characters required, and likelihood of user misinterpretation—we advocate our procedure for use when reporting many point estimators in tabular form. The procedure discards meaningless digits of the point estimator, and all but the left-most non-zero digit of the standard error. These two resulting values are separated by the “; ” sign.