Modeling data and knowledge in the EON guideline architecture.

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 Protégé-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.

Representation of clinical practice guidelines for computer-based implementations.

Representation of clinical practice guidelines is a critical issue for computer-based guideline development, implementation and evaluation. We studied eight types of computer-based guideline representation models. Typical primitives for these models include decisions, actions, patient states and execution states. Temporal constraints and nesting are important aspects of guideline structure representation. Integration of guidelines with electronic medical records can be facilitated by the introduction of formal models of patient data. Patient states and execution states are closely related to one another. Data collection, decision, patient state and intervention are four basic steps in a guideline's logic flow.

RASTA: a distributed temporal abstraction system to facilitate knowledge-driven monitoring of clinical databases.

The time dimension is very important for applications that reason with clinical data. Unfortunately, this task is inherently computationally expensive. As clinical decision support systems tackle increasingly varied problems, they will increase the demands on the temporal reasoning component, which may lead to slow response times. This paper addresses this problem. It describes a temporal reasoning system called RASTA that uses a distributed algorithm that enables it to deal with large data sets. The algorithm also supports a variety of configuration options, enabling RASTA to deal with a range of application requirements.

Building an explanation function for a hypertension decision-support system.

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.

Patient safety in guideline-based decision support for hypertension management: ATHENA DSS.

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.

A virtual medical record for guideline-based decision support.

A major obstacle in deploying computer-based clinical guidelines at the point of care is the variability of electronic medical records and the consequent need to adapt guideline modeling languages, guideline knowledge bases, and execution engines to idiosyncratic data models in the deployment environment. This paper reports an approach, developed jointly by researchers at Newcastle and Stanford, where guideline models are encoded assuming a uniform virtual electronic medical record and guideline-specific concept ontologies. For implementing a guideline-based decision-support system in multiple deployment environments, we created mapping knowledge bases to link terms in the concept ontology with the terminology used in the deployment systems. Mediation components use these mapping knowledge bases to map data in locally deployed medical record architectures to the virtual medical record. We discuss the possibility of using the HL7 Reference Information Model (RIM) as the basis for a standardized virtual medical record, showing how this approach also complies with the European pre-standard ENV13606 for electronic healthcare record communication.

Integration of textual guideline documents with formal guideline knowledge bases.

Numerous approaches have been proposed to integrate the text of guideline documents with guideline-based care systems. Current approaches range from serving marked up guideline text documents to generating advisories using complex guideline knowledge bases. These approaches have integration problems mainly because they tend to rigidly link the knowledge base with text. We are developing a bridge approach that uses an information retrieval technology. The new approach facilitates a versatile decision-support system by using flexible links between the formal structures of the knowledge base and the natural language style of the guideline text.

Implementing clinical practice guidelines while taking account of changing evidence: ATHENA DSS, an easily modifiable decision-support system for managing hypertension in primary care.

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.

Representation of temporal indeterminacy in clinical databases.

Temporal indeterminancy is common in clinical medicine because the time of many clinical events is frequently not precisely known. Decision support systems that reason with clinical data may need to deal with this indeterminancy. This indeterminacy support must have a sound foundational model so that other system components may take advantage of it. In particular, it should operate in concert with temporal abstraction, a feature that is crucial in several clinical decision support systems that our group has developed. We have implemented a temporal query system called Tzolkin that provides extensive support for the temporal indeterminancies found in clinical medicine, and have integrated this support with our temporal abstraction mechanism. The resulting system provides a simple, yet powerful approach for dealing with temporal indeterminancy and temporal abstraction.

From guideline modeling to guideline execution: defining guideline-based decision-support services.

We describe our task-based approach to defining the 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--making of decisions, specification of work to be performed, interpretation of data, setting of goals, and issuance of alert and reminders--that can be solved using various techniques. Our model includes constructs required for representing the knowledge used by these techniques. These constructs form a toolkit from which developers can select modeling solutions for guideline task. 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.

Applying temporal joins to clinical databases.

Clinical databases typically contain a significant amount of temporal information, information that is often crucial in medical decision-support systems. Most recent clinical information systems use the relational model when working with this information. Although these systems have reasonably well-defined semantics for temporal queries on a single relational table, many do not fully address the complex semantics of operations involving multiple temporal tables. Such operations can arise frequently in queries on clinical databases. This paper describes the issues encountered when joining a set of temporal tables, and outlines how such joins are far more complex than non-temporal ones. We describe the semantics of temporal joins in a query management system called Chronus II, a system we have developed to assist in evaluating patients for clinical trials.

A flexible approach to guideline modeling.

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.

The guideline interchange format: a model for representing guidelines.

OBJECTIVE: To allow exchange of clinical practice guidelines among institutions and computer-based applications. DESIGN: The GuideLine Interchange Format (GLIF) specification consists of GLIF model and the GLIF syntax. The GLIF model is an object-oriented representation that consists of a set of classes for guideline entities, attributes for those classes, and data types for the attribute values. The GLIF syntax specifies the format of the test file that contains the encoding. METHODS: Researchers from the InterMed Collaboratory at Columbia University, Harvard University (Brigham and Women's Hospital and Massachusetts General Hospital), and Stanford University analyzed four existing guideline systems to derive a set of requirements for guideline representation. The GLIF specification is a consensus representation developed through a brainstorming process. Four clinical guidelines were encoded in GLIF to assess its expressivity and to study the variability that occurs when two people from different sites encode the same guideline. RESULTS: The encoders reported that GLIF was adequately expressive. A comparison of the encodings revealed substantial variability. CONCLUSION: GLIF was sufficient to model the guidelines for the four conditions that were examined. GLIF needs improvement in standard representation of medical concepts, criterion logic, temporal information, and uncertainty.

