No abstract

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.

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.

The Guideline Interchange Format (GLIF) is a model for representation of sharable computer-interpretable guidelines. The current version of GLIF (GLIF3) is a substantial update and enhancement of the model since the previous version (GLIF2). GLIF3 enables encoding of a guideline at three levels: a conceptual flowchart, a computable specification that can be verified for logical consistency and completeness, and an implementable specification that is intended to be incorporated into particular institutional information systems. The representation has been tested on a wide variety of guidelines that are typical of the range of guidelines in clinical use. It builds upon GLIF2 by adding several constructs that enable interpretation of encoded guidelines in computer-based decision-support systems. GLIF3 leverages standards being developed in Health Level 7 in order to allow integration of guidelines with clinical information systems. The GLIF3 specification consists of an extensible object-oriented model and a structured syntax based on the resource description framework (RDF). Empirical validation of the ability to generate appropriate recommendations using GLIF3 has been tested by executing encoded guidelines against actual patient data. GLIF3 is accordingly ready for broader experimentation and prototype use by organizations that wish to evaluate its ability to capture the logic of clinical guidelines, to implement them in clinical systems, and thereby to provide integrated decision support to assist clinicians.

Standards-based, computable knowledge representations for eligibility criteria are increasingly needed to provide computer-based decision support for automated research participant screening, clinical evidence application, and clinical research knowledge management. We surveyed the literature and identified five aspects of eligibility criteria knowledge representations that contribute to the various research and clinical applications: the intended use of computable eligibility criteria, the classification of eligibility criteria, the expression language for representing eligibility rules, the encoding of eligibility concepts, and the modeling of patient data. We consider three of them (expression language, codification of eligibility concepts, and patient data modeling), to be essential constructs of a formal knowledge representation for eligibility criteria. The requirements for each of the three knowledge constructs vary for different use cases, which therefore should inform the development and choice of the constructs toward cost-effective knowledge representation efforts. We discuss the implications of our findings for standardization efforts toward sharable knowledge representation of eligibility criteria.

Ontologies are becoming so large in their coverage that no single person or a small group of people can develop them effectively and ontology development becomes a community-based enterprise. In this paper, we discuss requirements for supporting collaborative ontology development and present Collaborative Protégé-a tool that supports many of these requirements, such as discussions integrated with ontology-editing process, chats, and annotations of changes and ontology components. We have evaluated Collaborative Protégé in the context of ontology development in an ongoing large-scale biomedical project that actively uses ontologies at the VA Palo Alto Healthcare System. Users have found the new tool effective as an environment for carrying out discussions and for recording references for the information sources and design rationale.

Usability testing optimized the CDSS to better address barriers such as lack of provider education, confusion in dosing calculations and titration schedules, access to relevant patient information, provider discontinuity, documentation, and access to validated assessment tools. It also highlighted barriers to good clinical practice that are difficult to address with CDSS technology in its current conceptualization. For example, clinicians indicated that constraints on time and competing priorities in primary care, discomfort in patient-provider communications, and lack of evidence to guide opioid prescribing decisions impeded their ability to provide effective, guideline-adherent pain management. Iterative testing was essential for designing a highly usable and acceptable CDSS; however, identified barriers may limit the impact of the ATHENA-Opioid Therapy system and other CDSS on clinical practices and outcomes unless CDSS are paired with parallel initiatives to address these issues.

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.