The SAGE Guideline Model: Achievements and Overview
- Samson W Tu,
- James R Campbell,
- Julie Glasgow,
- Mark A Nyman,
- Robert McClure,
- James McClay,
- Craig Parker,
- Karen M Hrabak,
- David Berg,
- Tony Weida,
- James G Mansfield,
- Mark A Musen,
- Robert M Abarbanel
- Affiliations of the authors: Department of Medicine, Stanford University School of Medicine (SWT, MAM), Stanford, CA; University of Nebraska Medical Center (JRC, JM, KMH) Omaha, NE; GE Healthcare Integrated IT Solutions (JG), Seattle, WA; Mayo Clinic (MAN), Rochester, MN; Apelon Inc. (TW, RM), Ridgefield, CT; RemedyMD Inc. (CP), Sandy, UT; Hospira, Inc. (RMA), Lake Forest, IL; Kea Analytics (JGM), Bothell, WA
- Correspondence and reprints: Samson W. Tu, MSOB X259, 251 Campus Drive, Stanford, CA 94305-5479; e-mail: <swt{at}stanford.edu>
- Received 4 February 2007
- Accepted 22 May 2007
Abstract
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.
Footnotes
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This work was partially supported by grant 70NANB1H3049 of the U.S. National Institute of Standards and Technology, Advanced Technology Program. The Protégé resource is supported by Grant P41 LM007885 from the National Library of Medicine. Views expressed are those of the authors and not necessarily those of affiliated organizations. The authors wish to thank Dr. Mor Peleg for detailed comments on the draft version of the paper.
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↵a Sections on workflow and information standards draw on previously published materials.13
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↵b http://www.jointcommission.org/PerformanceMeasurement/PerformanceMeasurement/Pneumonia+Core+Measure+Set.htm (Accessed on 4/30/2007).
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↵c http://www.ahrq.gov/clinic/pneumonia/pneumonia.htm#pneumonia (Accessed on 4/30/2007.)
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↵d The Presence Criterion in Figure 4 is equivalent to the following GELLO expression: Observation.exists(code.implies(“CHF disease affecting CAP risk code”) and effectiveTime.within(Now), where Observation is a collection of Observation instances, code and effectiveTime are attributes of the Observation class, implies and within are operators associated with HL7 CodedValue and interval of PointInTime data types, and Now is a variable whose value is the current time.
