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Laypersons (''consumers'') often have difficulty finding, understanding, and acting on health in- formation due to gaps in their domain knowledge. Ideally, consumer health vocabularies (CHVs) would reflect the different ways consumers express and think about health topics, helping to bridge this vocabulary gap. However, despite the recent research on mismatches between consumer and professional language (e.g., lexical, semantic, and explanatory), there have been few systematic efforts to develop and evaluate CHVs. This paper presents the point of view that CHV development is practical and necessary for extending research on informatics-based tools to facilitate consumer health information seeking, retrieval, and understanding. In support of the view, we briefly describe a dis- tributed, bottom-up approach for (1) exploring the relationship between common consumer health expressions and professional concepts and (2) developing an open-access, preliminary (draft) ''first-generation'' CHV. While recognizing the limitations of the approach (e.g., not addressing psychosocial and cultural factors), we suggest that such explora- tory research and development will yield insights into the nature of consumer health expressions and assist developers in creating tools and applications to support consumer health information seeking. j J Am Med Inform Assoc. 2006;13:24-29. DOI 10.1197/jamia.M1761.

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.

Objective: Clinical information systems typically present patient data in chronologic order, organized by the source of the information (e.g., laboratory, radiology). This study evaluates the functionality and utility of a knowledge-based system that generates concept- oriented views (organized around clinical concepts such as disease or organ system) of clinical data. Design: The authors have developed a system that uses a knowledge base of interrelationships between medical concepts to infer relationships between data in electronic medical records. They use these inferences to produce summaries, or views, of the data that are relevant to a specific concept of interest. They evaluated the ability of the system to select relevant information, reduce information overload, and support physician information retrieval. Measurements: The sensitivity and specificity of the system for identifying relevant patient information were calculated. Effect on information overload was assessed by comparing the amount of information in each view with the amount of information in the entire record. Information retrieval accuracy and cost (time) were used to measure the effect of using concept- oriented views on the efficiency and effectiveness of retrievals. Results: The sensitivity and specificity of the system for identifying relevant clinical information were generally in the range of 70 to 80 percent. Concept-oriented views are effective in reducing the amount of information retrieved (over 80 percent reduction) and, compared with source- oriented views, are able to improve physician retrieval accuracy (p = 0.04). Conclusion: Computer-generated, concept-oriented views can be used to reduce clinician information overload and improve the accuracy of clinical data retrieval.