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Our conceptual model demonstrates our goal to investigate the impact of clinical decision support (CDS) utilization on cancer screening improvement strategies in the community health care (CHC) setting. We employed a dual modeling technique using both statistical and computational modeling to evaluate impact. Our statistical model used the Spearman’s Rho test to evaluate the strength of relationship between our proximal outcome measures (CDS utilization) against our distal outcome measure (provider self-reported cancer screening improvement). Our computational model relied on network evolution theory and made use of a tool called Construct-TM to model the use of CDS measured by the rate of organizational learning. We employed the use of previously collected survey data from community health centers Cancer Health Disparities Collaborative (HDCC). Our intent is to demonstrate the added valued gained by using a computational modeling tool in conjunction with a statistical analysis when evaluating the impact a health information technology, in the form of CDS, on health care quality process outcomes such as facility-level screening improvement. Significant simulated disparities in organizational learning over time were observed between community health centers beginning the simulation with high and low clinical decision support capability.

The theory of constructuralism describes how shared knowledge, representative of cultural forms, develops between individuals through social interaction. Constructuralism argues that through interaction and individual learning, the social network (who interacts with whom) and the knowledge network (who knows what) coevolve. In the present work, we extend the theory of constructuralism and implement this extension in an agent-based model (ABM). Our work focuses on the theory's inability to describe how people form and utilize stereotypes of higher order social structures, in particular observable social groups and society as a whole. In our ABM, we formalize this theoretical extension by creating agents that construct, adapt, and utilize social stereotypes of individuals, social groups, and society. We then use this model to carry out a virtual experiment that explores how ethnocentric stereotypes and the underlying distribution of culture in an artificial society interact to produce varying levels of social relationships across social groups. In general, we find that neither stereotypes nor the form of underlying cultural structures alone are sufficient to explain the extent of social relationships across social groups. Rather, we provide evidence that shared culture, social relations, and group stereotypes all intermingle to produce macrosocial structure.

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