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.

Protégé: a tool for managing and using terminology in radiology applications

The development of standard terminologies such as RadLex is becoming important in radiology applications, such as structured reporting, teaching file authoring, report indexing, and text mining. The development and maintenance of these terminologies are challenging, however, because there are few specialized tools to help developers to browse, visualize, and edit large taxonomies. Protégé (http://protege.stanford.edu) is an open-source tool that allows developers to create and to manage terminologies and ontologies. It is more than a terminology-editing tool, as it also provides a platform for developers to use the terminologies in end-user applications. There are more than 70,000 registered users of Protégé who are using the system to manage terminologies and ontologies in many different domains. The RadLex project has recently adopted Protégé for managing its radiology terminology. Protégé provides several features particularly useful to managing radiology terminologies: an intuitive graphical user interface for navigating large taxonomies, visualization components for viewing complex term relationships, and a programming interface so developers can create terminology-driven radiology applications. In addition, Protégé has an extensible plug-in architecture, and its large user community has contributed a rich library of components and extensions that provide much additional useful functionalities. In this report, we describe Protégé’s features and its particular advantages in the radiology domain in the creation, maintenance, and use of radiology terminology.

Annotation and query of tissue microarray data using the NCI Thesaurus

Background

The Stanford Tissue Microarray Database (TMAD) is a repository of data serving a consortium of pathologists and biomedical researchers. The tissue samples in TMAD are annotated with multiple free-text fields, specifying the pathological diagnoses for each sample. These text annotations are not structured according to any ontology, making future integration of this resource with other biological and clinical data difficult.

Results

We developed methods to map these annotations to the NCI thesaurus. Using the NCI-T we can effectively represent annotations for about 86% of the samples. We demonstrate how this mapping enables ontology driven integration and querying of tissue microarray data. We have deployed the mapping and ontology driven querying tools at the TMAD site for general use.

Conclusion

We have demonstrated that we can effectively map the diagnosis-related terms describing a sample in TMAD to the NCI-T. The NCI thesaurus terms have a wide coverage and provide terms for about 86% of the samples. In our opinion the NCI thesaurus can facilitate integration of this resource with other biological data.

Linking ontologies with three-dimensional models of anatomy to predict the effects of penetrating injuries

Rapid diagnosis of penetrating injuries is essential to increased chance of survival. Geometric models representing anatomic structures could be useful, but such models generally contain only information about the relationships of points in space as well as display properties. We describe an approach to predicting the anatomic consequences of penetrating injury by creating a geometric model of anatomy that integrates biomechanical and anatomic knowledge. We created a geometric model of the heart from the Visible Human image data set. We linked this geometric model of anatomy with an ontology of descriptive anatomic knowledge. A hierarchy of abstract geometric objects was created that represents organs and organ parts. These geometric objects contain information about organ identity, composition, adjacency, and tissue biomechanical properties. This integrated model can support anatomic reasoning. Given a bullet trajectory and a parametric representation of a cone of tissue damage, we can use our model to predict the organs and organ parts that are injured. Our model is extensible, being able to incorporate future information, such as physiological implications of organ injuries.

OBO to OWL: a protege OWL tab to read/save OBO ontologies

The Open Biomedical Ontologies (OBO) format from the GO consortium is a very successful format for biomedical ontologies, including the Gene Ontology. But it lacks formal computational definitions for its constructs and tools, like DL reasoners, to facilitate ontology development/maintenance. We describe the OBO Converter, a Java tool to convert files from OBO format to Web Ontology Language (OWL) (and vice versa) that can also be used as a Protégé Tab plug-in. It uses the OBO to OWL mapping provided by the National Center for Biomedical Ontologies (NCBO) (a joint effort of OBO developers and OWL experts) and offers options to ease the task of saving/reading files in both formats.

