In our experiments, the performance evaluation metrics included precision, recall, and F1-score. And we used macro-average to compute the average performance.

Our study has demonstrated that RoBERTa outperforms other language models in classifying medical departments from patient complaints. This superiority is attributed primarily to its extensive pre-training and dynamic masking, which collectively enhance its semantic understanding and generalization capabilities. Despite BlueBERT's specialization in medical terminology, its performance is compromised in the context of the more colloquial language prevalent in online consultations, rendering RoBERTa more effective. Furthermore, models like RoBERTa significantly surpass LLMs in classification tasks. LLMs, while adept at generating coherent text, tend to falter in precision-based classification, with hallucination issues further undermining their performance. Addressing these hallucination problems and enhancing the training of models specifically for Mandarin Chinese will be pivotal in our future research.

Additionally, our keyword trend analysis revealed that online medical consultations peaked during the mid to late stages of the pandemic (2021-2022), driven by isolation measures and the promotion of telemedicine. However, the number of consultations witnessed a decline post-pandemic (2023), falling below pre-pandemic levels, likely due to patient skepticism and discomfort with virtual interactions. This trend underscores a significant research opportunity to further enhance and optimize online consultation platforms, aiming to restore patient confidence and improve the overall efficacy of digital healthcare services.