人工智能在手术室护理质量与安全管理中的应用进展

doi:10.3761/j.issn.0254-1769.2026.19.005

摘要/Abstract

摘要：

人工智能具有较强的数据处理、自主学习、预测分析与辅助决策能力，可应用于手术耗材管理、手术运行流程优化、护理风险预警、手术安全核查等环节，有助于提高手术室护理质量与安全管理的及时性、精准性、可追溯性。该文综述了人工智能在手术室护理质量与安全管理中的应用进展，并分析相关挑战、提出应对策略，旨在推动人工智能在手术室护理管理中的规范应用、提升手术室护理质量。

关键词: 人工智能, 手术室, 综述, 护理管理研究

Abstract:

Artificial intelligence has strong capabilities in data processing，self-learning，predictive analysis，and intelligent decision support. It can be applied to surgical consumables management，optimization of operating room workflow，nursing risk warning，and surgical safety checklist support，thereby improving the timeliness，accuracy，and traceability of operating room nursing management. This review summarizes the application progress of artificial intelligence in operating room nursing management，analyzes related challenges，and proposes corresponding strategies，aiming to provide a reference for the standardized application of artificial intelligence in operating room nursing management and the improvement of nursing quality.

Key words: Artificial Intelligence, Operating Room, Review, Nursing Administration Research

赵子涵, 徐朝俊, 李明天, 周怡, 李川, 刘艳玲. 人工智能在手术室护理质量与安全管理中的应用进展[J]. 中华护理杂志, 2026, 61(19): 2626-2631.

ZHAO Zihan, XU Chaojun, LI Mingtian, ZHOU Yi, LI Chuan, LIU Yanling. Advances in the application of artificial intelligence in nursing quality and safety management in operating rooms[J]. Chinese Journal of Nursing, 2026, 61(19): 2626-2631.