AVAILABILITY

bioontology.org/tools/oboinowl/obo_converter.html.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Evaluating detection of an inhalational anthrax outbreak

Timely detection of an inhalational anthrax outbreak is critical for clinical and public health management. Syndromic surveillance has received considerable investment, but little is known about how it will perform relative to routine clinical case finding for detection of an inhalational anthrax outbreak. We conducted a simulation study to compare clinical case finding with syndromic surveillance for detection of an outbreak of inhalational anthrax. After simulated release of 1 kg of anthrax spores, the proportion of outbreaks detected first by syndromic surveillance was 0.59 at a specificity of 0.9 and 0.28 at a specificity of 0.975. The mean detection benefit of syndromic surveillance was 1.0 day at a specificity of 0.9 and 0.32 days at a specificity of 0.975. When syndromic surveillance was sufficiently sensitive to detect a substantial proportion of outbreaks before clinical case finding, it generated frequent false alarms.

Using semantic dependencies for consistency management of an ontology of brain-cortex anatomy

OBJECTIVE

In the context of the Semantic Web, ontologies have to be usable by software agents as well as by humans. Therefore, they must meet explicit representation and consistency requirements. This article describes a method for managing the semantic consistency of an ontology of brain-cortex anatomy.

METHOD

The methodology relies on the explicit identification of the relationship properties and of the dependencies that might exist among concepts or relationships. These dependencies have to be respected for insuring the semantic consistency of the model. We propose a method for automatically generating all the dependent items. As a consequence, knowledge base updates are easier and safer.

RESULT

Our approach is composed of three main steps: (1) providing a realistic representation, (2) ensuring the intrinsic consistency of the model and (3) checking its incremental consistency. The corner stone of ontological modeling lies in the expressiveness of the model and in the sound principles that structure it. This part defines the ideal possibilities of the ontology and is called realism of representation. Regardless of how well a model represents reality, the intrinsic consistency of a model corresponds to its lack of contradiction. This step is particularly important as soon as dependencies between relationships or concepts have to be fulfilled. Eventually, the incremental consistency encompasses the respect of the two previous criteria during the successive updates of the ontology.

CONCLUSION

The explicit representation of dependencies among concepts and relationships in an ontology can be helpfully used to assist in the management of the knowledge base and to ensure the model's semantic consistency.

Using ontologies linked with geometric models to reason about penetrating injuries

OBJECTIVE

Medical assessment of penetrating injuries is a difficult and knowledge-intensive task, and rapid determination of the extent of internal injuries is vital for triage and for determining the appropriate treatment. Physical examination and computed tomographic (CT) imaging data must be combined with detailed anatomic, physiologic, and biomechanical knowledge to assess the injured subject. We are developing a methodology to automate reasoning about penetrating injuries using canonical knowledge combined with specific subject image data.

METHODS AND MATERIAL

In our approach, we build a three-dimensional geometric model of a subject from segmented images. We link regions in this model to entities in two knowledge sources: (1) a comprehensive ontology of anatomy containing organ identities, adjacencies, and other information useful for anatomic reasoning and (2) an ontology of regional perfusion containing formal definitions of arterial anatomy and corresponding regions of perfusion. We created computer reasoning services ("problem solvers") that use the ontologies to evaluate the geometric model of the subject and deduce the consequences of penetrating injuries.

RESULTS

We developed and tested our methods using data from the Visible Human. Our problem solvers can determine the organs that are injured given particular trajectories of projectiles, whether vital structures--such as a coronary artery--are injured, and they can predict the propagation of injury ensuing after vital structures are injured.

CONCLUSION

We have demonstrated the capability of using ontologies with medical images to support computer reasoning about injury based on those images. Our methodology demonstrates an approach to creating intelligent computer applications that reason with image data, and it may have value in helping practitioners in the assessment of penetrating injury.

Identifying barriers to hypertension guideline adherence using clinician feedback at the point of care

Factors contributing to low adherence to clinical guidelines by clinicians are not well understood. The user interface of ATHENA-HTN, a guideline-based decision support system (DSS) for hypertension, presents a novel opportunity to collect clinician feedback on recommendations displayed at the point of care. We analyzed feedback from 46 clinicians who received ATHENA advisories as part of a 15-month randomized trial to identify potential reasons clinicians may not intensify hypertension therapy when it is recommended. Among the 368 visits for which feedback was provided, clinicians commonly reported they did not follow recommendations because: recorded blood pressure was not representative of the patient's typical blood pressure; hypertension was not a clinical priority for the visit; or patients were nonadherent to medications. For many visits, current quality-assurance algorithms may incorrectly identify clinically appropriate decisions as guideline nonadherent due to incomplete capture of relevant information. We present recommendations for how automated DSSs may help identify "apparent" barriers and better target decision support.

