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PRAKSAGORAS: A group decision support system for the improvement of the health care services

Matsatsinis Nikolaos, Panagiotis Manolitzas, Grigoroudis, Evangelos, Athanasios Spyridakos, Denis Yannacopoulos

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Summary

One of the main characteristics of the health sector is that consumes a large proportion ofGDP across the European Union. For this reason in our days the health care policy makers useadvanced models in order to evaluate the health care services and more over to redesignthem in order to improve their effectiveness. Studying the academic literature it’s obviousthat the majority of the models that have been developed for the improvement of the healthcare services don’t take into account the opinions of the citizens. Another limitation is thatthe majority of the methodologies take into account only the preference model of onedecision maker. Some recent research approaches in order to tackle the aforementionedproblem use satisfaction questionnaires in order to enable the citizens in the area ofreorganization of the health care services.Praksagoras is a Group Decision Support System that has been developed in order to redesignthe health care services and moreover to improve the decision making process by enablingmany decision makers like CEO of the hospital, the Physicians and the patients. Praksagorascombines simulations, Multicriteria satisfaction analysis, social choice theory and theaggregation disaggregation approach of Multi-criteria analysis. More specifically, via thesimulation model the system depicts the current operation of the emergency departments.The modeler has the ability to test alternatives scenarios for the emergency department inorder to test the impacts of these alternatives scenarios on consistent set of criteria such aswaiting times, length of stay, working load of the personnel and bed usage. The MUSA modelreveals the weak points of the services giving the opportunity to the analyst to developalternatives that are based on patients’ needs. In order to enable many stakeholders (doctors,patients etc.) in the decision making process we use the RACES approach. Races approachincorporates the social choice functions for aggregating individual rankings with MINORA andMIIDAS systems, in order to assess value function(s), as compatible as possible with acollective ranking. The main aim of this approach is to provide mechanisms for the support ofthe analysis of the individual and collective preference models and to enrich the knowledgeof the decision problem and the structure of DM's preferences.

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