Combining infobuttons and semantic web rules for identifying patterns and delivering highly-personalized education materials

Infobuttons have been established to be an effective resource for addressing information needs at the point of care, as evidenced by recent research and their inclusion in government-based electronic health record incentive programs in the United States. Yet their utility has been limited to wide success for only a specific set of domains (lab data, medication orders, and problem lists) and only for discrete, singular concepts that are already documented in the electronic medical record. In this manuscript, we present an effort to broaden their utility by connecting a semantic web-based phenotyping engine with an infobutton framework in order to identify and address broader issues in patient data, derived from multiple data sources. We have tested these patterns by defining and testing semantic definitions of pre-diabetes and metabolic syndrome. We intend to carry forward relevant information to the infobutton framework to present timely, relevant education resources to patients and providers.