Ontology-based representation of simulation models of physiology

Dynamic simulation models of physiology are often represented as a set of mathematical equations. Such models are very useful for studying and understanding the dynamic behavior of physiological variables. However, the sheer number of equations and variables can make these models unwieldy, difficult to under-stand, and challenging to maintain. We describe a symbolic, ontologically-guided methodology for representing a physiological model of the circulation. We created an ontology describing the types of equations in the model as well as the anatomic components and how they are connected to form a circulatory loop. The ontology provided an explicit representation of the model, both its mathematical and anatomic content, abstracting and hiding much of the mathematical complexity. The ontology also provided a framework to construct a graphical representation of the model, providing a simpler visualization than the large set of mathematical equations. Our approach may help model builders to maintain, debug, and extend simulation models.

Ontology-based annotation and query of tissue microarray data

The Stanford Tissue Microarray Database (TMAD) is a repository of data amassed by a consortium of pathologists and biomedical researchers. The TMAD data are annotated with multiple free-text fields, specifying the pathological diagnoses for each tissue sample. These annotations are spread out over multiple text fields and are not structured according to any ontology, making it difficult to integrate this resource with other biological and clinical data. We developed methods to map these annotations to the NCI thesaurus and the SNOMED-CT ontologies. Using these two ontologies we can effectively represent about 80% of the annotations in a structured manner. This mapping offers the ability to perform ontology driven querying of the TMAD data. We also found that 40% of annotations can be mapped to terms from both ontologies, providing the potential to align the two ontologies based on experimental data. Our approach provides the basis for a data-driven ontology alignment by mapping annotations of experimental data.

Use of declarative statements in creating and maintaining computer-interpretable knowledge bases for guideline-based care

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.

Use of description logic classification to reason about consequences of penetrating injuries

The consequences of penetrating injuries can be complex, including abnormal blood flow through the injury channel and functional impairment of organs if arteries supplying them have been severed. Determining the consequences of such injuries can be posed as a classification problem, requiring a priori symbolic knowledge of anatomy. We hypothesize that such symbolic knowledge can be modeled using ontologies, and that the reasoning task can be accomplished using knowl-edge representation in description logics (DL) and automatic classification. We demonstrate the capabilities of automated classification using the Web Ontology Language (OWL) to reason about the consequences of penetrating injuries. We created in OWL a knowledge model of chest and heart anatomy describing the heart structure and the surrounding anatomic compartments, as well as the perfusion of regions of the heart by branches of the coronary arteries. We then used a domain-independent classifier to infer ischemic regions of the heart as well as anatomic spaces containing ectopic blood secondary to the injuries. Our results highlight the advantages of posing reasoning problems as a classification task, and lever-aging the automatic classification capabilities of DL to create intelligent applications.

Using an ontology of human anatomy to inform reasoning with geometric models

The Virtual Soldier project is a large effort on the part of the U.S. Defense Advanced Research Projects agency to explore using both general anatomical knowledge and specific computed tomographic (CT) images of individual soldiers to aid the rapid diagnosis and treatment of penetrating injuries. Our goal is to develop intelligent computer applications that use this knowledge to reason about the anatomic structures that are directly injured and to predict propagation of injuries secondary to primary organ damage. To accomplish this, we needed to develop an architecture to combine geometric data with anatomic knowledge and reasoning services that use this information to predict the consequences of injuries.

