The Guideline Execution by Semantic Decomposition of Representation (GESDOR) Model

Introduction

Sharing of computer-interpretable clinical practice guidelines is a critical requirement for guideline development, dissemination, and implementation. Difficulty in the interchange between guideline representation models is a major hindrance to guideline sharing. We have developed the Guideline Execution by Semantic Decomposition of Representation (GESDOR) model to share guidelines at the execution level such that guidelines can be shared even they are developed by different people at different locations and encoded in different formats.

Overview of the Methods

The GESDOR model is based on the observation that different guideline representation models contain similar execution tasks that are used to support the implementation of guidelines. According to the GESDOR model, guidelines can be encoded in different formats. A set of generalized guideline execution tasks are extracted from the existing guideline representation models. Once the mapping relationship between specific guideline models and the generalized guideline execution tasks can be created, this set of generalized guideline execution tasks can be used to drive the execution of specific guideline instances encoded in different formats. The relationship among the guideline instances, the guideline representation models in which the guideline instances are encoded, and the generalized guideline execution tasks is shown in Figure 1.

Figure 1. The relationship among the guideline instances, the guideline models, and the generalized guideline execution tasks in GESDOR.

Specifically, the GESDOR guideline execution model comprises

(1) a set of guideline representation models, which defines the domain to which the GESDOR guideline execution model can be applied,

(2) a set of generalized guideline execution tasks that are extracted from the existing guideline representation models,

(3) a set of mapping relationships, each of which correspond to a specific guideline representation model defined in (1) and provides the semantic links from the elements of that model to the guideline tasks defined in (2), and

(4) a generic task-scheduling model, which harmonizes the existing approaches to task scheduling during guideline execution.

To implement the GESDOR model, the generalized guideline execution tasks need to be extracted first. The mapping relationship between a specific guideline representation model and these guideline tasks needs then to be created. Finally, a generic task-scheduling model needs to be developed to harmonize the existing approaches to task scheduling.

Development of the Generalized Guideline Execution Task Ontology (GETO)

To extract the generalized guideline execution tasks, we started from a comprehensive literature review on the existing guideline representation models. Two specific guideline models, the 3^rd version of the GLIF model (GLIF3) and a variant of the PROforma model (PROforma*), were used as the working templates. Based on these analyses, we have found a set of generalized guideline execution tasks and a guideline’s process structure that are common across different guideline representation models. These generalized guideline execution tasks include (1) the primary tasks, such as data collection, clinical intervention, medical decision making, patient state verification, branching, synchronization, and subguideline, which constitute the basic unit of a guideline’s process structure, and (2) the auxiliary tasks, such as criterion evaluation, event registration, and event invocation, which are used to support the execution of the primary tasks.

To represent a generalized guideline execution task, we used (1) a set of input elements, which define the participants of the task, (2) a set of output elements, which define the execution effects of the task, (3) a set of subtasks, which define the other guideline execution tasks that are embedded within the current task, and (4) a set of execution constraints, including preconditions, postconditions, and events, which define the restrictions on the invocation, completion, and triggering of a primary task. The generalized guideline execution tasks were then integrated and organized as an ontology. We took an incremental approach to the development of this generalized guideline execution task ontology. During this process, we used Protégé-2000 as the knowledge acquisition tool.

Creating the Mapping Relationship between a Specific Guideline Representation Model and the GETO

The mapping relationship between a specific guideline representation model and the GETO defines the semantic links between them. It is used in the GESDOR model as a set of rules to direct the translation of the guideline instances from their original encoding format to the instances of the guideline tasks that drive the execution of the guidelines.

We assumed that the guideline representation models in this research would be organized as ontologies. As the generalized guideline execution tasks were also arranged as an ontology, the mapping between a guideline model and the generalized guideline execution tasks became the mapping between two ontologies. Accordingly, we define the mapping relationship at the class layer and the slot layer, with pairs of anchoring classes as the basic units. For this purpose, we developed an ontology mapping model to create the class mapping and slot mapping between a guideline representation model and the GETO, with different types of class mapping and slot mapping. To facilitate the development and maintenance of the mapping relationship between a guideline representation model and the GETO, we developed the GESDOR Ontology Mapping Editor. In addition, we develop a set of guiding principles that assists to make critical decisions when creating the mapping relationships. A screenshot of the GESDOR Ontology Mapping Editor is shown in Figure 2.

