In 1994, the New England Journal of Medicine published an editorial, defining very strict criteria that submitted papers on cost-effectiveness analysis should meet in order to be considered for publication [10]. Briefly, editors will refuse a paper if one or more authors has economic interests with the study sponsor, if any. As a matter of fact, many economic analyses focuse on pharmaceutical agents and are funded by pharmaceutical industry. As another position paper [11] outlines, the results of these studies are exploited by pharmaceutical firms "to support the pricing and marketing of new interventions and to influence national health care systems and third-party payers in their development of coverage and payement decisions". It is then clear that the scientific literature in this field, unlike many others, has a direct link with the market and, more generally, with society. The above mentioned editorial raised many criticisms. But, without entering into details, both adverse and favourable comments highlight the need to establish principles and methods for conducting economic studies in health care. This need is not new, as shown by a special paper of the New England Journal of Medicine published in 1977 [Weinstein], but it is only recently that the scarcity of resources and the governments' drive for better financial management has given new impulse to the area, and has led to the diffusion of some research guidelines [11]. In most opinion, explicit modeling of the decision process facilitates the application of these guidelines and the evaluation of the analysis. In this sense, the framework proposed here could be exploited by health care professionals at various levels, from the physician who has to decide a therapy, to the officer that has to control local expenditures. A number of commercial products for performing decision analysis are nowadays on the market, such as Hugin which is based on influence diagrams, Smltree , and DATA which are based on decision trees. However, the usefulness of these packages is limited to 'stand-alone' applications: models built within these systems cannot share any knowledge or data source with other models, nor can make use of common ontologies. Hugin models are able to learn from data, but the user must create ad-hoc data files for allowing such learning. In addition, these products run on different hardware platforms. While it is not our aim to propose new products for decision analysis, we can see the need for a tool which could make use not only of existing technology for decision analysis, but could also integrate it with other technologies, such as data base management and knowledge engineering, and could provide predefined decision structures addressing the multi-user aspects of cost-assessment and cost-effectiveness applications in health-care.