Self-supervised fusion of clinical expertise and interpersonal skills for enhanced physician recommendation

For online medical consultation platforms, it is essential to recommend physicians with clinical expertise and interpersonal skills to improve patient satisfaction and clinical outcomes. Current physician recommendation is focused primarily on physicians' clinical qualifications and historical interactions, while largely overlooking the significant impact of interpersonal skills on effective patient interactions. To address this gap, we propose a novel framework called Self-supervised Fusion of Clinical Expertise and Interpersonal Skills for Enhanced Physician Recommendation. This framework jointly models physicians' clinical expertise and interpersonal skills through self-supervised learning. Specifically, we have designed a multi-attribute fusion paradigm that integrates three key physician attributes: (1) clinical expertise derived from physicians' profiles and historical dialogues, (2) interpersonal skills, including empathy, clarity, and responsiveness, measured through role-based emotional analysis of patient-physician interactions, and (3) patient evaluations that reflect their subjective experiences. Our self-supervised learning strategy aligns these diverse attributes into a unified representation space, automatically capturing the intrinsic correlations between physicians' expertise, interpersonal patterns, and patient feedback. Furthermore, the transformer-based encoder utilizes enhanced fusion representations and patient representations to assess the suitability of a physician to handle a specific patient consultation. Experiments on a real-world medical dialogue dataset demonstrate that SF-CEisEPR+FL outperforms the MUL-ATT baseline (a representative dialogue-driven method), achieving absolute gains of +6% in Precision@1, +5% in MAP, and +0.4% in ERR@5.