参考文献 34

[1]	高兴莲, 郭莉, 何丽, 等. 手术室专业护理质量敏感指标的专家共识[J]. 护理学杂志, 2025, 40(13):45-50.
	Gao XL, Guo L, He L, et al. Expert consensus on sensitive indicators of professional nursing quality in operating room[J]. J Nurs Sci, 2025, 40(13):45-50.
[2]	国家卫生健康委员会. 关于印发《全国护理事业发展规划(2021—2025年)》的通知[EB/OL]. (2022-04-29)[2025-12-01]. https://wjw.hubei.gov.cn/bmdt/ztzl/sswgh/ghwj/202205/t20220512_4125280.shtml.
	National Health Commission. Circular on the issuance of the national nursing career development plan(2021-2025)[EB/OL]. (2022-04-29)[2025-12-01]. https://wjw.hubei.gov.cn/bmdt/ztzl/sswgh/ghwj/202205/t20220512_4125280.shtml.
[3]	McKinsey Company. What is AI(artificial intelligence)[EB/OL]. (2024-04-03)[2025-12-01]. https://www.mckinsey.com/featured-insights/mckinsey-explainers/what-is-ai.
[4]	国务院. 关于印发新一代人工智能发展规划的通知[EB/OL]. (2017-07-08)[2025-12-01]. https://www.gov.cn/zhengce/content/2017-07/20/content_5211996.htm.
	State Council. Circular on the issuance of the development plan for a new generation of artificial intelligence[EB/OL]. (2017-07-08)[2025-12-01]. https://www.gov.cn/zhengce/content/2017-07/20/content_5211996.htm.
[5]	何璐, 汤国娇, 徐旻, 等. 手术室器械全流程可追溯信息化管理实践[J]. 护理学杂志, 2023, 38(24):52-55.
	He L, Tang GJ, Xu M, et al. Information management practice of operating room instruments based on whole process traceability[J]. J Nurs Sci, 2023, 38(24):52-55.
[6]	陈盛帆, 黄建琪, 高正. 手术室高值耗材智能管理系统的开发[J]. 中国医疗器械杂志, 2021, 45(1):42-45.
	Chen SF, Huang JQ, Gao Z. Development of intelligent management system for high value consumable material in operating room[J]. Chin J Med Lnstrumentation, 2021, 45(1):42-45.
[7]	谢文瑶, 蔡益民, 黄金菁, 等. 介入手术室高值耗材智能管理系统的构建与应用[J]. 护理研究, 2022, 36(20):3725-3728.
	Xie WY, Cai YM, Huang JJ, et al. Construction and application of intelligent management system for high value consumables in the interventional operating room[J]. Chin Nurs Res, 2022, 36(20):3725-3728.
[8]	黄雨佳, 李永刚, 兰俊, 等. 智能物资柜的研发和应用[J]. 中华护理杂志, 2021, 56(2):194-198. DOI URL
	Huang YJ, Li YG, Lan J, et al. Research development and application of intelligent supplies cabinet[J]. Chin J Nurs, 2021, 56(2):194-198. DOI URL
[9]	魏建华, 李广义, 程启元, 等. 基于智能货架和物流机器人的手术室智能物流体系建设研究[J]. 中国医学装备, 2022, 19(2):137-141.
	Wei JH, Li GY, Cheng QY, et al. Research on the construction of intelligent logistics system for operating theater based on intelligent shelves and logistics robots[J]. China Med Equip, 2022, 19(2):137-141.
[10]	Strömblad CT, Baxter-King RG, Meisami A, et al. Effect of a predictive model on planned surgical duration accuracy,patient wait time,and use of presurgical resources:a randomized clinical trial[J]. JAMA Surg, 2021, 156(4):315. DOI URL
[11]	Tuwatananurak JP, Zadeh S, Xu XL, et al. Machine learning can improve estimation of surgical case duration:a pilot study[J]. J Med Syst, 2019, 43(3):44. DOI PMID
[12]	Ramamurthi A, Neupane B, Deshpande P, et al. Development and validation of an artificial intelligence system for surgical case length prediction[J]. Surgery, 2025, 179:108942. DOI URL
[13]	Park JB, Roh GH, Kim K, et al. Development of predictive model of surgical case durations using machine learning approach[J]. J Med Syst, 2025, 49(1):8. DOI
[14]	Padoy N. Machine and deep learning for workflow recognition during surgery[J]. Minim Invasive Ther Allied Technol, 2019, 28(2):82-90. DOI PMID
[15]	Loo T, Mcglennen B, Incavo S, et al. Measuring provider-level differences in perioperative workflow using computer vision-based artificial intelligence[J]. BMJ Health Care Inform, 2025, 32(1):e101591.
[16]	Nguyen HP, Khairnar SM, Palacios SG, et al. Kinematic adaptive frame recognition(KAFR):a novel framework for video segmentation via frame similarity and surgical tool tracking[J]. IEEE Access, 2025, 13:101681-101697. DOI URL
[17]	Luo L, Zhang FY, Yao Y, et al. Machine learning for identification of surgeries with high risks of cancellation[J]. Health Inform J, 2020, 26(1):141-155. DOI PMID
