收稿日期: 2025-04-06
网络出版日期: 2026-03-02
The application of clinical decision support system in emergency triage management：a scoping review
Received date: 2025-04-06
Online published: 2026-03-02
目的 对临床决策支持系统（clinical decision support system，CDSS）在急诊分诊管理中应用的相关研究进行范围综述，旨在为急诊正确应用CDSS提供参考。方法 计算机检索PubMed、Web of Science、Embase、Cochrane Library、EBSCO、中国知网、万方数据库、维普中文科技期刊数据库，检索时限为建库至2025年2月。筛选有关CDSS在急诊分诊中应用的相关研究，基于范围综述的研究框架分析研究的基本特征、CDSS的功能、应用的可行性及结局指标等。结果 共纳入14篇文献。CDSS可有效改善资源分配，提高分诊效率，但其在分诊管理中的应用以自动分级为主，缺乏标准化的结局指标。结论 CDSS能提高分诊准确率、缩短危重患者等待时间，并降低ICU入院率。未来需开展多中心研究，建立标准化CDSS结局指标体系，开展高质量的随机对照试验研究以验证其应用效果。
于雅诺 , 宋雨航 , 高凌婕 , 李晓波 . 临床决策支持系统在急诊分诊管理中应用的范围综述[J]. 中华急危重症护理杂志, 2026 , 7(3) : 306 -311 . DOI: 10.3761/j.issn.2096-7446.2026.03.008
Objective To conduct a scoping review of research on the application of clinical support decision system（CDSS） in emergency triage，aiming to provide reference for the correct use of CDSS in emergency departments.Methods Computer retrieval of PubMed，Web of Science，Embase，Cochrane Library，EBSCO，CNKI，Wanfang Database，and VIP were conducted，and the search period was from the establishment of the database until February 2025. We screened relevant studies on the application of CDSS in emergency triage，analyzed the basic characteristics，functions，feasibility，and effects of CDSS based on a scoping review research framework.Results A total of 14 articles was included. CDSS can effectively improve resource allocation and enhance triage efficiency，but its application in triage management was mainly focused on automatic grading，lacking standardized outcome indicators.Conclusion CDSS can improve triage accuracy，shorten waiting time for critically ill patients，and reduce ICU admission rates. In the future，multi-center research should be conducted to establish a standardized CDSS outcome indicator system. High-quality randomized controlled trials should be conducted to validate its application effectiveness.
| [1] | 李晓婉, 熊莉娟, 王玉梅, 等. 急诊科患者就诊量的时间序列研究[J]. 中国护理管理, 2022, 22(12):1855-1859. |
| Li XW, Xiong LJ, Wang YM, et al. Time series analysis of number of patients in emergency department[J]. Chin Nurs Manag, 2022, 22(12):1855-1859. | |
| [2] | Eitel DR, Rudkin SE, Malvehy MA, et al. Improving service quality by understanding emergency department flow:a White Paper and position statement prepared for the American Academy of Emergency Medicine[J]. J Emerg Med, 2010, 38(1):70-79. |
| [3] | Javidan AP, Hansen K, Higginson I, et al. The International Federation for Emergency Medicine report on emergency department crowding and access block:a brief summary[J]. Emerg Med J, 2021, 38(3):245-246. |
| [4] | 王雪霏, 张婷, 黄立坤. 从急诊患者分流探讨有效分级诊疗[J]. 山西医药杂志, 2021, 50(15):2259-2261. |
| Wang XF, Zhang T, Huang LK. Discussion on effective graded diagnosis and treatment from emergency patients’ shunt[J]. Shanxi Med J, 2021, 50(15):2259-2261. | |
| [5] | Barnes S, Saria S, Levin S. An evolutionary computation approach for optimizing multilevel data to predict patient outcomes[J]. J Healthc Eng, 2018, 2018:7174803. |
| [6] | Agnihotri T, Fan M, McLeod S, et al. Impact of an electronic decision-support system on nursing triage process:a usability and workflow analysis[J]. Can J Nurs Res, 2021, 53(2):107-113. |
| [7] | 中华护理学会急诊专业委员会,浙江省急诊医学质量控制中心, 金静芬. 急诊预检分级分诊标准[J]. 中国急救复苏与灾害医学杂志, 2016, 11(4):338-340. |
| Emergency Nursing Committee of Chinese Nursing Association, Emergency Medical Quality Control Center of Zhejiang Province, Jin JF. Classification and triage criteria for emergency pre-examination[J]. China J Emerg Resusc Disaster Med, 2016, 11(4):338-340. | |
| [8] | 杨宇辉, 李素姣, 喻洪流, 等. 临床决策支持系统研究进展[J]. 生物医学工程学进展, 2021, 42(4):203-207. |
| Yang YH, Li SJ, Yu HL, et al. Research progress of clinical decision support system[J]. Shanghai Biomed Eng, 2021, 42(4):203-207. | |
| [9] | Boonstra A, Laven M. Influence of artificial intelligence on the work design of emergency department clinicians a systematic literature review[J]. BMC Health Serv Res, 2022, 22(1):669. |
