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The SAGE (Standards-Based Active Guideline Environment) project was formed to create a methodology and infrastructure required to demonstrate integration of decision-support technology for guideline-based care in commercial clinical information systems. This paper describes the development and innovative features of the SAGE Guideline Model and reports our experience encoding four guidelines. Innovations include methods for integrating guideline-based decision support with clinical workflow and employment of enterprise order sets. Using SAGE, a clinician informatician can encode computable guideline content as recommendation sets using only standard terminologies and standards-based patient information models. The SAGE Model supports encoding large portions of guideline knowledge as re-usable declarative evidence statements and supports querying external knowledge sources. [ABSTRACT FROM AUTHOR]

Interoperable support of electronic health records and clinical decision support technology are central to the vision of sustainable information infrastructure. Efforts to implement interoperable clinical guidelines for immunization practice have been sparse. We used the SAGE knowledge workbench to develop a knowledge base to provide immunization decision support in primary care. We translated the written clinical guideline into a structured decision logic format. The semantic content to completely capture CDC clinical decision logic required 197 separate concepts but was completely captured with SNOMED CT and LOINC. Although 88% of concepts employed precoordinated codes, 6% of guideline concepts required expanded vocabulary services employing Boolean logical definition using two or more SNOMED concepts. Postcoordination requirements were modest, representing just 6% of guideline semantic concepts. We conclude that creation of interoperable knowledge bases employing clinical vocabulary standards is achievable and realistic. Employment of information model (HL7 RIM) and vocabulary (SNOMED CT, LOINC) standards is a necessary and feasible requirement to achieve interoperability in clinical decision support.

Developing computer-interpretable clinical practice guidelines (CPGs) to provide decision support for guideline-based care is an extremely labor-intensive task. In the EON/ATHENA and SAGE projects, we formulated substantial portions of CPGs as computable statements that express declarative relationships between patient conditions and possible interventions. We developed query and expression languages that allow a decision-support system (DSS) to evaluate these statements in specific patient situations. A DSS can use these guideline statements in multiple ways, including: (1) as inputs for determining preferred alternatives in decision-making, and (2) as a way to provide targeted commentaries in the clinical information system. The use of these declarative statements significantly reduces the modeling expertise and effort required to create and maintain computer-interpretable knowledge bases for decision-support purpose. We discuss possible implications for sharing of such knowledge bases.