Patient-physician communication is an often overlooked yet a very important aspect of providing medical care. Positive patient-physician quality of communication within discourse has an influence on various aspects of a consultation such as a patient's treatment adherence to prescribed medical regimen and their medical care outcome. As few reference standards exist for exploring semantics within the patient-physician setting and its effects on personalized healthcare, this paper presents a study exploring three methods to capture, model and evaluate patient-physician communication among three distinct data-sources. We introduce, compare and contrast these methods for capturing and modeling patient-physician communication quality using relatedness between discourse content within a given consultation. Results are shown for all three data-sources and communication quality scores among physicians recorded. We found our models demonstrate the ability to capture positive communication quality between both participants within a consultation. We also evaluate these findings against self-reported questionnaires highlighting various aspects of the consultation and rank communication quality among seventeen physicians who consulted amid one-hundred and thirty-two patients.
Keywords: Distributional similarity; Natural language processing; Patient–physician communication; Semantic similarity and relatedness.
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