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Methods and tools for the development of computer-interpretable guidelines |
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| EON |
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A component-based suite of models and software components for the creation of guideline-based applications |
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| keywords |
Clinical Practice Guidelines, Knowledge Representation,
Expert Systems, Computer-Assisted Decision Making |
| developed by |
Stanford Medical Informatics |
| introduced |
1996 |
| status |
EON project funding ended in March 2003. The SAGE project carried forward some of the work. |
| support |
US National Library of Medicine |
| in use |
ATHENA Hypertension advisory system deployed at a number US Department of Veteran
Affairs sites in the USA.
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| tools |
N/A
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| description |
The EON guideline modelling and execution system forms
part of the EON architecture, a component-based suite of models and software components for the creation of
guideline-based applications,
developed by Stanford University.
The central research questions of the EON system include:
- How to model clinical guidelines and
protocols to provide patient-specific
decision support?
- How to represent and reason with
time-oriented patient data?
- How to present and explain
decision-support recommendations and
conclusions?
- How to create a knowledge-engineering
environment for easy encoding of guidelines
and protocols?
EON includes an extensible suite of models to represent parts of a clinical
practice guideline, domain ontologies, a view of patient data (virtual medical
record), and other entities (e.g. those that define roles in an organization). The guideline
model (called the Dharma model) defines guideline knowledge structures such as eligibility criteria, abstraction definitions,
guideline algorithm, decision models, and recommended actions. The EON guideline execution
system obtains patient data through a specified temporal database manager or from user input,
and then generate recommendations according to the contents of the specific guideline.
Explanation for the recommendations, based on the Toulmin's argument structure,
are available to a user.
Encoding of EON guidelines is done in the Protégé-2000 knowledge-engineering
environment. The encoding process is facilitated by specialized views of the EON guideline model designed to satisfy specific requirements of different classes of guidelines.
Theses requirements are conceptualised in terms of a set of guideline tasks, e.g. decision making, specification of work to be performed, interpretation of data,
setting goals. A guideline developer using EON creates specialized views of the
guideline model by selecting modelling solutions to these tasks.
In the EON guideline model, conditional goals (e.g. if patient is diabetic, the
target blood pressures are 135/80) are associated with guidelines and
subguidelines. The guideline algorithm is represented
as a set of scenarios (cf the PRODIGY model for chronic care guidelines), action steps, decisions, branches, synchronisation
nodes connected by a "followed-by" relation.
EON provides three criteria languages to allow usability and medical expressivity:
- A simple object-oriented language that clinicians can use to encode the
majority of decision criteria
- A temporal query and abstraction language
- First-order predicate logic.
Among its benefits,
EON supported the reusability of medical domain knowledge, temporal queries and abstractions.
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| references |
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Tu SW, & Musen MA. Modeling Data and Knowledge in the EON Guideline Architecture.
Proc. MedInfo 2001, London, UK, 280-284. 2001.
[PubMed]
[SMI]
|
"
Compared to guideline representation formalisms, data and knowledge modeling for clinical guidelines is a relatively neglected area. Yet it has enormous impact on the format and expressiveness of decision criteria that can be written, on the inferences that can be made from patient data, on the ease with which guidelines can be formalized, and on the method of integrating guideline-based decision-support services into implementation sites' information systems. We clarify the respective roles that data and knowledge modeling play in providing patient-specific decision support based on clinical guidelines. We show, in the context of the EON guideline architecture, how we use the Protege-2000 knowledge-engineering environment to build (1) a patient-data information model, (2) a medical-specialty model, and (3) a guideline model that formalizes the knowledge needed to generate recommendations regarding clinical decisions and actions. We show how the use of such models allows development of alternative decision-criteria languages and allows systematic mapping of the data required for guideline execution from patient data contained in electronic medical record systems.
"
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| Shankar RD, Martins SB, Tu SW, Goldstein MK, Musen MA.
Building an explanation function for a hypertension decision-support system.
Medinfo. 2001;10(Pt 1):538-42.
[PubMed]
[Abstract - SMI]
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"
ATHENA DSS is a decision-support system that provides recommendations for managing hypertension in primary care. ATHENA DSS is built on a component-based architecture called EON. User acceptance of a system like this one depends partly on how well the system explains its reasoning and justifies its conclusions. We addressed this issue by adapting WOZ, a declarative explanation framework, to build an explanation function for ATHENA DSS. ATHENA DSS is built based on a component-based architecture called EON. The explanation function obtains its information by tapping into EON's components, as well as into other relevant sources such as the guideline document and medical literature. It uses an argument model to identify the pieces of information that constitute an explanation, and employs a set of visual clients to display that explanation. By incorporating varied information sources, by mirroring naturally occurring medical arguments and by utilizing graphic visualizations, ATHENA DSS's explanation function generates rich, evidence-based explanations.
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Goldstein MK, Hoffman BB, Coleman RW et al.
Patient safety in guideline-based decision support for hypertension management: ATHENA DSS.
Proc AMIA Symp. 2001;:214-8.
[PubMed]
[]
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"
The Institute of Medicine recently issued a landmark report on medical error.1 In the penumbra of this report, every aspect of health care is subject to new scrutiny regarding patient safety. Informatics technology can support patient safety by correcting problems inherent in older technology; however, new information technology can also contribute to new sources of error. We report here a categorization of possible errors that may arise in deploying a system designed to give guideline-based advice on prescribing drugs, an approach to anticipating these errors in an automated guideline system, and design features to minimize errors and thereby maximize patient safety. Our guideline implementation system, based on the EON architecture, provides a framework for a knowledge base that is sufficiently comprehensive to incorporate safety information, and that is easily reviewed and updated by clinician-experts.
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Goldstein MK, Hoffman BB, Coleman RW et al.
