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RDF-Based Method to Uncover Implicit Health Communication Episodes from Unstructured Health Data

Presenter:

Pericles Giannaris

Date:

02-06-2017

Time:

11:00AM-12:00PM

Location:

2206A Student Center

Health communication is the process that coordinates health services such as specimen transaction, oral interactions, medical records, and more. Healthcare workflows are based on communication established historically through the practice of healthcare or by the leadership in health institutions. However, during healthcare practices communication doesn’t flow according to plan; interpersonal miscommunication, technical glitches, information overload, etc. risk inefficient healthcare services. We hypothesize that health records contain information related to communication and we can retrieve it in order to address issues of communication. We present an informatics pipeline to retrieve health communication episodes from unstructured health data. The method uses Resource Description Framework (RDF), ontological modeling, and description logic inference to uncover and quantify implicit communication episodes. Retrieved communication has the potential to optimize and improve health communication structures especially, in data-intensive to precision medicine settings.