On a multiserver Markovian queueing system with balking and reneging
Computers and Operations Research
Stochastic (Q,r inventory model with customer reneging
Computers and Industrial Engineering
The M/M/c/N queue with balking and reneging
Computers and Operations Research
Using simulation to reduce length of stay in emergency departments
WSC '94 Proceedings of the 26th conference on Winter simulation
Reducing time in an emergency room via a fast-track
WSC '95 Proceedings of the 27th conference on Winter simulation
Proceedings of the 35th conference on Winter simulation: driving innovation
Proceedings of the 35th conference on Winter simulation: driving innovation
Emergency departments II: simulating Six Sigma improvement ideas for a hospital emergency department
Proceedings of the 35th conference on Winter simulation: driving innovation
A Diffusion Approximation for a GI/GI/1 Queue with Balking or Reneging
Queueing Systems: Theory and Applications
Computers and Operations Research
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Administrators know when Emergency Department (ED) overcrowding is a problem in their hospital. Lead times to change ED capacity are long and require strategic tools. ED patients who Leave WithOut Treatment (LWOT) before seeing a physician are, in queuing nomenclature, 'reneging' from an overcrowded situation and are an important measure of ED patient safety. We propose to enable strategic decision making on future ED capacity on the basis of patient safety (rather than congestion measures). We hypothesize that the LWOT reneging percentage is captured by the balking probability (p"K) relationship of an M/M/1/K queue. If true, this relationship is superior to the typical ad hoc regression relationships commonly found. Since it is based on a physical scientific mechanism, the sample size requirements and extrapolation power are improved. We derive the form of a binomial response nonlinear weighted regression model that best fits p"K for predicting LWOT to long-term ED performance by means of Gauss-Newton linearization. Our results include asymptotic Wald confidence intervals on prediction, specific Pearson and Deviance model goodness-of-fit tests, and residual analysis that facilitate identification of outlying data points. None of these features exist for reneging (or balking) models previously presented in the literature.