In this workshop we will focus on Monte Carlo disease simulations and how they can be used to perform economic evaluations of health care interventions. Monte Carlo disease simulation is a modeling technique that operates on a patient level basis, explicitly estimating the effect of variability among patients in both underlying disease progression patterns and in individual responsiveness to treatments. Typical outputs from these simulations are patient functional status, life years, quality-adjusted life years, and associated costs, all of which can be appropriately discounted. The output information is presented in the form of distributions which can be used to estimate mean or median values and confidence intervals for the outcomes of interest. These results can be used to compute cost-effectiveness ratios and other drug value measures. Monte Carlo disease simulation also allows decision makers to address the question of risk associated with smaller populations who may not tend to the "average" results generated by Markov models or simulations of large populations. In this workshop, we describe how to create a Monte Carlo simulation model and how different types of uncertainty can be incorporated into the model. We will briefly compare and contrast Monte Carlo and Markov simulation techniques. Discussion topics will be illustrated and motivated by an HIV/AIDS model of the effect of combination antiretroviral therapy on viral load and CD4 progression. This workshop should be beneficial to outcomes researchers and health care decision makers who need to incorporate uncertainty about the natural history of a disease and the impact of alternative disease management strategies for individual patients into their drug value analyses.