The SAGE approach to guideline modeling is motivated by several considerations.

Standards and Sharing First is the need to make use of standards. Past efforts have gone into developing shared models for representing medical decisions and clinical guidelines. However, it takes more than a formalism for medical logic to accomplish sharing of computable medical knowledge. Reuse of a guideline knowledge base also requires an infrastructure that includes a flexible medical record query interfaces, terminology mediation, and an action interface.

Standards vs. RequirementsWith the emergence of clinical standards such as Health Level Seven’s Version 3 (HL7 v3) Reference Information Model (RIM) and College of American Pathologists’s SNOMED Clinical Terms, we have the opportunity to build a guideline model from basic principles, to take advantage of these infrastructural standards in a systematic way. Still, making use of standards for modeling guideline is not a straight forward process. Rarely do existing standards completely satisfy the requirements of guideline modeling. Thus the elucidation of the complex relationship between existing standards and requirements for guideline modeling and deployment is one of the themes of the SAGE project.

Integration with WorkflowAnother consideration is SAGE’s approach to integrating guideline-based decision support with the workflow of care process. The success of clinical decision-support systems (DSSs) depends heavily on how the system is integrated into the care process. The SAGE project takes the approach that, as a provider of decision-support services to CISs, SAGE will not be in control of host systems’ workflow management. Thus, in the SAGE modeling approach, we are not required to model detailed workflow as, for example, University of Pavia’s careflow methodology proposes. Instead, the SAGE system will respond to opportunities for decision support in the care process.

Scenarios of CareWe model enough of the workflow contexts to recognize appropriate events that should trigger decision-support services. Upon receipt of such triggering events, the SAGE DSS will deliver, through existing functions of the CIS, guideline-based recommendations appropriate for members of a care team. The implication of this approach for the guideline modeling is that guideline knowledge must support operations in an event-driven reactive system and it must take into account clinical and organization contexts such as care setting and provider roles. Instead of just creating an electronic version of a clinical practice guideline, guideline modeling in SAGE formalizes guideline knowledge being used in specific scenarios and settings.

New Guideline RepresentationOur decision to start a new guideline representation is based on much interchange and cross-fertilization that has taken place recently in the guideline modeling community. Beginning with workshops such as the ones sponsored by InterMed in 1999, Open Clinical in 2000, and University of Leipzig in 2001, and continuing with a number of comparison papers, workers in the guideline modeling community have gained much better understanding of the commonalities and differences among different guideline modeling approaches and of the design choices made in them. The SAGE project has given us the opportunity to synthesize prior work and, wherever possible, to establish mappings between the SAGE model and other models.

As a result of these considerations, the SAGE project took an approach to guideline modeling that is based on the use of (1) standard data models and services, (2) standard terminologies, and (3) a deployment-based approach to guideline encoding.

To achieve interoperability of guideline decision-support system (GDSS) with vendor clinical information systems (CIS), we make explicit a suite of models and services that together define the interface between DSS and CIS. An organizational model that defines available clinical and administrative events, roles, settings, and resources provides the vocabulary to describe the contexts in which our GDSS provides decision-support services.

A guideline may be triggered by a patient check-in event generated at a primary care, outpatient clinic where guideline-based alerts are generated for providers who have the roles of clinic nurse, and primary care physician.

Virtual Medical RecordA guideline is encoded using a simplified view of a patient’s medical record data, called a Virtual Medical Record (VMR) that is ultimately based on the HL7 RIM. The VMR classes, by themselves, still allow several degrees of freedom in representing patient information (e.g. the code slot in AdverseReaction may be “allergic drug reaction” (SNOMED CT 74069000) or more restrictive “vaccine allergy’ (SNOMED CT 294640001). Detailed clinical models, also called Clinical Expression Models (CEMs), spell out, by placing constraints on attributes of VMR classes, precisely how patient data would be represented.

Terms from terminologies are the atomic units of meaning that we use to make assertions through information models such as VMR and CEMs. However, concepts used in clinical guidelines often do not match precisely the term hierarchies in standard medical terminologies. For example, the concept of “pulmonary problem excluding asthma”, is unlikely to have an exact equivalent in any standard terminology. For the SAGE project we developed several strategies to define guideline concepts from standard terminologies.

The first technique is to use the a reference terminology’s own compositional method for defining new concepts. Using SNOMED CT, for example, we can define to terms such as “severe wound” as a {“wound lesion” (SNOMED CT 239155007) associated severity “severe” (SNOMED CT 24484000)}.

Our second technique is to using a notation, which we call Concept Expression, to define a term as Boolean combinations of other terms (e.g. “pulmonary disease excluding asthma” as a {“disease of lung” (SNOMED CT 19829001) AND NOT “asthma” (SNOMED CT 195967001)}).

To ensure that the a guideline formalized in a SAGE knowledge base is informed by the usage scenarios of the guidelines in the care process, the SAGE project developed a seven-step deployment-driven guideline modeling methodology (see figure below). Once the decision to develop a guideline has been made, the SAGE guideline knowledge base methodology requires that clinicians first create clinical scenarios[1] that are detailed enough to support integration of guideline recommendations into clinical workflow. These usage scenarios identify opportunities for providing decision support, the roles and information needs of care providers, events that may activate the guideline system, and guideline knowledge relevant in these scenarios.

In subsequent steps, clinicians distill, from guideline texts, medical literature, and their clinical expertise, the guideline logic and concepts[2,3] needed to generate these recommendations.

A vocabulary inventory[5] is created when the guideline concepts are mapped to reference terminologies. The usage of the concepts in making statements about a patient is standardized as clinical expression models[4]. In the final stages, clinicians work with knowledge engineers to formalize the guideline knowledge in term of the SAGE guideline model, producing a guideline knowledge-base[4].

Finally, before a formalized guideline can be installed and used in a local institution, its medical content must be reviewed and revised (in what we call the localization process[7]) and its data models, terminologies, and organization assumptions (roles, events, and resources) must be mapped to those of the local institution (in what we call the binding process[7]).

The SAGE guideline knowledge-base development process. The process is driven not only by the guideline literature, but by use cases for clinical decision support based on carefully defined clinical scenarios. The guideline knowledge base is support by a series of terminology, information, and organizational models.