[18]	Zhang FY, Cui XY, Gong RR, et al. Key experimental factors of machine learning-based identification of surgery cancellations[J]. J Healthc Eng, 2021, 2021:6247652.
[19]	Hassanzadeh H, Boyle J, Khanna S, et al. Daily surgery caseload prediction:towards improving operating theatre efficiency[J]. BMC Med Inform Decis Mak, 2022, 22(1):151. DOI
[20]	Bui H, Ekşioglu SD, A Villafranca A, et al. A long short-term memory model for forecasting the surgical case volumes at a hospital[J]. IISE Trans Healthc Syst Eng, 2023, 13(3):226-236.
[21]	Zadeh HS, Zhang LL, Fackrell M, et al. Advanced forecasting of emergency surgical case arrivals:enhancing operating room performance[J]. Perioper Care Oper Room Manag, 2025, 38:100451.
[22]	李丽, 颜艳, 房馨, 等. 腹腔镜手术患者术中低体温风险预测模型的构建及验证[J]. 中华护理杂志, 2022, 57(4):463-468. DOI URL
	Li L, Yan Y, Fang X, et al. Establishment and validation of a risk prediction model for intraoperative hypothermia in patients undergoing laparoscopic surgery[J]. Chin J Nurs, 2022, 57(4):463-468. DOI URL
[23]	项海燕, 黄立峰, 钱维明, 等. 基于深度学习的全身麻醉患者围术期非计划低体温预测模型的构建与应用[J]. 中华急诊医学杂志, 2022, 31(8):1116-1120.
	Xiang HY, Huang LF, Qian WM, et al. Construction and application of inadvertent perioperative hypothermia prediction model for patients under general anesthesia based on deep learning[J]. Chin J Emerg Med, 2022, 31(8):1116-1120.
[24]	韩姗姗, 秦永平, 屈虹, 等. 神经外科患儿术中获得性压力性损伤风险预测模型的构建及验证[J]. 中华护理杂志, 2025, 60(8):928-933. DOI URL
	Han SS, Qin YP, Qu H, et al. Construction and validation of a risk prediction model for intraoperative acquired pressure injury in neurosurgical children[J]. Chin J Nurs, 2025, 60(8):928-933. DOI URL
[25]	Whiteside LA, Roy ME. Use of an artificial intelligence device for evaluating blood loss in complex major orthopaedic surgery procedures[J]. J Arthroplasty, 2024, 39(S1):S53-S58. DOI URL
[26]	程福, 陈春霞, 杨冬梅, 等. 基于机器学习算法建立个体化手术用血预测模型[J]. 中国输血杂志, 2021, 34(8):850-854.
	Cheng F, Chen CX, Yang DM, et al. Development of an individualized prediction model of allogenic blood transfusion in elective patients based on machine learning[J]. Chin J Blood Transfus, 2021, 34(8):850-854.
[27]	殷德涛, 刘益豪, 丁欣, 等. 《手术安全核查表》在手术安全应用中的实践探讨[J]. 医学与哲学, 2021, 42(15):5-8,24.
	Yin DT, Liu YH, Ding X, et al. Practical exploration of “surgery safety checklists” in the application of surgical safety[J]. Med Philos, 2021, 42(15):5-8,24.
[28]	边梅, 师文, 郭小兰, 等. 基于语音声纹识别的手术安全核查软件研发与应用[J]. 中国数字医学, 2024, 19(4):45-53.
	Bian M, Shi W, Guo XL, et al. Development and application of surgical safety verification software based on speech and voiceprint recognition[J]. China Digit Med, 2024, 19(4):45-53.
[29]	Al Abbas AI, Sankaranarayanan G, Polanco PM, et al. The operating room black box:understanding adherence to surgical checklists[J]. Ann Surg, 2022, 276(6):995-1001. DOI URL
[30]	Deol ES, Henning G, Basourakos S, et al. Artificial intelligence model for automated surgical instrument detection and counting:an experimental proof-of-concept study[J]. Patient Saf Surg, 2024, 18(1):24. DOI
[31]	Ali Haider S, Ho OA, Borna S, et al. Use of multimodal artificial intelligence in surgical instrument recognition[J]. Bioengineering, 2025, 12(1):72. DOI URL
[32]	Jiang LS, Wu Z, Xu XL, et al. Opportunities and challenges of artificial intelligence in the medical field:current application,emerging problems,and problem-solving strategies[J]. J Int Med Res, 2021, 49(3):03000605211000157.
[33]	许来雨, 彭伶丽, 黄伟红, 等. 皮瓣移植术后血管危象识别与防控管理信息系统的开发及应用研究[J]. 中华护理杂志, 2022, 57(11):1291-1296. DOI URL
	Xu LY, Peng LL, Huang WH, et al. Development and application of management information system for recognition and prevention of flap vascular crisis[J]. Chin J Nurs, 2022, 57(11):1291-1296. DOI URL
[34]	宋晓安, 卢兴泉, 马静, 等. 新入职护士考核评估管理信息系统的开发与应用[J]. 中华护理杂志, 2024, 59(8):974-980. DOI URL
	Song XA, Lu XQ, Ma J, et al. Study on the development and application of an evaluation management information system for new nurses[J]. Chin J Nurs, 2024, 59(8):974-980. DOI URL