Autism spectrum disorder is an inherently complex phenomenon requiring large studies of many different types to further understanding of its causes. The National Database for Autism Research (NDAR) is being constructed to aid in this effort by providing a means for researchers to share and integrate data. An autism ontology drafted by a group at Stanford is being incorporated for use by NDAR to allow semantic data integration. The architecture upon which NDAR is built - the UCSD developed data integration environment - supports the use of this autism ontology, including annotation of data with ontological concepts and ontology enhanced queries on databases, both central and federated.

OBJECTIVE: Statistical aberrancy-detection algorithms play a central role in automated public health systems, analyzing large volumes of clinical and administrative data in real-time with the goal of detecting disease outbreaks rapidly and accurately. Not all algorithms perform equally well in terms of sensitivity, specificity, and timeliness in detecting disease outbreaks and the evidence describing the relative performance of different methods is fragmented and mainly qualitative. DESIGN: We developed and evaluated a unified model of aberrancy-detection algorithms and a software infrastructure that uses this model to conduct studies to evaluate detection performance. We used a task-analytic methodology to identify the common features and meaningful distinctions among different algorithms and to provide an extensible framework for gathering evidence about the relative performance of these algorithms using a number of evaluation metrics. We implemented our model as part of a modular software infrastructure (Biological Space-Time Outbreak Reasoning Module, or BioSTORM) that allows configuration, deployment, and evaluation of aberrancy-detection algorithms in a systematic manner. MEASUREMENT: We assessed the ability of our model to encode the commonly used EARS algorithms and the ability of the BioSTORM software to reproduce an existing evaluation study of these algorithms. RESULTS: Using our unified model of aberrancy-detection algorithms, we successfully encoded the EARS algorithms, deployed these algorithms using BioSTORM, and were able to reproduce and extend previously published evaluation results. CONCLUSION: The validated model of aberrancy-detection algorithms and its software implementation will enable principled comparison of algorithms, synthesis of results from evaluation studies, and identification of surveillance algorithms for use in specific public health settings.

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.

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.

InterMed is a collaboration among research groups from Stanford, Harvard, and Columbia Universities. The primary goal of InterMed has been to develop a sharable language that could serve as a standard for modeling computer-interpretable guidelines (CIGs). This language, called GuideLine Interchange Format (GLIF), has been developed in a collaborative manner and in an open process that has welcomed input from the larger community. The goals and experiences of the InterMed project and lessons that the authors have learned may contribute to the work of other researchers who are developing medical knowledge-based tools. The lessons described include (1) a work process for multi-institutional research and development that considers different viewpoints, (2) an evolutionary lifecycle process for developing medical knowledge representation formats, (3) the role of cognitive methodology to evaluate and assist in the evolutionary development process, (4) development of an architecture and (5) design principles for sharable medical knowledge representation formats, and (6) a process for standardization of a CIG modeling language.

OBJECTIVES: Many groups are developing computer-interpretable clinical guidelines (CIGs) for use during clinical encounters. CIGs use "Task-Network Models" for representation but differ in their approaches to addressing particular modeling challenges. We have studied similarities and differences between CIGs in order to identify issues that must be resolved before a consensus on a set of common components can be developed. DESIGN: We compared six models: Asbru, EON, GLIF, GUIDE, PRODIGY, and PROforma. Collaborators from groups that created these models represented, in their own formalisms, portions of two guidelines: American College of Chest Physicians cough guidelines [correction] and the Sixth Report of the Joint National Committee on Prevention, Detection, Evaluation, and Treatment of High Blood Pressure. MEASUREMENTS: We compared the models according to eight components that capture the structure of CIGs. The components enable modelers to encode guidelines as plans that organize decision and action tasks in networks. They also enable the encoded guidelines to be linked with patient data-a key requirement for enabling patient-specific decision support. RESULTS: We found consensus on many components, including plan organization, expression language, conceptual medical record model, medical concept model, and data abstractions. Differences were most apparent in underlying decision models, goal representation, use of scenarios, and structured medical actions. CONCLUSION: We identified guideline components that the CIG community could adopt as standards. Some of the participants are pursuing standardization of these components under the auspices of HL7.

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.

