Conceptual models: Definitions, construction, and applications in public health surveillance

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.