Entropy-Based Analysis of Emotional Dynamics in Doctor–Patient Co-Decision in Primary Care

Decision-making in healthcare involves both clinical evidence and emotional and cognitive processes that are often difficult to formalize. While sentiment analysis has been widely used to capture emotional information in clinical interactions, it does not fully account for how emotional categories are distributed across interactions. This study proposes an entropy-based approach to complement sentiment analysis by quantifying the diversity and distribution of emotional states expressed during doctor–patient consultations. A dataset of 30 primary care consultations involving patients with hypertension (HTA) and dyslipidemia (DLM) was analyzed using natural language processing techniques. Emotional information was extracted using the NRC Emotion Lexicon, and normalized Shannon entropy was computed at the intervention level. Intervention-based, role-based, and phase-based analyses were conducted. The results show that emotional entropy varies throughout the consultation and across consultation phases. Consultation-level analyses showed higher overall entropy in HTA than in DLM consultations, while no significant overall effect of the speaker role was found. Entropy also varied significantly in consultation phases, although the disease-by-phase interaction was not significant. Robustness analyses supported the stability of these patterns, and the negligible association with sentiment valence indicates complementary information. These findings highlight entropy as a complementary measure for analyzing emotional dynamics in clinical interactions.