Challenges in converting frame-based ontology into OWL: the Foundational Model of Anatomy case-study

A description logics representation of the Foundational Model of Anatomy (FMA) in the Web Ontology Language (OWL-DL) would allow developers to combine it with other OWL ontologies, and would provide the benefit of being able to access generic reasoning tools. However, the FMA is currently represented in a frame language. The differences between description logics and frames are not only syntactic, but also semantic. We analyze some theoretical and computational limitations of converting the FMA into OWL-DL. Namely, some of the constructs used in the FMA do not have a direct equivalent in description logics, and a complete conversion of the FMA in description logics is too large to support reasoning. Therefore, an OWL-DL representation of the FMA would have to be optimized for each application. We propose a solution based on OWL-Full, a superlanguage of OWL-DL, that meets the expressiveness requirements and remains application-independent. Specific simplified OWL-DL representations can then be generated from the OWL-Full model by applications. We argue that this solution is easier to implement and closer to the application needs than an integral translation, and that the latter approach would only make the FMA maintenance more difficult.

Translating research into practice: organizational issues in implementing automated decision support for hypertension in three medical centers

Information technology can support the implementation of clinical research findings in practice settings. Technology can address the quality gap in health care by providing automated decision support to clinicians that integrates guideline knowledge with electronic patient data to present real-time, patient-specific recommendations. However, technical success in implementing decision support systems may not translate directly into system use by clinicians. Successful technology integration into clinical work settings requires explicit attention to the organizational context. We describe the application of a "sociotechnical" approach to integration of ATHENA DSS, a decision support system for the treatment of hypertension, into geographically dispersed primary care clinics. We applied an iterative technical design in response to organizational input and obtained ongoing endorsements of the project by the organization's administrative and clinical leadership. Conscious attention to organizational context at the time of development, deployment, and maintenance of the system was associated with extensive clinician use of the system.

The SAGE guideline modeling: motivation and methodology

The SAGE (Standards-Based Sharable Active Guideline Environment) project is a collaboration among research groups at six institutions in the US. The ultimate goal of the project is to create an infrastructure that will allow execution of standards-based clinical practice guidelines across heterogeneous clinical information systems. This paper describes the design goals of the SAGE guideline model in the context of the technological infrastructure and guideline modeling methodology that the project is developing.

Evaluating provider adherence in a trial of a guideline-based decision support system for hypertension

Measurement of provider adherence to a guideline-based decision support system (DSS) presents a number of important challenges. Establishing a causal relationship between the DSS and change in concordance requires consideration of both the primary intention of the guideline and different ways providers attempt to satisfy the guideline. During our work with a guideline-based decision support system for hypertension, ATHENA DSS, we document a number of subtle deviations from the strict hypertension guideline recommendations that ultimately demonstrate provider adherence. We believe that understanding these complexities is crucial to any valid evaluation of provider adherence. We also describe the development of an advisory evaluation engine that automates the interpretation of clinician adherence with the DSS on multiple levels, facilitating the high volume of complex data analysis that is created in a clinical trial of a guideline-based DSS.

An intelligent case-adjustment algorithm for the automated design of population-based quality auditing protocols

We develop a method and algorithm for deciding the optimal approach to creating quality-auditing protocols for guideline-based clinical performance measures. An important element of the audit protocol design problem is deciding which guide-line elements to audit. Specifically, the problem is how and when to aggregate individual patient case-specific guideline elements into population-based quality measures. The key statistical issue involved is the trade-off between increased reliability with more general population-based quality measures versus increased validity from individually case-adjusted but more restricted measures done at a greater audit cost. Our intelligent algorithm for auditing protocol design is based on hierarchically modeling incrementally case-adjusted quality constraints. We select quality constraints to measure using an optimization criterion based on statistical generalizability coefficients. We present results of the approach from a deployed decision support system for a hypertension guideline.

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.

An analytic framework fo space-time aberrancy detection in public health surveillance data

Public health surveillance is changing in response to concerns about bioterrorism, which have increased the pressure for early detection of epidemics. Rapid detection necessitates following multiple non-specific indicators and accounting for spatial structure. No single analytic method can meet all of these requirements for all data sources and all surveillance goals. Analytic methods must be selected and configured to meet a surveillance goal, but there are no uniform criteria to guide the selection and configuration process. In this paper, we describe work towards the development of an analytic framework for space-time aberrancy detection in public health surveillance data. The framework decomposes surveillance analysis into sub-tasks and identifies knowledge that can facilitate selection of methods to accomplish sub-tasks.