This paper presents a multimodal emotion analysis framework designed to enhance patient-centric healthcare communication and support clinical decision-making. Our system addresses automated patient emotion monitoring during consultations, telemedicine sessions, and mental health screenings by combining audio transcription, facial emotion analysis, and text processing. Using emotion patterns from the CREMA-D dataset as a foundation for healthcare-relevant emotional expressions, we introduce a novel emotion-annotated text format “[emotion] transcript [emotion]” integrating Whisper-based audio transcription with DeepFace facial emotion analysis. We systematically evaluate eight transformer architectures (BERT, RoBERTa, DeBERTa, XLNet, ALBERT, DistilBERT, ELECTRA, and BERT-base) for three-class clinical emotion classification: Distress/Negative (anxiety, fear), Stable/Neutral (baseline), and Engaged/Positive (comfort). Our multimodal fusion strategy achieves 86.8% accuracy with DeBERTa-v3-base, representing a 12.6% improvement over unimodal approaches and meeting clinical requirements for reliable patient emotion detection. Cross-modal attention analysis reveals facial expressions provide crucial disambiguation, with stronger attention to negative emotions (0.41 vs 0.28), aligning with clinical priorities for detecting patient distress. Our contributions include emotion-annotated text representation for healthcare contexts, systematic transformer evaluation for clinical deployment, and a framework enabling real-time patient emotion monitoring and emotionally-aware clinical decision support.