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.

A fuzzy taxonomy for e-Health projects / D'Urso, Pierpaolo; DE GIOVANNI, Livia; Spagnoletti, Paolo. - In: INTERNATIONAL JOURNAL OF MACHINE LEARNING AND CYBERNETICS. - ISSN 1868-8071. - STAMPA. - (2012), pp. 1-18. [10.1007/s13042-012-0118-4]

A fuzzy taxonomy for e-Health projects

D'URSO, PIERPAOLO;DE GIOVANNI, LIVIA;SPAGNOLETTI, PAOLO
2012

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.
e-Health; Healthcare; Fuzzy clustering; Imprecise evaluation scales; Soft taxonomy
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11385/38855
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