Evaluating the impact of Information Technology (IT) projects represents a problematic task for policy and decision makers aiming to define roadmaps based on previous experiences. Especially in the healthcare sector IT can support a wide range of processes and it is difficult to analyze in a comparative way the benefits and results of e-Health practices in order to define strategies and to assign priorities to potential investments. A first step towards the definition of an evaluation framework to compare e-Health initiatives consists in the definition of clusters of homogeneous projects that can be further analyzed through multiple case studies. However imprecision and subjectivity affect the classification of e-Health projects that are focused on multiple aspects of the complex healthcare system scenario. In this paper we apply a method, based on advanced cluster techniques and fuzzy theories, for validating a project taxonomy in the e-Health sector. An empirical test of the method has been performed over a set of European good practices in order to define a taxonomy for classifying e-Health projects. © 2012 Springer-Verlag.

A fuzzy taxonomy for e-Health projects / D'Urso, Pierpaolo; Livia De, Giovanni; Paolo, Spagnoletti. - In: INTERNATIONAL JOURNAL OF MACHINE LEARNING AND CYBERNETICS. - ISSN 1868-8071. - 4:5(2013), pp. 487-504. [10.1007/s13042-012-0118-4]

A fuzzy taxonomy for e-Health projects

D'URSO, Pierpaolo;
2013

Abstract

Evaluating the impact of Information Technology (IT) projects represents a problematic task for policy and decision makers aiming to define roadmaps based on previous experiences. Especially in the healthcare sector IT can support a wide range of processes and it is difficult to analyze in a comparative way the benefits and results of e-Health practices in order to define strategies and to assign priorities to potential investments. A first step towards the definition of an evaluation framework to compare e-Health initiatives consists in the definition of clusters of homogeneous projects that can be further analyzed through multiple case studies. However imprecision and subjectivity affect the classification of e-Health projects that are focused on multiple aspects of the complex healthcare system scenario. In this paper we apply a method, based on advanced cluster techniques and fuzzy theories, for validating a project taxonomy in the e-Health sector. An empirical test of the method has been performed over a set of European good practices in order to define a taxonomy for classifying e-Health projects. © 2012 Springer-Verlag.
2013
fuzzy clustering; soft taxonomy; e-health; imprecise evaluation scales; healthcare
01 Pubblicazione su rivista::01a Articolo in rivista
A fuzzy taxonomy for e-Health projects / D'Urso, Pierpaolo; Livia De, Giovanni; Paolo, Spagnoletti. - In: INTERNATIONAL JOURNAL OF MACHINE LEARNING AND CYBERNETICS. - ISSN 1868-8071. - 4:5(2013), pp. 487-504. [10.1007/s13042-012-0118-4]
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11573/663817
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