数字孪生技术在急危重症护理领域应用研究的范围综述

doi:10.3761/j.issn.2096-7446.2025.12.020

摘要/Abstract

摘要：

目的对数字孪生技术在急危重症护理领域中的应用研究进行范围综述，为促进急危重症护理的数字化建设提供参考。方法遵循范围综述的方法学，系统检索PubMed、Web of Science、Cochrane Library、CINAHL、Embase、中国生物医学文献数据库、中国知网、万方数据库及维普中文期刊数据库等数据库中数字孪生技术在急危重症护理领域应用研究的相关文献，并对纳入文献进行总结和分析。检索时限为建库至2025年5月。结果共纳入18篇文献，数字孪生技术在急危重症护理领域中的应用涉及病情监测及预警、医疗决策支持、智能医疗资源管理、医学教育、远程医疗。评价指标涉及可行性评价指标、临床结局指标、工作质量指标。结论数字孪生技术可提升急危重症护理的精准性、及时性和协同性，提升护理质量和效率。但目前研究仍处于起步阶段，未来研究应扩展数字孪生技术在临床中的应用场景，建立科学的评价指标，整合多领域前沿技术，促进数字孪生技术在急危重症护理领域的发展。

关键词: 数字孪生技术, 急危重症护理, 智能护理, 人工智能, 范围综述

Abstract:

Objective To conduct a comprehensive review of the application research of digital twin technology in the field of critical care nursing，providing a reference for promoting the digital construction of critical care nursing. Methods Following the methodology of scoping review，a systematic search was conducted in databases such as PubMed，Web of Science，Cochrane Library，CINAHL，Embase，China Biomedical Literature Database，CNKI，Wanfang Database，and VIP Chinese Journal Database for relevant literature on the application of digital twin technology in acute and critical care nursing，the included literature was summarized and analyzed. The search period was from the inception of the database to May 2025. Results 18 articles were included. The application of digital twins in acute and critical care nursing involved disease monitoring and early warning，medical decision support，intelligent medical resource management，medical education，and telemedicine. Evaluation indicators included feasibility evaluation indicators，clinical outcome indicators，and work quality indicators. Conclusion Digital twins can enhance the accuracy，timeliness，and coordination of acute and critical care nursing，improving the quality and efficiency of nursing. Future research should expand the application scenarios of digital twins in clinical settings，establish scientific evaluation indicators，integrate cutting-edge technologies from multiple fields，and promote the development of digital twins in acute and critical care nursing.

Key words: Digital Twin, Acute and Critical Care, Intelligent Nursing, Artificial Intelligence, Scoping Review

仝紫薇, 王红, 孙旭, 周智聪, 刘晓熠, 王意茹. 数字孪生技术在急危重症护理领域应用研究的范围综述[J]. 中华急危重症护理杂志, 2025, 6(12): 1523-1528.

TONG Ziwei, WANG Hong, SUN Xu, ZHOU Zhicong, LIU Xiaoyi, WANG Yiru. Application of digital twin technology in the field of acute and critical care：a scoping review[J]. Chinese Journal of Emergency and Critical Care Nursing, 2025, 6(12): 1523-1528.