This paper describes successive reformulations of skeletal-plan refinement as a problem-solving method. We argue that, whereas ideas derived from planning literature helped to determine the overall structure of the planning systems, domain-derived considerations and architectural framework in which the systems were implemented played important roles in these reformulations. We illustrate the argument by describing a new framework that integrates knowledge-based applications with a temporal data-abstraction and data-management system. In this framework, both applications and temporal-data mediators are encapsulated as Common Object Request Broker Architecture (CORBA) objects. The skeletal-plan refinement method itself is formulated as a collection of cooperating CORBA objects. We have found that we needed to reformulate the method ontology, mapping relations and control structure of the skeletal-planning problem-solving method in this framework. Our experience suggests that problem-solving methods are not necessarily fixed structures that can be plugged into arbitrary application environments, and that we need to develop a flexible configuration environment and expressive mapping formalisms to accommodate the requirements of application environments. These requirements include the ways data are made available and the ways software components interact with one another.

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.

This paper describes how we applied the PROTÉGÉ-II architecture to build a knowledge-based system that configures elevators. The elevator-configuration task was solved originally with a system that employed the propose-and-revise problem-solving method (VT). A variant of this task, here named the Sisyphus-2 problem, is used by the knowledge-acquisition community for comparative studies. PROTÉGÉ-II is a knowledge-engineering environment that focuses on the use of reusable ontologies and problem-solving methods to generate task-specific knowledge-acquisition tools and executable problem solvers. The main goal of this paper is to describe in detail how we used PROTÉGÉ-II to model the elevator-configuration task. This description provides a starting point for comparison with other frameworks that use abstract problem-solving methods. Beginning with the textual description of the elevator-configuration task, we analysed the domain knowledge with respect to PROTÉGÉ-II’s main goal: to build domain-specific knowledge-acquisition tools. We used PROTÉGÉ-II’s suite of tools to construct a knowledge-based system, called ELVIS, that includes a reusable domain ontology, a knowledge-acquisition tool, and a propose-and-revise problem-solving method that is optimized to solve the elevator-configuration task. We entered domain-specific knowledge about elevator configuration into the knowledge base with the help of a task-specific knowledge-acquisition tool that PROTÉGÉ-II generated from the ontologies. After we constructed mapping relations to connect the knowledge base with the method’s code, the final executable problem solver solved the test case provided with the Sisyphus-2 material. We have found that the development of ELVIS has afforded a valuable test case for evaluating PROTÉGÉ-II’s suite of system-building tools. Only projects based on reasonably large problems, such as the Sisyphus-2 task, will allow us to improve the design of PROTÉGÉ-II and its ability to produce reusable components.

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.

Problem-solving methods for knowledge-based systems establish the behavior of such systems by defining the roles in which domain knowledge is used and the ordering of inferences. Developers can compose problem-solving methods that accomplish complex application tasks from primitive, reusable methods. The key steps in this development approach are task analysis, method selection (from a library), and method configuration. Protégé-ii is a knowledge-engineering environment that allows developers to select and configure problem-solving methods. In addition, Protégé-ii generates domain-specific knowledge-acquisition tools that domain specialists can use to create knowledge bases on which the methods may operate. The board-game method is a problem-solving method that defines control knowledge for a class of tasks that developers can model in a highly specific way. The method adopts a conceptual model of problem solving in which the solution space is construed as a “game board” on which the problem solver moves “playing pieces” according to prespecified rules. This familiar conceptual model simplifies the developer's cognitive demands when configuring the board-game method to support new application tasks. We compare configuration of the board-game method to that of a chronological-backtracking problem-solving method for the same application tasks (for example, towers of Hanoi and the Sisyphus room-assignment problem). We also examine how method designers can specialize problem-solving methods by making ontological commitments to certain classes of tasks. We exemplify this technique by specializing the chronological-backtracking method to the board-game method.

Building a problem solver and acquiring the knowledge needed to operate it are the two central goals of knowledge engineering. to achieve these goals, knowledge engineers construct models of the domain and of the task of interest. the various approaches used for modeling, however, have so far failed to define methods and techniques that can be applied across domains and tasks, and to produce models that can be reused in future applications. In this article, we propose that both of these objectives can be achieved by the use of building blocks called mechanisms. We examine the composition of mechanisms and also show how these mechanisms can be manipulated to construct problemsolving methods. We present PROTÉGÉ-II, a knowledge-acquisition shell that uses problem-solving methods to drive the modeling of tasks, the automatic generation of knowledge-acquisition tools, and the control flow of the problem solver. the modeling of tasks, within the context of PROTÉGÉ-II, is illustrated with two examples: one from the game domain and another from the medical-therapy domain. In addition, we introduce the conceptual basis for a library of mechanisms that serves as a repository of reusable knowledge components. © 1993 John Wiley & Sons, Inc.