Conceptual models are the core of robust classification schemes (e.g., SNOMED-RT, GALEN) and emerging standards for the exchange of health care information (e.g., HL-7). Conceptual models are defined here as a conceptualization, simplification, or abstraction of reality. Community surveillance activities involve representing information for sharing, and therefore they rely heavily on conceptual models. Conceptual models, either implicit or explicit, act to guide processes of information exchange and as the context for assimilating heterogeneous data. An explicit conceptual model is what guides aggregation of "units of information" and will be an essential component of successful surveillance systems. The Centers for Disease Control and Prevention has labeled conceptual data modeling as "one of the most powerful and effective analytical techniques ever developed for understanding and organizing information required to support any enterprise." There is little formal discussion about what constitutes strong conceptual models or a formal methodology for how they are constructed. This project examined qualitative data from two sources to develop a generalized methodology for constructing conceptual models. Narratives from the literature in various domains are explored, and these data were triangulated with content analysis from a case study. In this case study, domain experts reviewed and commented on a conceptual model designed to represent pediatric asthma knowledge. This triangulation of literature and expert reviews identified a generalized methodology for the construction and evaluation of conceptual models. In addition to the discussion of conceptual model development, this poster presents potential applications of conceptual models for data aggregation and manipulation critical to syndromic surveillance. Zachary Pincus, David L. Buckeridge, Michael K. Choy, Justin V. Graham, Martin J. O'Connor, and Mark Musen The use of "nontraditional" health data, such as school absenteeism and pharmacy sales, is thought to increase the sensitivity and timeliness of warnings from syndromic surveillance systems. A common description scheme for nontraditional data and data sources will greatly increase the utility of such data. Standards such as those proposed in the National Electronic Disease Surveillance System and the Health Level 7 Reference Information Model enable interchange of traditional epidemiological and clinical data. Unfortunately, such standards were not designed to accommodate the needs of nontraditional data and their sources. To meet these needs, we have developed a flexible, extensible ontology (metadata model). Most data standards attempt to preenumerate fields and formats for every needed piece of information. However, it is not yet established which types of nontraditional data and metadata are necessary or useful. In addition, heterogeneity in the format and structure of nontraditional data makes it difficult to construct a single, comprehensive "standard." Instead, our ontology allows users to construct detailed, customized, machine-readable descriptions of data sources, data formats, and relationships between data sources by building them up from simple terms and attributes provided by the ontology. New classes of data or data sources, and individual instances of these classes, can be entered through an open source knowledge acquisition application. In an initial evaluation, we used the ontology to produce a model of output data from a simulated outbreak. We are continuing to validate the ontology as a
The problem of heterogeneous data can be solved with the use of conceptual models, as some works have proposed REF .