Craig G. Parker

The SAGE Guideline Model: Achievements and Overview

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.

Modeling guidelines for integration into clinical workflow.

The success of clinical decision-support systems requires that they are seamlessly integrated into clinical workflow. In the SAGE project, which aims to create the technological infra-structure for implementing computable clinical practice guide-lines in enterprise settings, we created a deployment-driven methodology for developing guideline knowledge bases. It involves (1) identification of usage scenarios of guideline-based care in clinical workflow, (2) distillation and disambiguation of guideline knowledge relevant to these usage scenarios, (3) formalization of data elements and vocabulary used in the guideline, and (4) encoding of usage scenarios and guideline knowledge using an executable guideline model. This methodology makes explicit the points in the care process where guideline-based decision aids are appropriate and the roles of clinicians for whom the guideline-based assistance is intended. We have evaluated the methodology by simulating the deployment of an immunization guideline in a real clinical information system and by reconstructing the workflow context of a deployed decision-support system for guideline-based care. We discuss the implication of deployment-driven guideline encoding for sharability of executable guidelines.

Detailed clinical models for sharable, executable guidelines.

The goal of shareable, executable clinical guidelines is both worthwhile and challenging. One of the largest hurdles is that of representing the necessary clinical information in a precise and shareable manner. Standard terminologies and common information models, such as the HL7 RIM, are necessary, they are not sufficient. In addition, common detailed clinical models are needed to give precise semantics and to make the task of mapping between models manageable. We discuss the experience of the SAGE project related to detailed clinical models.

Lessons learned in detailed clinical modeling at Intermountain Healthcare

BACKGROUND AND OBJECTIVE Intermountain Healthcare has a long history of using coded terminology and detailed clinical models (DCMs) to govern storage of clinical data to facilitate decision support and semantic interoperability. The latest iteration of DCMs at Intermountain is called the clinical element model (CEM). We describe the lessons learned from our CEM efforts with regard to subjective decisions a modeler frequently needs to make in creating a CEM. We present insights and guidelines, but also describe situations in which use cases conflict with the guidelines. We propose strategies that can help reconcile the conflicts. The hope is that these lessons will be helpful to others who are developing and maintaining DCMs in order to promote sharing and interoperability. METHODS We have used the Clinical Element Modeling Language (CEML) to author approximately 5000 CEMs. RESULTS Based on our experience, we have formulated guidelines to lead our modelers through the subjective decisions they need to make when authoring models. Reported here are guidelines regarding precoordination/postcoordination, dividing content between the model and the terminology, modeling logical attributes, and creating iso-semantic models. We place our lessons in context, exploring the potential benefits of an implementation layer, an iso-semantic modeling framework, and ontologic technologies. CONCLUSIONS We assert that detailed clinical models can advance interoperability and sharing, and that our guidelines, an implementation layer, and an iso-semantic framework will support our progress toward that goal.

Chapter 17 – Ontologies, Vocabularies and Data Models

The purpose of this chapter is to describe current vocabulary and terminology issues and challenges related specifically to the successful implementation of clinical decision support (CDS) systems. The chapter discusses: why standard coded data are essential for accurate and reliable execution of decision logic; how to unambiguously reference data in the electronic health record (EHR) from CDS expressions; alternatives for pre- and post-coordinated representations of data; representation of patient data as name-value pairs; the relationship between terms and information/data models which provide the context of use; terminology in the life cycle of CDS; the next steps that are needed in standardizing models and terminology for use in CDS.