Abstract
This study presents a methodology for creating and employing decision-analytic models in order to solve problems involving different health care expertises. Several agents may concur in making a choice, each of them having a different role both in building and using the model. As a matter of fact, each agent can provide specialized knowledge for the creation of parts of this model. Operators may use different sets of data for exploring possible model improvement, and may manipulate different variables influencing final decision. The training example given by the paper will be a model for a therapeutic choice based on the cost/effectiveness ratio. Two agents will be taken into consideration: a physician, able to predict health outcomes and a hospital manager, able to compute costs. They both have access to the hospital data base, but with different logical views, that allow them to validate and improve the relative part of the model. The aim of this work was directed towards demonstrating that the use of explicit knowledge representation concerning budget-related choices in health care, can improve communication among different operators, and eventually ameliorate the quality control of health care services.
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© 1995 Springer-Verlag Berlin Heidelberg
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Quaglini, S., Stefanelli, M., Locatelli, F. (1995). Decision models for cost-effectiveness analysis: a means for knowledge sharing and quality control in health care multidisciplinary tasks. In: Barahona, P., Stefanelli, M., Wyatt, J. (eds) Artificial Intelligence in Medicine. AIME 1995. Lecture Notes in Computer Science, vol 934. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-60025-6_146
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DOI: https://doi.org/10.1007/3-540-60025-6_146
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