A consensus support model based on linguistic information for the initial-self assessment of the EFQM in health care organizations

► Consensus model for the initial self-assessment of the EQFM Excellende Model. ► Linguistic information is used in the EFQM instead of numerical values. ► Linguistic consensus degrees and linguistic distances are used to obtain consensus. The improvement of the quality of the services is one of the...

Full description

Saved in:
Bibliographic Details
Published inExpert systems with applications Vol. 40; no. 8; pp. 2792 - 2798
Main Authors Moreno-Rodrı´guez, J.M., Cabrerizo, F.J., Pérez, I.J., Martı´nez, M.A.
Format Journal Article
LanguageEnglish
Published Amsterdam Elsevier Ltd 15.06.2013
Elsevier
Subjects
Online AccessGet full text

Cover

Loading…
More Information
Summary:► Consensus model for the initial self-assessment of the EQFM Excellende Model. ► Linguistic information is used in the EFQM instead of numerical values. ► Linguistic consensus degrees and linguistic distances are used to obtain consensus. The improvement of the quality of the services is one of the primary sources of competitive advantage in health care organizations. As customers typically search for higher quality of care when choosing treatments, health plans and providers, the health care organizations strive to improve the quality of care and patient safety and satisfaction, as primary goals, with the resources that are available. To do so, the European Foundation for Quality Management (EFQM) Excellence Model for self-assessment has been used by the health care organizations to improve their services and their competitiveness in the global market. However, when the health care organizations address self-assessment processes for the first time, the initial effort needed presents many difficulties. The aim of this paper is to offer a consensus support model based on linguistic information to conduct the self-assessment of the EFQM Excellence Model when questionnaires are used. It is based on the use of linguistic information to provide the individuals’ opinions, which facilitates the individual responses, and on the use of fuzzy majority, represented by means of a linguistic quantifier, to compute the measures guiding the consensus reaching process.
Bibliography:ObjectType-Article-2
SourceType-Scholarly Journals-1
ObjectType-Feature-1
content type line 23
ObjectType-Article-1
ObjectType-Feature-2
ISSN:0957-4174
1873-6793
DOI:10.1016/j.eswa.2012.11.011