A declarative explanation framework that uses a collection of visualization agents.

User acceptance of a knowledge-based system depends partly on how effective the system is in explaining its reasoning and justifying its conclusions. The WOZ framework provides effective explanations for component-based decision-support systems. It represents explanation using explicit models, and employs a collection of visualization agents. It blends the strong features of existing explanation strategies, component-based systems, graphical visualizations, and explicit models. We illustrate the features of WOZ with the help of a component-based medical therapy system. We describe the explanation strategy, the roles of the visualization agents and components, and the communication structure. The integration of existing and new visualization applications, the domain-independent framework, and the incorporation of varied knowledge sources for explanation can result in a flexible explanation facility.

A temporal database mediator for protocol-based decision support.

To meet the data-processing requirements for protocol-based decision support, a clinical data-management system must be capable of creating high-level summaries of time-oriented patient data, and of retrieving those summaries in a temporally meaningful fashion. We previously described a temporal-abstraction module (RESUME) and a temporal-querying module (Chronus) that can be used together to perform these tasks. These modules had to be coordinated by individual applications, however, to resolve the temporal queries of protocol planners. In this paper, we present a new module that integrates the previous two modules and that provides for their coordination automatically. The new module can be used as a standalone system for retrieving both primitive and abstracted time-oriented data, or can be embedded in a larger computational framework for protocol-based reasoning.

EON: a component-based approach to automation of protocol-directed therapy.

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 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, evaluated, and reported independently. We have evaluated the integration of the components as a composite architecture by implementing T-HELPER, a computer-based patient-record system that uses EON to offer advice regarding the management of patients who are following clinical trial protocols for AIDS or HIV infection. 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.

Toward reusable software components at the point of care.

An architecture built from five software components -a Router, Parser, Matcher, Mapper, and Server -fulfills key requirements common to several point-of-care information and knowledge processing tasks. The requirements include problem-list creation, exploiting the contents of the Electronic Medical Record for the patient at hand, knowledge access, and support for semantic visualization and software agents. The components use the National Library of Medicine Unified Medical Language System to create and exploit lexical closure-a state in which terms, text and reference models are represented explicitly and consistently. Preliminary versions of the components are in use in an oncology knowledge server.

The EON model of intervention protocols and guidelines.

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.

Ontology-based configuration of problem-solving methods and generation of knowledge-acquisition tools: application of PROTEGE-II to protocol-based decision support.

PROTEGE-II is a suite of tools and a methodology for building knowledge-based systems and domain-specific knowledge-acquisition tools. In this paper, we show how PROTEGE-II can be applied to the task of providing protocol-based decision support in the domain of treating HIV-infected patients. To apply PROTEGE-II, (1) we construct a decomposable problem-solving method called episodic skeletal-plan refinement, (2) we build an application ontology that consists of the terms and relations in the domain, and of method-specific distinctions not already captured in the domain terms, and (3) we specify mapping relations that link terms from the application ontology to the domain-independent terms used in the problem-solving method. From the application ontology, we automatically generate a domain-specific knowledge-acquisition tool that is custom-tailored for the application. The knowledge-acquisition tool is used for the creation and maintenance of domain knowledge used by the problem-solving method. The general goal of the PROTEGE-II approach is to produce systems and components that are reusable and easily maintained. This is the rationale for constructing ontologies and problem-solving methods that can be composed from a set of smaller-grained methods and mechanisms. This is also why we tightly couple the knowledge-acquisition tools to the application ontology that specifies the domain terms used in the problem-solving systems. Although our evaluation is still preliminary, for the application task of providing protocol-based decision support, we show that these goals of reusability and easy maintenance can be achieved. We discuss design decisions and the tradeoffs that have to be made in the development of the system.

Computer-based screening of patients with HIV/AIDS for clinical-trial eligibility.

OBJECTIVE: To assess the potential effect of a computer-based system on accrual to clinical trials, we have developed methodology to identify retrospectively and prospectively patients who are eligible or potentially eligible for protocols. DESIGN: Retrospective chart abstraction with computer screening of data for potential protocol eligibility. SETTING: A county-operated clinic serving human immunodeficiency virus (HIV) positive patients with or without acquired immune deficiency syndrome (AIDS). PATIENTS: A randomly selected group of 60 patients who were HIV-infected, 30 of whom had an AIDS-defining diagnosis. DESIGN: Using a computer-based eligibility screening system, for each clinic visit and hospitalization, patients were categorized as eligible, potentially eligible, or ineligible for each of the 17 protocols active during the 7-month study period. Reasons for ineligibility were categorized. RESULTS: None of the patients was enrolled on a clinical trial during the 7-month period. Thirteen patients were identified as eligible for protocol; three patients were eligible for two different protocols; and one patient was eligible for the same protocol during two different time intervals. Fifty-four patients were identified as potentially eligible for a total of 165 accrual opportunities, but important information, such as the result of a required laboratory test, was missing, so that eligibility could not be determined unequivocally. Ineligibility for protocol was determined in 414 (35%) potential opportunities based only on conditions that were amenable to modification, such as the use of concurrent medications; 194 (17%) failed only laboratory tests or subjective determinations not routinely performed; and 346 (29%) failed only routine laboratory tests. CONCLUSIONS: There are substantial numbers of eligible and potentially eligible patients who are not enrolled or evaluated for enrollment in prospective clinical trials. Computer-based eligibility screening when coupled with a computer-based medical record offers the potential to identify patients eligible or potentially eligible for clinical trial, to assist in the selection of protocol eligibility criteria, and to make accrual estimates.