Rationale and Objectives
This short report provides a non-technical overview of one emerging modeling technique, discrete event simulation (DES).
Methods
A selective review of the literature that has applied DES methods to evaluate imaging technologies.
Results
Mathematical models to evaluate the likely costs and outcomes of health technologies have become increasingly accepted. Increasing experience has also brought a mounting awareness of the limitations of conventional modeling techniques such as decision trees and Markov processes. Patient-level simulation, including DES, may provide a more flexible approach to modeling for economic evaluation of health technologies.
Conclusions
The strengths of DES suggest that it may have an increasingly important role in the future modeling of annual screening programs, diagnosis, and treatment of chronic recurrent disease and modeling the utilization of imaging equipment.
Over the past 20 years, the use of mathematical models to evaluate the likely costs and outcomes of health technologies has become increasingly accepted. Early examples in the imaging sciences literature include the use of simple decision trees to evaluate the role of CT in the staging of lung cancer ( ) and Markov modeling to evaluate the course of costs and outcomes after screening mammography ( ). Some policy makers, such as the UK National Institute for Health and Clinical Excellence (NICE), now routinely commission independently conducted economic models of new, controversial, drugs, procedures, and imaging technology ( ). These modeling exercises result in a synthesis of the available evidence, an estimate of cost-effectiveness, and a description of the remaining gaps in the evidence. Increasing experience with modeling in health care settings has led to a number of, sometimes discordant, “best practice” guidelines ( ). These guidelines aim to improve the consistency and quality of published models.
Increasing experience has also brought a mounting awareness of the limitations of conventional modeling techniques such as decision trees and Markov models. It is now evident that many clinical problems do not fit comfortably into the framework provided by these techniques and that more flexible methods are required. There are many potential candidates to fill this gap, many of which involve patient-level simulation ( ). The focus of this short report is on one prominent patient-level technique; discrete event simulation (DES). DES is being used more frequently in health care settings, but its use is still far from mainstream ( Table 1 ). A handful of DES studies have now been published in the imaging sciences (6–8). The purpose of this report is to provide a nontechnical primer on the potential role of DES in evaluating imaging technologies.
Table 1
Methods for modeling the costs and outcomes of health care
Year No. of PUBMED citations for Discrete Event Simulation No. of PUBMED citations for Markov Model 2000 3 92 2001 5 97 2002 8 101 2003 6 132 2004 13 190 2005 14 207
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Limitations of conventional methods
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Discrete event simulation
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Barriers to the adoption of DES in medicine
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