{
  "abstract": "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.\n\nMethods: 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.\n\nResults: 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).\n\nConclusion: 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.",
  "abstract_shown": true,
  "affiliations": [
    "Department of Clinical Laboratory Medicine Jinniu Maternity and Child Health Hospital of Chengdu Chengdu China",
    "School of Medicine University of Electronic Science and Technology of China Chengdu China",
    "Information Center West China Hospital Sichuan University Chengdu China",
    "State Key Laboratory of Oral Diseases & National Center for Stomatology & National Clinical Research Center for Oral Diseases & Department of Information Management West China Hospital of Stomatology Sichuan University Chengdu Sichuan China"
  ],
  "article_type": "research article",
  "arxiv": null,
  "author_total": 5,
  "authors": [
    {
      "affs": [
        0,
        1
      ],
      "name": "Jiaxi Li"
    },
    {
      "affs": [
        2
      ],
      "name": "Yu Zhang"
    },
    {
      "affs": [
        0
      ],
      "name": "Jing Zhao"
    },
    {
      "affs": [
        0
      ],
      "name": "Shengyang He"
    },
    {
      "affs": [
        3
      ],
      "name": "Danni Li"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/7k6tq4c5xgtwa.bib",
  "cited_by": 1,
  "doi": "10.1002/hpm.70106",
  "fulltext_url": "https://pmc.ncbi.nlm.nih.gov/articles/PMC13730248/",
  "id": "7k6tq4c5xgtwa",
  "issns": [
    "0749-6753",
    "1099-1751"
  ],
  "landing_url": "https://doi.org/10.1002/hpm.70106",
  "language": "en",
  "licence": "CC-BY-NC-4.0",
  "licence_label": "CC BY-NC 4.0",
  "licence_source": "article",
  "licence_url": "https://creativecommons.org/licenses/by-nc/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "heading",
      "Research Article"
    ],
    [
      "openalex",
      "2718|Artificial Intelligence in Healthcare and Education"
    ]
  ],
  "openalex": "W7172003622",
  "pdf_url": null,
  "pmcid": "PMC13730248",
  "pmid": "42536800",
  "published": "2026-07-31",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/7k6tq4c5xgtwa.ris",
  "sources": [
    {
      "first_seen": "2026-11-07T12:22:50Z",
      "native_id": "PMC13730248",
      "source": "pmc",
      "url": "https://doi.org/10.1002/hpm.70106"
    }
  ],
  "subjects": [
    {
      "field": "medicine",
      "origin": "openalex:2718|Artificial Intelligence in Healthcare and Education",
      "subfield": "epidemiology-public-health"
    }
  ],
  "title": "Harnessing the Potential of LLMs in Hospital Management: Insights Into Medical Data Inquiry",
  "title_text": "Harnessing the Potential of LLMs in Hospital Management: Insights Into Medical Data Inquiry",
  "type": "peer-reviewed",
  "updated_at": "2026-11-07T12:22:50Z",
  "url": "https://research.draconitic.com/paper/7k6tq4c5xgtwa",
  "venue": "The International Journal of Health Planning and Management",
  "versions": []
}