图/表 2

参考文献 29

[1]	Crawford AM, Shiferaw AA, Ntambwe P, et al. Global critical care:a call to action[J]. Crit Care, 2023, 27(1):28. doi: 10.1186/s13054-022-04296-3
[2]	郭联山, 李政钊. 人工智能在急危重症患者诊治中应用研究进展[J]. 中国急救医学, 2023, 43(10):837-840.
	Guo LS, Li ZZ. Research progress of artificial intelligence in the diagnosis and treatment of critically ill patients[J]. Chin J Crit Care Med, 2023, 43(10):837-840.
[3]	徐天宇, 余松轩, 侯冷晨, 等. 数智融合背景下重症医学科发展的机遇、挑战与对策[J]. 海军军医大学学报, 2025, 46(1):118-122.
	Xu TY, Yu SX, Hou LC, et al. Critical care medicine under the background of digital intelligence integration:opportunities,challenges,and strategies[J]. Acad J Nav Med Univ, 2025, 46(1):118-122.
[4]	陈玉倩, 侯晓慧, 朱碧帆, 等. 数字孪生在精准医疗应用中的研究进展和挑战[J]. 海军军医大学学报, 2023, 44(1):97-101.
	Chen YQ, Hou XH, Zhu BF, et al. Digital twin in precision medicine application:research progress and challenges[J]. Acad J Nav Med Univ, 2023, 44(1):97-101.
[5]	Halpern GA, Nemet M, Gowda DM, et al. Advances and utility of digital twins in critical care and acute care medicine:a narrative review[J]. J Yeungnam Med Sci, 2025,42:9.
[6]	Sterne JAC, Savović J, Page MJ, et al. RoB 2:a revised tool for assessing risk of bias in randomised trials[J]. BMJ, 2019,366:l4898.
[7]	Sterne JA, Hernán MA, Reeves BC, et al. ROBINS-I:a tool for assessing risk of bias in non-randomised studies of interven-tions[J]. BMJ, 2016,355:i4919.
[8]	曾宪涛, 刘慧, 陈曦, 等. Meta分析系列之四:观察性研究的质量评价工具[J]. 中国循证心血管医学杂志, 2012, 4(4):297-299.
	Zeng XT, Liu H, Chen X, et al. Meta-analysis series IV:quality evaluation tools for observational research[J]. Chin J Evid Based Cardiovasc Med, 2012, 4(4):297-299.
[9]	Patel BV, Mumby S, Johnson N, et al. A randomized control trial evaluating the advice of a physiological-model/digital twin-based decision support system on mechanical ventilation in patients with acute respiratory distress syndrome[J]. Front Med, 2024,11:1473629.
[10]	An G, Cockrell C. A design specification for Critical Ill-ness Digital Twins(CIDTs) to cure sepsis:responding to the Natio-nal Academies of Sciences,Engineering and Medicine Report “Foundational Research Gaps and Future Directions for Digi-tal Twins”[J]. ArXiv,2024:arXiv:2405.05301v2.
[11]	姚尧, 袁骏毅, 侯旭敏. 数字孪生驱动的心胸外科重症监护室智能管理系统设计与探索[J]. 中国医疗设备, 2024, 39(10):98-103.
	Yao Y, Yuan JY, Hou XM. Design and exploration of digital twin-driven intelligent management system for cardiothoracic intensive care unit[J]. China Med Devices, 2024, 39(10):98-103.
[12]	Florencia J, Moyaux T, Trilling L, et al. Toward improving dy-namic resource scheduling in the context of digital twin of emergency department[J]. IEEE Trans Autom Sci Eng, 2024, 22:7255-7267. doi: 10.1109/TASE.2024.3463489
[13]	ArabiDarrehDor G, Tivay A, Meador C, et al. Mathematical mo-deling,in-human evaluation and analysis of volume kinetics and kidney function after burn injury and resuscitation[J]. IEEE Trans Biomed Eng, 2022, 69(1):366-376. doi: 10.1109/TBME.2021.3094515
[14]	Rovati L, Gary PJ, Cubro E, et al. Development and usability testing of a patient digital twin for critical care education:a mixed methods study[J]. Front Med, 2024,10:1336897.
[15]	Cannon JW, Gruen DS, Zamora R, et al. Digital twin mathema-tical models suggest individualized hemorrhagic shock resus-citation strategies[J]. Commun Med, 2024, 4(1):113. doi: 10.1038/s43856-024-00535-6
[16]	Weaver L, Shamohammadi H, Saffaran S, et al. Digital twins of acute hypoxemic respiratory failure patients suggest a mecha-nistic basis for success and failure of noninvasive ventilation[J]. Crit Care Med, 2024, 52(9):e473-e484.
[17]	Aarzoo, Ghosh A, Pandhare V, et al. Expert-in-loop digital twin-based decision support system for early detection of ventila-tor-induced lung injury[J]. Procedia Comput Sci, 2024, 251:651-659. doi: 10.1016/j.procs.2024.11.164
[18]	Danesh A, Juraev F, El-Sappagh S, et al. Integrating digital twin technology with dynamic ensemble learning for sepsis prediction in intensive care units[J]. Jiis, 2024, 30(2):25-59. doi: 10.13088/jiis.2024.30.2.025
[19]	Aluvalu R, Mudrakola S, Uma Maheswari V, et al. The novel emergency hospital services for patients using digital twins[J]. Microprocess Microsyst, 2023,98:104794.
[20]	Chakshu NK, Nithiarasu P. An AI based digital-twin for prio-ritising pneumonia patient treatment[J]. Proc Inst Mech Eng H, 2022, 236(11):1662-1674. doi: 10.1177/09544119221123431
[21]	Ang CYS, Lee JWW, Chiew YS, et al. Virtual patient frame-work for the testing of mechanical ventilation airway pressure and flow settings protocol[J]. Comput Methods Programs Biomed, 2022,226:107146.
[22]	Hussain A, Yaseen MU, Imran M, et al. An attention-based Res-Net architecture for acute hemorrhage detection and classifi-cation:toward a health 4.0 digital twin study[J]. IEEE Access, 2022,10:126712-126727.
[23]	Caljé-van der Klei T, Sun QH, Chase JG, et al. Pulmonary response prediction through personalized basis functions in a virtual patient model[J]. Comput Methods Programs Biomed, 2024,244:107988.
[24]	Sun QH, Chase JG, Zhou C, et al. Predicting pulmonary dis-tension in a virtual patient model for mechanical ventilation[J]. IFAC-PapersOnLine, 2021, 54(15):91-96.
[25]	Lal A, Li GX, Cubro E, et al. Development and verification of a digital twin patient model to predict specific treatment response during the first 24 hours of sepsis[J]. Crit Care Explor, 2020, 2(11):e0249. doi: 10.1097/CCE.0000000000000249 pmid: 33225302
[26]	Zhou C, Geoffrey Chase J, Knopp J, et al. Virtual patients for mechanical ventilation in the intensive care unit[J]. Comput Methods Programs Biomed, 2021,199:105912.
[27]	Lal A, Pinevich Y, Gajic O, et al. Artificial intelligence and computer simulation models in critical illness[J]. World J Crit Care Med, 2020, 9(2):13-19. doi: 10.5492/wjccm.v9.i2.13 pmid: 32577412
[28]	胡慧娟, 王明帮, 雷崎方, 等. 数字孪生医院:改变医疗的未来[J]. 生物医学工程学杂志, 2024, 41(2):376-382.
	Hu HJ, Wang MB, Lei QF, et al. Digital twin hospitals:trans-forming the future of healthcare[J]. J Biomed Eng, 2024, 41(2):376-382.
[29]	陈利, 杨又. 区块链技术在临床护理中的应用进展[J]. 中华护理杂志, 2024, 59(2):250-256. doi: 10.3761/j.issn.0254-1769.2024.02.018
	Chen L, Yang Y. Research progress of blockchain technology in clinical nursing[J]. Chin J Nurs, 2024, 59(2):250-256. doi: 10.3761/j.issn.0254-1769.2024.02.018