Cuffy Clint, Hagiwara Nao, Vrana Scott, McInnes Bridget T
Virginia Commonwealth University, 401 S. Main St., Richmond, VA 23284, USA.
Virginia Commonwealth University, 401 S. Main St., Richmond, VA 23284, USA.
J Biomed Inform. 2020 Dec;112:103589. doi: 10.1016/j.jbi.2020.103589. Epub 2020 Oct 6.
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.
医患沟通是提供医疗服务中一个常常被忽视却非常重要的方面。医患之间在话语交流中积极的沟通质量会对诊疗的各个方面产生影响，比如患者对规定治疗方案的依从性以及他们的医疗护理结果。由于在医患环境中探索语义及其对个性化医疗保健的影响方面几乎没有参考标准，本文提出了一项研究，探索从三个不同数据源捕获、建模和评估医患沟通的三种方法。我们介绍、比较并对比了这些使用给定诊疗中话语内容之间的相关性来捕获和建模医患沟通质量的方法。展示了所有三个数据源的结果以及记录的医生之间的沟通质量得分。我们发现我们的模型能够证明在一次诊疗中捕捉双方之间积极沟通质量的能力。我们还根据突出诊疗各个方面的自我报告问卷评估这些发现，并对在132名患者中进行诊疗的17名医生的沟通质量进行排名。
