For over half a century, the management science and industrial engineering community has applied simulation methods to problems in the delivery of health care services. Research in the 1970s used discrete event simulation (DES) to improve patient flows in emergency rooms and doctor’s offices [1], to optimize the geographic location of ambulance stations to minimize response time, and to plan for staffing needs in various hospital departments. In 1976, the growth in these efforts prompted the devotion of an entire issue of Operations Research to the application of these methods in health care, and there were predictions that simulation would revolutionize health care delivery in ways similar to the tremendous improvements that had been seen through its application in manufacturing and network control [2]. However, given the poor state of the quality and high costs of US health care, it could be said that with some notable exceptions, the industrial revolution in health care delivery has not yet occurred. The Institute of Medicine, collaborating with the National Academy of Engineering, described what it termed the “paradox” of American health care, in which the best and the brightest produce multiple innovations in drugs and devices, but that virtually no talent and energy has been devoted to the operation of the health care system [3].

The most recent issues of Value in Health contain two reports by the ISPOR Simulation Modeling Emerging Good Practices Task Force. The first, “Applying Dynamic Simulation Modeling Methods in Health Care Delivery Research—The SIMULATE Checklist” [4], provides a thoughtful overview of dynamic simulation methods, spells out a series of important definitions to ensure that future discussions have a common vocabulary, and develops a simple tool, the SIMULATE checklist, to assist modelers in making decisions about the need for a dynamic simulation method. It is an important first start, but work still needs to be done to enhance the value such a checklist has for an investigator. Some components of a problem essentially require that a dynamic modeling system be used (the need to represent component interactions, such as often seen in infectious disease models), whereas others may or may not require a dynamic system, but using a dynamic model may be useful or efficient (time and multilevel components are often very well modeled in microsimulation or state transition models).