Mapping Clinical Intent in Internet Hospitals: Identifying and Profiling Patient Demand via Natural Language...

Patient-generated text in internet hospitals represents a massive but unstructured source of clinical demand. Misalignment between rigid administrative menus and actual patient intent leads to triage inefficiency and resource mismatches. We developed a hybrid artificial intelligence framework to decode patient needs and optimize service allocation using real-world data from a tertiary internet hospital, with external validation on an independent multihospital dataset (N = 497). In this study, we analyzed 28,851 inquiries from a tertiary internet hospital using a 2-stage approach: (a) unsupervised K-Means clustering to discover intrinsic intent taxonomies, and (b) supervised classification using a fine-tuned MacBERT model. We benchmarked this specialized small model against traditional baselines and state-of-the-art large language models (LLMs; Qwen2.5 and HuatuoGPT-II) on a gold-standard dataset of 4,814 annotated records. Unsupervised analysis identified 8 distinct patient intents, revealing substantial semantic overlap between rehabilitation and follow-up requests. The fine-tuned MacBERT model achieved a weighted F1-score of 0.680, outperforming both the medical-specific LLM (F1 = 0.595) and the general LLM (F1 = 0.585). External validation on an independent nationwide platform dataset (N = 497) confirmed cross-platform transferability, with the hybrid learning framework achieving a weighted F1 of 0.578 versus 0.389 (Qwen2.5-7B) and 0.190 (HuatuoGPT-II). Departmental analysis exposed distinct "demand fingerprints": 39% of Neurology inquiries focused on prescription renewals, whereas 68% of Dermatology inquiries involved initial symptom consultations. These findings indicate that, within the studied Chinese hospital setting, fine-tuned task-specific models may offer superior utility over general LLMs for clinical triage, with the performance gap widening under domain shift-though multicenter prospective validation is required to establish generalizability. Mapping intent fingerprints across specialties provides a quantifiable basis for restructuring digital hospital operations.