Implementing clinical practice guidelines while taking account of changing evidence: ATHENA DSS, an easily modifiable decision-support system for managing hypertension in primary care.
Proc AMIA Symp. 2000;:300-4.
[SMI]
[AMIA]
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"
This paper describes the ATHENA Decision Support System (DSS), which operationalizes guidelines for hypertension using the EON architecture. ATHENA DSS encourages blood pressure control and recommends guideline-concordant choice of drug therapy in relation to comorbid diseases. ATHENA DSS has an easily modifiable knowledge base that specifies eligibility criteria, risk stratification, blood pressure targets, relevant comorbid diseases, guideline-recommended drug classes for patients with comorbid disease, preferred drugs within each drug class, and clinical messages. Because evidence for best management of hypertension evolves continually, ATHENA DSS is designed to allow clinical experts to customize the knowledge base to incorporate new evidence or to reflect local interpretations of guideline ambiguities. Together with its database mediator Athenaeum, ATHENA DSS has physical and logical data independence from the legacy Computerized Patient Record System (CPRS) supplying the patient data, so it can be integrated into a variety of electronic medical record systems.
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| Tu SW, & Musen MA. From Guideline Modeling to Guideline Execution: Defining Guideline-Based Decision-Support Services.
Proc. AMIA Symposium, Los Angeles, CA, 863-867. 2000.
[SMI]
[]
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We describe the task-based approach we have taken to define guideline-based decision-support services that the EON system provides. We categorize uses of guidelines in patient-specific decision support into a set of generic tasks-decision-making, specification of work to be performed, interpretation of data, setting goal, and issuance of alert and reminders-that can be solved using various techniques. Our model contains modeling constructs required for representing knowledge used by these techniques. These constructs form a toolkit from which modeling solutions for guideline tasks can be selected. Based on the tasks and the guideline model, we define a guideline-execution architecture and a model of interactions between a decision-support server and clients that invoke services provided by the server. These services use generic interfaces derived from guideline tasks and their associated modeling constructs. We describe two implementations of these decision-support services and discuss how this work can be generalized. We argue that a well-defined specification of guideline-based decision-support services will facilitate sharing of tools that implement computable clinical guidelines.
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Tu SW, Musen MA.
A flexible approach to guideline modeling.
Proc AMIA Symp. 1999;:420-4.
[PubMed]
[SMI]
[Paper - AMIA]
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We describe a task-oriented approach to guideline modeling that we have been developing in the EON project. We argue that guidelines seek to change behaviors by making statements involving some or all of the following tasks (1) setting of goals or constraints, (2) making decisions among alternatives, (3) sequencing and synchronization of actions, and (4) interpreting data. Statements about these tasks make assumptions about models of time and of data abstractions, and about degree of uncertainty, points of view, and exception handling. Because of this variability in guideline tasks and assumptions, monolithic models cannot be custom tailored to the requirements of different classes of guidelines. Instead, we have created a core model that defines a set of basic concepts and relations and that uses different submodels to account for differing knowledge requirements. We describe the conceptualization of the guideline domain that underlies our approach, discuss components of the core model and possible submodels, and give three examples of specialized guideline models to illustrate how task-specific guideline models can be specialized and assembled to better match modeling requirements of different guidelines.
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| Musen, M.A., Tu, S.W., Das, A.K., and Shahar, Y. EON: A component-based approach to automation of protocol-directed therapy.
Journal of the American Medical Information Association 3(6): 367-388, 1996.
[PubMed]
[PubMedCentral]
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Provision of automated support for planning protocol-directed therapy requires a computer program to take as input clinical data stored in an electronic patient-record system, and to generate as output recommendations for therapeutic interventions and laboratory testing that are defined by applicable protocols. This paper presents a synthesis of research carried out at Stanford University to model the therapy-planning task, and to demonstrate a component-based architecture for building protocol-based decision-support systems. We have constructed general-purpose software components that (1) interpret abstract protocol specifications to construct appropriate patient-specific treatment plans; (2) infer from time-stamped patient data higher-level, interval-based, abstract concepts; (3) perform time-oriented queries on a time-oriented patient database; and (4) allow for acquisition and maintenance of protocol knowledge in a manner that facilitates efficient processing both by humans and by computers. We have implemented these components in a computer system known as EON. Each of the components has been developed and evaluated independently. We have evaluated the integration of the components as a composite architecture EON components by implementing T-HELPER, a computer-based patient- record system that uses EON to offer advice regarding the management of patients who have AIDS. A test of the reuse of the software components in a different clinical domain demonstrated rapid development of a prototype application to support protocol-based care of patients who have breast cancer.
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| Tu SW, Musen MA. The EON model of intervention protocols and guidelines.
Proc AMIA Annu Fall Symp. 1996;:587-91.
[PubMed]
[]
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"
We present a computational model of treatment protocols abstracted from implemented systems that we have developed previously. In our framework, a protocol is modeled as a hierarchical plan where high-level protocol steps are decomposed into descriptions of more specific actions. The clinical algorithms embodied in a protocol are represented by procedures that encode the sequencing, looping, and synchronization of protocol steps. The representation allows concurrent and optional protocol steps. We define the semantics of a procedure in terms of an execution model that specifies how the procedure should be interpreted. We show that the model can be applied to an asthma guideline different from the protocols for which the model was originally constructed.
" |
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| contact |
Samson Tu
Stanford Medical Informatics
Stanford University School of Medicine
Contact: Samson Tu
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| links |
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| acknowledgements |
| Samson Tu, Stanford Medical Informatics |
| page history |
Entry on OpenClinical: 2001
Last main updates: 1 December 2002; 14 March 2004.
Content archived 14 March 2004
Design - template v0.3: 25 June 2005. |
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EON