Artificial neural networks and their potentialities in analyzing budget health data: an application for Italy of what-if theory

Paolo Massimo Buscema (), Guido Maurelli (), Francesco Saverio Mennini, Lara Gitto, Simone Russo (), Matteo Ruggeri, Silvia Coretti and Americo Cicchetti ()
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Paolo Massimo Buscema: SEMEION Research Centre of Sciences of Communication
Guido Maurelli: SEMEION Research Centre of Sciences of Communication
Simone Russo: University of Rome Tor Vergata
Americo Cicchetti: Università Cattolica del Sacro Cuore

Quality & Quantity: International Journal of Methodology, 2017, vol. 51, issue 3, No 18, 1276 pages

Abstract: Abstract Since 1992 the Italian local health units (LHU) gained financial independence and became responsible to provide and deliver health care at the local level. Management and financial accounting represent the tool utilized to monitor their net income and the working capital every year. From 2001 on, LHU budget data have being summarized by means of the “income statement”. The income statement is considered the most relevant form for the monitoring of healthcare expenditures. A big amount of data have been collected after that obligation of publishing the income statement. The application of new methods for a better understanding of relationships among variables would be worthwhile. The development of artificial neural networks (ANNs) can represent a useful tool to analyze the relationships among these variables. The purpose of this paper is showing the potentialities of ANNs and especially of artificial neural networks what-if theory (AWIT) model when applied to health budgetary data. This innovative methodology has been employed, in the present paper, to analyze data from five Italian Regions, carrying out some comparison among them. In short, using one dataset that is defined as being the ideal standard containing the relationships necessary to measure desired outcomes, another dataset can be compared to determine its degree of closeness. We can determine the degree of closeness of the second or treated dataset with the original standard. This is the key concept of the method called AWIT. The descriptive analysis carried out outlines the areas of waste LHU and suggests to develop strategies to contrast an inefficient use of resources.

Keywords: Healthcare savings; Artificial neural network; Healthcare costs (search for similar items in EconPapers)
Date: 2017
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DOI: 10.1007/s11135-016-0329-y

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