Class Mapping

List of Anchoring Class Pairs

A Potential Anchoring Class Pair

Guideline Representation Model

GETO

Log Information

System Setting

Slot Mappings

Figure 2. A screenshot of the GESDOR Ontology Mapping Editor.

The GESDOR Guideline Execution Model

The GESDOR model is built on the approach of guideline execution that was used by the GLIF3 Guideline Execution Engine (GLEE). In contrast to GLEE, which is driven by the GLIF3 guideline representation model, the GESDOR model uses generalized guideline execution tasks to drive the execution of guidelines. Specifically, guidelines encoded in different formats are stored in a guideline repository, from which they can be retrieved and translated into the instances of the guideline tasks. This translation process is directed by the mapping relationship between the generalized guideline execution tasks and the model in which a guideline encoded. Once the translation has been completely, the guideline task instances are used by the GESDOR guideline execution engine, along with a generic task-scheduling model that harmonizes the existing approaches to task scheduling, to drive the execution of the guideline. The overall system architecture of the GESDOR model is shown in Figure 3.

Figure 3. The overall system architecture of the GESDOR guideline execution model.

Evaluation

A preliminary evaluation has shown that the GESDOR model can be used for the effective execution of guidelines that are encoded in different formats, and thus realizes guideline sharing at the execution level. However, GESDOR’s chaining records should be used conservatively.

Further Information

An overview of the GESDOR model, which will be published as a paper in the Proceedings of the AMIA Symposium 2003, can be downloaded here. For further questions and comments, please contact Dr. Dongwen Wang, Department of Biomedical Informatics, Columbia University, at dongwen.wang@dbmi.columbia.edu.

References

· Wang D, Peleg M, Bu D, Cantor M, Landesberg G, Lunenfeld E, Tu SW, Kaiser GE, Hripcsak G, Patel VL, Shortliffe EH. GESDOR – a generic execution model for sharing of computer-interpretable clinical practice guidelines. Proc AMIA Symp. 2003;:. (in press) [full text in PDF format]

· Wang D. A generic execution model for sharing of computer-interpretable clinical practice guidelines. PhD Dissertation. Columbia University, 2003.

· Wang D, Peleg M, Tu SW, Boxwala AA, Greenes RA, Patel VL, Shortliffe EH. Representation primitives, process models and patient data in computer-interpretable clinical practice guidelines: a literature review of guideline representation models. Int J Med Inform. 2002;68(1-3):59-70. [full text in PDF format]

· Wang D, Shortliffe EH. GLEE – a model-driven execution system for computer-based implementation of clinical practice guidelines. Proc AMIA Symp. 2002;:855-9. [full text in PDF format]

· Wang D, Peleg M, Tu SW, Shortliffe EH, Greenes RA. Representation of clinical practice guidelines for computer-based implementations. Medinfo. 2001;10(Pt 1):285-9. [full text in PDF format]

· Boxwala AA, Tu S, Peleg M, Zeng Q, Ogunyemi O, Greenes RA, et al. Toward a representation format for sharable clinical guidelines. J Biomed Inform. 2001;34(3):157-169. [full text in PDF format]

· Greenes RA, Peleg M, Boxwala A, Tu S, Patel V, Shortliffe EH. Sharable computer-based clinical practice guidelines: rationale, obstacles, approaches, and prospects. Medinfo. 2001;10(Pt 1):201-5.

· Peleg M, Boxwala AA, Ogunyemi O, Zeng Q, Tu S, Lacson R, et al. GLIF3: the evolution of a guideline representation format. Proc AMIA Symp. 2000:645-9.

· Fox J, Johns N, Rahmanzadeh A. Disseminating medical knowledge: the PROforma approach. Artif Intell Med. 1998;14(1-2):157-81. [full text in PDF format]

· Shortliffe EH, Patel VL, Cimino JJ, Barnett GO, Greenes RA. A study of collaboration among medical informatics research laboratories. Artif Intell Med. 1998;12(2):97-123. [full text in PDF format]

Last updated on Aug 05, 2003