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A Pattern-based Analysis of Clinical Computer-interpretable Guideline Modeling Languages

^a Department of Eindhoven University of Technology, Eindhoven, the Netherlands
^b Departmet of Management Information Systems, University of Haifa, Haifa, Israel.

^_* Correspondence: Nataliya Mulyar, MSc, Eindhoven University of Technology Paviljoen J.08, NL-5600 MB Eindhoven, the Netherlands (Email: n.mulyar{at}tue.nl).

Received for publication: 01/30/07; accepted for publication: 07/26/07.

Objectives: Languages used to specify computer-interpretable guidelines (CIGs) differ in their approaches to addressing particular modeling challenges. The main goals of this article are: (1) to examine the expressive power of CIG modeling languages, and (2) to define the differences, from the control-flow perspective, between process languages in workflow management systems and modeling languages used to design clinical guidelines.

Design: The pattern-based analysis was applied to guideline modeling languages Asbru, EON, GLIF, and PROforma. We focused on control-flow and left other perspectives out of consideration.

Measurements: We evaluated the selected CIG modeling languages and identified their degree of support of 43 control-flow patterns. We used a set of explicitly defined evaluation criteria to determine whether each pattern is supported directly, indirectly, or not at all.

Results: PROforma offers direct support for 22 of 43 patterns, Asbru 20, GLIF 17, and EON 11. All four directly support basic control-flow patterns, cancellation patterns, and some advance branching and synchronization patterns. None support multiple instances patterns. They offer varying levels of support for synchronizing merge patterns and state-based patterns. Some support a few scenarios not covered by the 43 control-flow patterns.

Conclusion: CIG modeling languages are remarkably close to traditional workflow languages from the control-flow perspective, but cover many fewer workflow patterns. CIG languages offer some flexibility that supports modeling of complex decisions and provide ways for modeling some decisions not covered by workflow management systems. Workflow management systems may be suitable for clinical guideline applications.