主管:国家卫生健康委员会
主办:国家卫生健康委医院管理研究所
中国科技核心期刊(中国科技论文统计源期刊)
中国科学引文数据库(CSCD)核心库期刊
《中文核心期刊要目总览》核心期刊

中国护理管理 ›› 2026, Vol. 26 ›› Issue (3): 429-434.doi: 10.3969/j.issn.1672-1756.2026.03.021

• 数智化护理 • 上一篇    下一篇

DeepSeek大语言模型在护理病历智能化管理中的应用

张宇辰 刘霞 赵林 脱淼 盖玉彪 谷如婷 魏丽丽   

  1. 青岛大学附属医院重症医学科,266000 山东省青岛市(张宇辰,盖玉彪);护理部(刘霞,赵林,脱淼);胸外科(谷如婷);院长办公室(魏丽丽)
  • 出版日期:2026-03-15 发布日期:2026-03-15
  • 通讯作者: 魏丽丽,博士,主任护师,副院长,E-mail:13573828157@163.com
  • 作者简介:张宇辰,硕士,主管护师
  • 基金资助:
    青岛市医疗卫生优秀人才培养项目;青岛大学附属医院“临床医学+X”科研项目(QDFY+X2023205)

Application of DeepSeek Large Language Model in intelligent management of nursing records

ZHANG Yuchen, LIU Xia, ZHAO Lin, TUO Miao, GAI Yubiao, GU Ruting, WEI Lili   

  1. Intensive Care Unit, The Affiliated Hospital of Qingdao University, Qingdao, Shandong province, 266000, China
  • Online:2026-03-15 Published:2026-03-15
  • Contact: E-mail:13573828157@163.com

摘要: 目的:通过应用DeepSeek大语言模型对护理病历实施全流程智能化管理并评价应用效果,以提升护理病历管理质量和效率。方法:2025年2月,青岛市某三级甲等医院护理部联合信息管理部完成DeepSeek的应用,对护理病历实施全流程智能化管理,通过智能交互框辅助完成护理决策、护理病历书写、质量控制等各环节流程,比较模型应用前后的护理病历完成时间、错误率、病历质量、病历系统可用性。结果:在DeepSeek大语言模型辅助下,记录护理病历完成时间由(3.96±1.56)小时/份缩短至(2.47±0.53)小时/份,护理病历错误率由6.33%降低至1.56%,护理病历质量评分及护士对于病历系统可用性的评分提高,差异均有统计学意义(P<0.05)。结论:在DeepSeek大语言模型的支持下对护理病历数据进行深度分析与挖掘,提高了病历质量评估的准确性,促进了护理工作标准化,保障了患者护理过程的质量与安全。

关键词: 深度求索;大语言模型;人工智能;护理病历;质量控制;健康教育

Abstract: Objective: To implement intelligent full-process management of nursing records through the application of DeepSeek Large Language Model and evaluate its application effectiveness, aiming to enhance the quality and efficiency of nursing record management. Methods: In February 2025, the nursing department and information management department in a tertiary grade A hospital in Qingdao completed the application of DeepSeek. Intelligent full-process management of nursing records was implemented, utilizing an intelligent interactive interface to assist in nursing decision-making, documentation, quality control, and other workflow stages. We compared the time required to complete nursing records, error rates, quality and system usability of nursing records before and after the DeepSeek model's deployment. Results: After the deployment of DeepSeek, the time required to complete a nursing record was reduced from (3.96±1.56) hours to (2.47±0.53) hours, and the error rate of nursing records decreased from 6.33% to 1.56%. Meanwhile, nurses' ratings of the medical record quality and system's usability were significantly improved. All the differences were statistically significant (P<0.05). Conclusion: With the support of DeepSeek Large Language Model, in-depth analysis and mining of nursing record data have been conducted, improving the accuracy of record quality assessment, promoting the standardization of nursing practices, and ensuring the quality and safety of patient care.

Key words: DeepSeek; Large Language Model; Artificial Intelligence; nursing record; quality control; health education

中图分类号:  R47;R197