A Scalable Architecture for Incremental Specification and Maintenance of Procedural and Declarative Clinical Decision-Support Knowledge
Avner Hatsek*, 1, Yuval Shahar 1, Meirav Taieb-Maimon 1, Erez Shalom 1, Denis Klimov 1, Eitan Lunenfeld 2
Identifiers and Pagination:Year: 2010
First Page: 255
Last Page: 277
Publisher Id: TOMINFOJ-4-255
Article History:Received Date: 11/8/2009
Revision Received Date: 16/7/2010
Acceptance Date: 6/8/2010
Electronic publication date: 14/12/2010
Collection year: 2010
open-access license: This is an open access article licensed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/3.0/) which permits unrestricted, non-commercial use, distribution and reproduction in any medium, provided the work is properly cited.
Clinical guidelines have been shown to improve the quality of medical care and to reduce its costs. However, most guidelines exist in a free-text representation and, without automation, are not sufficiently accessible to clinicians at the point of care. A prerequisite for automated guideline application is a machine-comprehensible representation of the guidelines. In this study, we designed and implemented a scalable architecture to support medical experts and knowledge engineers in specifying and maintaining the procedural and declarative aspects of clinical guideline knowledge, resulting in a machine comprehensible representation. The new framework significantly extends our previous work on the Digital electronic Guidelines Library (DeGeL) The current study designed and implemented a graphical framework for specification of declarative and procedural clinical knowledge, Gesher. We performed three different experiments to evaluate the functionality and usability of the major aspects of the new framework: Specification of procedural clinical knowledge, specification of declarative clinical knowledge, and exploration of a given clinical guideline. The subjects included clinicians and knowledge engineers (overall, 27 participants). The evaluations indicated high levels of completeness and correctness of the guideline specification process by both the clinicians and the knowledge engineers, although the best results, in the case of declarative-knowledge specification, were achieved by teams including a clinician and a knowledge engineer. The usability scores were high as well, although the clinicians’ assessment was significantly lower than the assessment of the knowledge engineers.