Biomedical subjects

Samson W Tu

Publications and source records attributed to Samson W Tu.

13 recordsLinked to original sources

PubMed2005

Semantic clinical guideline documents.

Decision-support systems based on clinical practice guidelines can support physicians and other healthcare personnel in the process of following best practice consistently. A knowledge-based approach to represent guidelines makes it possible to encode computer-interpretable guidelines in a formal manner,perform consistency checks, and use the guidelines directly in decision-support systems.Decision-support authors and guideline users require guidelines in human-readable formats in addition to computer-interpretable ones (e.g., for guideline review and quality assurance). We propose a new document-oriented information architecture that combines knowledge-representation models with electronic and paper documents. The approach integrates decision-support modes with standard document formats to create a combined clinical-guideline model that supports on-line viewing, printing, and decision support.

Decision Making, Computer-Assisted↗

PubMed2004

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.

Academic Medical Centers↗

PubMed2004

Design and implementation of the GLIF3 guideline execution engine.

We have developed the GLIF3 Guideline Execution Engine (GLEE) as a tool for executing guidelines encoded in the GLIF3 format. In addition to serving as an interface to the GLIF3 guideline representation model to support the specified functions, GLEE provides defined interfaces to electronic medical records (EMRs) and other clinical applications to facilitate its integration with the clinical information system at a local institution. The execution model of GLEE takes the "system suggests, user controls" approach. A tracing system is used to record an individual patient's state when a guideline is applied to that patient. GLEE can also support an event-driven execution model once it is linked to the clinical event monitor in a local environment. Evaluation has shown that GLEE can be used effectively for proper execution of guidelines encoded in the GLIF3 format. When using it to execute each guideline in the evaluation, GLEE's performance duplicated that of the reference systems implementing the same guideline but taking different approaches. The execution flexibility and generality provided by GLEE, and its integration with a local environment, need to be further evaluated in clinical settings. Integration of GLEE with a specific event-monitoring and order-entry environment is the next step of our work to demonstrate its use for clinical decision support. Potential uses of GLEE also include quality assurance, guideline development, and medical education.

Database Management Systems↗

PubMed2004

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.

Decision Support Systems, Clinical↗

PubMed2004

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.

Decision Making, Computer-Assisted↗

PubMed2004

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.

Decision Support Systems, Clinical↗

PubMed2003

The structure of guideline recommendations: a synthesis.

We propose that recommendations in a clinical guideline can be structured either as collections of decisions that are to be applied in specific situations or as processes that specify activities that take place over time. We formalize them as "recommendation sets" consisting of either Activity Graphs that represent guideline-directed processes or Decision Maps that represent atemporal recommendations or recommendations involving decisions made at one time point. We model guideline processes as specializations of workflow processes and provide possible computational models for decision maps. We evaluate the proposed formalism by showing how various guideline-modeling methodologies, including GLIF, EON, PRODIGY3, and Medical Logic Modules can be mapped into the proposed structures. The generality of the formalism makes it a candidate for standardizing the structure of recommendations for computer-interpretable guidelines.

Decision Making, Computer-Assisted↗

PubMed2003

GESDOR - a generic execution model for sharing of computer-interpretable clinical practice guidelines.

We developed the Guideline Execution by Semantic Decomposition of Representation (GESDOR) model to share guidelines encoded in different formats at the execution level. For this purpose, we extracted a set of generalized guideline execution tasks from the existing guideline representation models. We then created the mappings between specific guideline representation models and the set of the common guideline execution tasks. Finally, we developed a generic task-scheduling model to harmonize the existing approaches to guideline task scheduling. The evaluation has shown that the GESDOR model can be used for the effective execution of guidelines encoded in different formats, and thus realizes guideline sharing at the execution level.

Decision Making, Computer-Assisted↗

PubMed2003

Protégé-2000: an open-source ontology-development and knowledge-acquisition environment.

Protégé-2000 is an open-source tool that assists users in the construction of large electronic knowledge bases. It has an intuitive user interface that enables developers to create and edit domain ontologies. Numerous plugins provide alternative visualization mechanisms, enable management of multiple ontologies, allow the use of interference engines and problem solvers with Protégé ontologies, and provide other functionality. The Protégé user community has more than 7000 members.

Database Management Systems↗

PubMed2003

A knowledge-acquisition wizard to encode guidelines.

An important step in building guideline-based clinical care systems is encoding guidelines. Protégé-2000, developed in our laboratory, is a general-purpose knowledge-acquisition tool that facilitates domain experts and developers to record, browse and maintain domain knowledge in knowledge bases. In this poster we illustrate a knowledge-acquisition wizard that we built around Protégé-2000. The wizard provides an environment that is more intuitive to domain specialists to enter knowledge, and domain specialists and practitioners to review the knowledge entered.

Artificial Intelligence↗

PubMed2002

Representation primitives, process models and patient data in computer-interpretable clinical practice guidelines: a literature review of guideline representation models.

Representation of clinical practice guidelines in a computer-interpretable format is a critical issue for guideline development, implementation, and evaluation. We studied 11 types of guideline representation models that can be used to encode guidelines in computer-interpretable formats. We have consistently found in all reviewed models that primitives for representation of actions and decisions are necessary components of a guideline representation model. Patient states and execution states are important concepts that closely relate to each other. Scheduling constraints on representation primitives can be modeled as sequences, concurrences, alternatives, and loops in a guideline's application process. Nesting of guidelines provides multiple views to a guideline with different granularities. Integration of guidelines with electronic medical records can be facilitated by the introduction of a formal model for patient data. Data collection, decision, patient state, and intervention constitute four basic types of primitives in a guideline's logic flow. Decisions clarify our understanding on a patient's clinical state, while interventions lead to the change from one patient state to another.

Artificial Intelligence↗

PubMed2002

Knowledge-based bioterrorism surveillance.

An epidemic resulting from an act of bioterrorism could be catastrophic. However, if an epidemic can be detected and characterized early on, prompt public health intervention may mitigate its impact. Current surveillance approaches do not perform well in terms of rapid epidemic detection or epidemic monitoring. One reason for this shortcoming is their failure to bring existing knowledge and data to bear on the problem in a coherent manner. Knowledge-based methods can integrate surveillance data and knowledge, and allow for careful evaluation of problem-solving methods. This paper presents an argument for knowledge-based surveillance, describes a prototype of BioSTORM, a system for real-time epidemic surveillance, and shows an initial evaluation of this system applied to a simulated epidemic from a bioterrorism attack.

Artificial Intelligence↗

PubMed2002

The Chronus II temporal database mediator.

Clinical databases typically contain a significant amount of temporal information. This information is often crucial in medical decision-support systems. Although temporal queries are common in clinical systems, the medical informatics field has no standard means for representing or querying temporal data. Over the past decade, the temporal database community has made a significant amount of progress in temporal systems. Much of this research can be applied to clinical database systems. This paper outlines a temporal database mediator called Chronus II. Chronus II extends the standard relational model and the SQL query language to support temporal queries. It provides an expressive general-purpose temporal query language that is tuned to the querying requirements of clinical decision support systems. This paper describes how we have used Chronus II to tackle a variety of clinical problems in decision support systems developed by our group.

Database Management Systems↗