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大语言模型在围手术期医学中的临床和经济影响:一项随机交叉试验

Clinical and economic impact of a large language model in perioperative medicine: a randomized crossover trial.

作者信息

Ke Yu He, Yang Ong Bernard Soon, Jin Liyuan, Sim Jacqueline Xiu Ling, Chan Chi Ho, Soh Chai Rick, Wong Danny Jon Nian, Liu Nan, Sng Ban Leong, Ting Daniel Shu Wei, Yeo Su Qian, Ong Marcus Eng Hock, Abdullah Hairil Rizal

机构信息

Department of Anesthesiology, Singapore General Hospital, Singapore, Singapore.

Data Science and Artificial Intelligence Lab, Singapore General Hospital, Singapore, Singapore.

出版信息

NPJ Digit Med. 2025 Jul 21;8(1):462. doi: 10.1038/s41746-025-01858-x.


DOI:10.1038/s41746-025-01858-x
PMID:40691284
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC12280117/
Abstract

Preoperative assessment is a critical but time-consuming component of perioperative care, often hindered by poor guideline adherence and high documentation burdens. This study evaluates the impact of PEACH (PErioperative AI CHatbot), an LLM-based clinical decision support system, on documentation efficiency, quality, user acceptance, and cost-effectiveness in preoperative consultations. PEACH did not significantly reduce overall documentation time in this randomized crossover trial involving resident physicians at Singapore General Hospital. However, subgroup analyses showed time savings for moderate-complexity patients (5.77 min, p = 0.010) and experienced physicians (4.6 min, p = 0.040). Evaluators preferred PEACH-assisted documentation in 57.1% of cases, with improved inclusion of issue lists (p = 0.05). Economic modeling projected annual institutional savings of SGD197,501 (USD146,297), with sensitivity analyses ranging from SGD 48,979 to 197,499 (USD36,280 to 146,295). These findings suggest that LLM-based tools like PEACH may enhance preoperative documentation efficiency and offer economic value.

摘要

术前评估是围手术期护理的一个关键但耗时的组成部分,常常因指南依从性差和文档负担过重而受到阻碍。本研究评估了基于大语言模型的临床决策支持系统PEACH(围手术期人工智能聊天机器人)对术前会诊中文档效率、质量、用户接受度和成本效益的影响。在这项涉及新加坡总医院住院医师的随机交叉试验中,PEACH并没有显著减少总体文档记录时间。然而,亚组分析显示,对于中等复杂程度的患者(节省5.77分钟,p = 0.010)和经验丰富的医生(节省4.6分钟,p = 0.040),时间有所节省。在57.1%的病例中,评估人员更喜欢PEACH辅助的文档记录,问题清单的完整性有所提高(p = 0.05)。经济模型预测,每年可为机构节省197,501新元(146,297美元),敏感性分析结果在48,979新元至197,499新元之间(36,280美元至146,295美元)。这些发现表明,像PEACH这样基于大语言模型的工具可能会提高术前文档记录效率并提供经济价值。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8417/12280117/5cd1797fecc4/41746_2025_1858_Fig2_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8417/12280117/cbaff9ed782b/41746_2025_1858_Fig1_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8417/12280117/5cd1797fecc4/41746_2025_1858_Fig2_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8417/12280117/cbaff9ed782b/41746_2025_1858_Fig1_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8417/12280117/5cd1797fecc4/41746_2025_1858_Fig2_HTML.jpg

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Clinical and economic impact of a large language model in perioperative medicine: a randomized crossover trial.

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本文引用的文献

[1]
Retrieval augmented generation for 10 large language models and its generalizability in assessing medical fitness.

NPJ Digit Med. 2025-4-5

[2]
Ethical Application of Generative Artificial Intelligence in Medicine.

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[3]
A strategy for cost-effective large language model use at health system-scale.

NPJ Digit Med. 2024-11-18

[4]
Custom Large Language Models Improve Accuracy: Comparing Retrieval Augmented Generation and Artificial Intelligence Agents to Noncustom Models for Evidence-Based Medicine.

Arthroscopy. 2025-3

[5]
The evaluation of the performance of ChatGPT in the management of labor analgesia.

J Clin Anesth. 2024-11

[6]
Evaluating Large Language Model (LLM) Performance on Established Breast Classification Systems.

Diagnostics (Basel). 2024-7-11

[7]
Large Language Model Capabilities in Perioperative Risk Prediction and Prognostication.

JAMA Surg. 2024-8-1

[8]
Evaluating the accuracy of ChatGPT-4 in predicting ASA scores: A prospective multicentric study ChatGPT-4 in ASA score prediction.

J Clin Anesth. 2024-9

[9]
Evaluation of the Performance of Generative AI Large Language Models ChatGPT, Google Bard, and Microsoft Bing Chat in Supporting Evidence-Based Dentistry: Comparative Mixed Methods Study.

J Med Internet Res. 2023-12-28

[10]
Prompt Engineering as an Important Emerging Skill for Medical Professionals: Tutorial.

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