Queuing Analytic Theory and Discrete Events Simulation for Healthcare:

Modern healthcare achieved great progress in developing and manufacturing medical devices, treatment procedures and drugs to serve individual patients. However, relatively little technical talent and material resources have been devoted to improving the overall health care delivery process as a system. Discrete-event simulation (DES) models and queuing analytic (QA) theory are the most widely applied system engineering and operations research methods used for system analysis and justification of operational business decisions. There are some proponents of using QA theory to solve many pressing hospital problems of patient flow and variability, calculating needed nursing resources and the number of beds and operating rooms (IHI, 2008; Litvak, 2007; McManus et al, 2004; Haraden et al, 2003). They ‘prescribe’ QA theory as a necessary part of a so-called managing variability program (MVP) (IHI, 2008). However they tend to ignore some serious practical limitations of QA theory for hospitals applications. D’Alesandro (2008) summarized why QA theory is misplaced in hospitals. On the other hand, there is a strong tendency of using discrete-events simulation (DES) as a preferred and much more versatile methodology of health care management science to address complex problems of patient flow and predicting needed resources. Jacobson et al (2006) presented a review of publications since 1999 on DES of health care systems. The objective of this material is to provide a comparative analysis of both QA and DES models by applying them side by side to the same problems. Using a number of concrete quantitative examples it is demonstrated why QA approach has serious practical limitations while DES is indeed a more powerful, flexible and informative methodology than QA.