Harnessing the Potential of LLMs in Hospital Management: Insights Into Medical Data Inquiry

Background Considering the rapid expansion of hospital operations and the increasing digitisation of medical data, there is a pressing need for efficient and intelligent methods to process and analyse large‐scale medical data. Methods We integrated the QLoRA algorithm with ChatGLM2‐6b, Llama2‐7b, and Llama2‐13b models, fine‐tuning them on a local SQL dataset to optimise query performance. Prompt‐Engineering with ChatGPT was further applied for effective SQL execution. Results Original open‐source models showed almost no SQL generation capability (overall EX ≈ 0 for ChatGLM2‐6B and Llama2‐7B; 0.04 for Llama2‐13B). QLoRA fine‐tuning substantially improved performance, with QLoRA‐Llama2‐13B achieving the best results among open‐source models (overall EX 0.41 ± 0.030). Proprietary models demonstrated significantly stronger performance. Zero‐shot ChatGPT‐3.5 achieved moderate accuracy (EX 0.44 ± 0.007), which improved to 0.94 ± 0.017 with few‐shot prompting. GPT‐4.1 further improved performance, reaching 0.78 ± 0.038 in the zero‐shot setting and 0.96 ± 0.011 with few‐shot prompting. The few‐shot GPT‐4.1 results were comparable to those of database engineers (EX 0.97 ± 0.017), with no significant difference ( p = 0.42). Conclusion Fine‐tuned LLMs and few‐shot GPT‐4.1 demonstrate substantial improvements in SQL query execution, providing a robust framework for efficient medical data analysis and informed hospital decision‐making.