| Literature DB >> 33035705 |
Clint Cuffy1, Nao Hagiwara2, Scott Vrana2, Bridget T McInnes2.
Abstract
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.Entities:
Keywords: Distributional similarity; Natural language processing; Patient–physician communication; Semantic similarity and relatedness
Year: 2020 PMID: 33035705 PMCID: PMC8056946 DOI: 10.1016/j.jbi.2020.103589
Source DB: PubMed Journal: J Biomed Inform ISSN: 1532-0464 Impact factor: 6.317