Accident analysis in radiotherapy highlighted the need to increase quality assurance (QA) programs by the identification of failures/errors with very low probability (rare event) but very severe consequences. In this field, a Failure Mode, Effects and Criticality Analysis (FMECA) technique, used in various industrial processes to rank critical events, has been met with much interest. The literature describes different FMECA methods; however, it is necessary to understand if these tools are incisive and effective in the healthcare sector. In this work, comparisons of FMECA methodologies in the risk assessment of patients undergoing treatments performed with helical tomotherapy are reported. Failure modes identified for the phases “treatment planning” and “treatment execution” are classified using the Risk Priority Number (RPN) index. Differences and similarities in the classification of failures/errors of the examined FMECA approaches are highlighted.
All activities aimed to study primary causes and effects of air pollution cannot disregard the fact that is necessary to have an optimal Air Quality Monitoring Network (AQMN) for assessing population exposure to air pollution and predicting the magnitude of the health risks. In the framework of a cooperation between ARPA Sicilia organisation and Department of Engineering, University of Palermo, researches were performed to develop an innovative methodology useful to define environmental similarity maps, aimed at supporting the design of air quality monitoring networks at regional scale. This approach is based on new index, called fuzzy environmental analogy index (FEAI), based on fuzzy theory. FEAI is deduced by combining two indexes: meteorological pressure indicator (MPI) and anthropic pressure indicator (API). MPI allows to investigate, for the examined territory, analogies relevant to meteorological conditions, API emphasizes the importance of impacts related to anthropogenic or natural sources at regional scale. Finally, FEAI applications for a case study, related to Sicily region, Italy, are also described. The obtained results allow to confirm the capability of FEAI index to investigate similarities between neighboring areas, in terms of environmental pressures due to anthropic and natural sources, and so to identify gaps of the monitoring network used to define existing air quality conditions.
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