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1 Introduction
In recent decades the term quality has been introduced in to the common language as an essential part of clinical management. The intents of quality approach in healthcare are mainly focused towards the identification of needs, and the evaluation of the service through measurements and indices suitable to assess the performance of the system [1,2]. As in all sectors the use of suitable quality metrics is crucial. Then must be defined tools to evaluate and combine indices to have structured methodologies that express a global evaluation of the quality performance.
Quality indicators can be classified in several different ways, in addition they can be compared and analyzed using different approaches. Some indicators can take contributions from assessed classifications, or more indicators can be used simultaneously according to the rules of data stratification [3]. Indicators can describe events and rates of service's utilization by the population [4]. Higher rates of several indicators may be interpreted as efficiency in functioning, whereas an indicator such as the death rate, which is usually referred to specific clinical situations, may serve to assess the risk of death after medical intervention. A widespread approach relies on the definition and use of benchmarks, which allows a comparison between indicators related to homogeneous systems and processes [5]. Indicators can be divided into sub-groups to analyse the system, even through simplified models offer the possibility to identify the areas of the health system in a conceptual framework that link these areas to correlating indicators for an overall analysis. The goal is a first characterization of the individual components and the successive study of inter-relationship between them to improve the outcome in the individual patient. For this the model proposed by Donabedian [6] and integrated by Handler et al. [7] is widely accepted and proposed in many studies [1] for the characterization and the measure of the quality in healthcare.
An accepted fact in managing metrics for quality is to put in evidence that in any process there is a background noise due to cumulative effects of many small, essentially unavoidable causes, i.e. stable system changes. Thus the estimation of useful indicators requires a phase of analysis in which statistical techniques can be deployed. Between more common approaches to evaluate systems dynamic of...