Analysis of the GLARE and GPROVE approaches to clinical guidelines

Authors:
Alessio Bottrighi;Federico Chesani;Paola Mello;Marco Montali;Stefania Montani;Sergio Storari;Paolo Terenziani
Affiliations:
DI, Univ. Piemonte Orientale “A. Avogadro”, Alessandria, Italy;DEIS, Univ. Bologna, Bologna, Italy;DEIS, Univ. Bologna, Bologna, Italy;DEIS, Univ. Bologna, Bologna, Italy;DI, Univ. Piemonte Orientale “A. Avogadro”, Alessandria, Italy;ENDIF, Univ. Ferrara, Ferrara, Italy;DI, Univ. Piemonte Orientale “A. Avogadro”, Alessandria, Italy
Venue:
KR4HC'09 Proceedings of the 2009 AIME international conference on Knowledge Representation for Health-Care: data, Processes and Guidelines
Year:
2009

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Abstract

Clinical guidelines (GLs) play an important role in medical practice, and computerized support to GLs is now one of the most central areas of research in Artificial Intelligence in medicine. In recent years, many groups have developed different computer-assisted management systems of GL. Each approach has its own peculiarities and thus a comparison is necessary. Many possible aspects can be analyzed, but a first analysis has probably to consider the GL models, i.e. the representation formalisms provided. To this end, Peleg and al. [4] have analyzed and compared six different frameworks. In this paper, we analyse also GLARE and GPROVE on the basis of the same methodology. Moreover, we extend such analysis by considering the tools and the facilities that GLARE and GPROVE provide to support the use of GLs. The final goal of our analysis is to exploit the differences between these two systems and if they can be fruitfully integrated.