A model for developing a decision support system to simulate national drug policy indicators evaluate consequences of their decisions. Although some national and international organizations offer some key indicators for evaluation of NDP on the basis of availability, affordability, quality, and rationality, these indicators are often retrospective and they need time-consuming data collecting procedures. Lack of key indicators for evaluation of NDP in many countries causes failure in determining the relationship between health indices and the volume of the pharmaceutical market [2]. Although these countries have organizations and rules for regulating medicines, the system often does not function properly [6, 7]. Sometimes lack of tools for predicting the future leads to some policies which could not be evaluated at the time of decision making [5]. In the case of safety, efficacy, and quality, NDAs usually try to follow major organizations such as the FDA or EMEA; but to enhance availability and affordability, each country needs its own local solutions. Accurate national economic and health data, sufficient human resources, clarified rules and efficient structure are some prerequisites for calculation of the indicators that are the main weaknesses of most countries. Of course, the consequences of these defects would be extended to the life of all people and the community and health policy makers [8]. According to the mentioned explanations, pharmaceutical policy makers need a kind of decision support system which should be responsible for the following tasks: – shortening the decision process, – increasing the rationality of actions, – evaluating the different alternatives, – reducing the costs, – decreasing the human-derived mistakes, – increasing reliability and validity, – providing potentials for sensitivity analysis and repeatability. Recently, modelling and simulation techniques have created an opportunity to reduce the costs and risks of uncertain conditions. Searching bibliography databases shows the techniques that are used in artificial physician software and in the quantitative structuring of drugs. In managerial work and policy making, modelling and simulation have been applied in some fields [9, 10]. Although there are a few articles about the use of simulation models as a decision support system for hospitals or disease management, we think that developing a well-adjusted model is crucial to reduce the risk of decisions in pharmaceutical policy. There are many models for simulation but only a few of them have been used in the health care system. They are as follows: • Mathematic models that try to put all relations and interactions between variables in a unique formula. Deferential equations, Fourier-type series and expansions are some methods in this modelling. Validation of the model strongly relies on the quantity and quality of variables which are used in the model [11]. • Econometric models use statistical regression to make an equation between independent and dependent variables. Although there are many techniques to adjust the best model to variables, many unknown factors in pharmaceutical markets decrease the accuracy of the model. In spite of the accuracy of the models, regression models connect some input variables to one output variable. In this situation, we have many NDP indicators as the output variable and some output variables are inputs for other models. Thus we need at least one regression model for each output variable, which unfortunately would make the model too complicated [12]. • Artificial neural networks simulate the neurons’ functions by connecting some input variables to some output variables. These models have to be trained to show an acceptable accuracy, but for training they need too many data. Usually it is impossible to find a good data source for model variables, especially in developing countries [12, 13]. • System dynamic is another model to simulate many industrial, social and natural events. In these models, complicated events break down to simple parts, and then some stocks and flows are Product-related variables Price-related variables Place-related variables Promotion-related variables The expected model • All inputs might affect outputs • Some outputs might affect input • Some other variables might affect the model Availability indicators Affordability indicators Quality indicators Rationality indicators Figure 1. The expected model of support system to simulate national drug policies indicators Arch Med Sci 5, October / 2011 745