Amid electronic health records and laboratory tests, office-based patient and provider communication is still the heart of primary care. Patients often present multiple complaints and, consequently, may be expressing intense emotions, requiring physicians to decide how to balance competing demands. How physicians navigate conversations has implications for patient satisfaction, payments, and quality of care. However, traditional observational measures of emotions and what is discussed during clinical visits are slow and costly and do not scale for use in clinical practice. We evaluated machine-learning methods for automated annotation of medical topics and emotional valence in patient-provider dialogue transcripts.