| [10] | Anesi GL, Liu VX, Chowdhury M, et al. Association of ICU admission and outcomes in sepsis and acute respiratory failure[J]. Am J Respir Crit Care Med, 2022, 205(5):520-528. |
| [11] | Tricco AC, Lillie E, Zarin W, et al. PRISMA extension for scoping reviews(PRISMA-ScR):checklist and explanation[J]. Ann Intern Med, 2018, 169(7):467-473. |
| [12] | Choi A, Choi SY, Chung K, et al. Development of a machine learning-based clinical decision support system to predict clinical deterioration in patients visiting the emergency department[J]. Sci Rep, 2023, 13(1):8561. |
| [13] | Jiang HL, Mao HF, Lu HM, et al. Machine learning-based models to support decision-making in emergency department triage for patients with suspected cardiovascular disease[J]. Int J Med Inform, 2021, 145:104326. |
| [14] | Kim J, Chang H, Kim D, et al. Machine learning for prediction of septic shock at initial triage in emergency department[J]. J Crit Care, 2020, 55:163-170. |
| [15] | Nguyen M, Corbin CK, Eulalio T, et al. Developing machine learning models to personalize care levels among emergency room patients for hospital admission[J]. J Am Med Inform Assoc, 2021, 28(11):2423-2432. |
| [16] | Dong SL, Bullard MJ, Meurer DP, et al. Emergency triage:comparing a novel computer triage program with standard triage[J]. Acad Emerg Med, 2005, 12(6):502-507. |
| [17] | Reilly BM, Evans AT, Schaider JJ, et al. Impact of a clinical decision rule on hospital triage of patients with suspected acute cardiac ischemia in the emergency department[J]. JAMA, 2002, 288(3):342-350. |
| [18] | Horng S, Sontag DA, Halpern Y, et al. Creating an automated trigger for sepsis clinical decision support at emergency department triage using machine learning[J]. PLoS One, 2017, 12(4):e0174708. |
| [19] | Rendell K, Koprinska I, Kyme A, et al. The Sydney Triage to Admission Risk Tool(START2) using machine learning techniques to support disposition decision-making[J]. Emerg Med Australas, 2019, 31(3):429-435. |
| [20] | Stryer DB. The development and role of predictive instruments in acute coronary events:improving diagnosis and management[J]. J Cardiovasc Nurs, 2002, 16(3):1-8. |
| [21] | Xiao Y, Zhang J, Chi C, et al. Criticality and clinical department prediction of ED patients using machine learning based on heterogeneous medical data[J]. Comput Biol Med, 2023, 165:107390. |
| [22] | Holmstr?m IK, Kaminsky E, Lindberg Y, et al. Registered Nurses’ experiences of using a clinical decision support system for triage of emergency calls:a qualitative interview study[J]. J Adv Nurs, 2020, 76(11):3104-3112. |
| [23] | Amland RC, Sutariya BB. An investigation of sepsis surveillance and emergency treatment on patient mortality outcomes:an observational cohort study[J]. JAMIA Open, 2018, 1(1):107-114. |
| [24] | Boeddinghaus J, Doudesis D, Lopez-Ayala P, et al. Machine learning for myocardial infarction compared with guideline-recommended diagnostic pathways[J]. Circulation, 2024, 149(14):1090-1101. |
| [25] | McLeod SL, McCarron J, Ahmed T, et al. Interrater reliability,accuracy,and triage time pre-and post-implementation of a real-time electronic triage decision-support tool[J]. Ann Emerg Med, 2020, 75(4):524-531. |
| [26] | Alsabri M, Aderinto N, Mourid MR, et al. Artificial intelligence for pediatric emergency medicine[J]. J Med Surg Public Health, 2024, 3:100137. |
| [27] | Ala A, Chen F. Alternative mathematical formulation and hybrid meta-heuristics for patient scheduling problem in health care clinics[J]. Neural Comput Appl, 2020, 32(13):8993-9008. |
| [28] | 中华护理学会急诊护理专业委员会,浙江省急诊医学质量控制中心. 急诊预检分诊标准(成人部分)[J]. 中华急危重症护理杂志, 2020, 1(1):45-48. |
| Emergency Nursing Committee of Chinese Nursing Association,Emergency Medicine Quality Control Center of Zhejiang Province. Emergency triage scale(adult part)[J]. Chin J Emerg Crit Care Nurs, 2020, 1(1):45-48. | |
| [29] | Poppas A, Elkind MSV, O’Gara PT, et al. Optimizing clinical practice guidelines:a key step to improving patient care and outcomes[J]. J Am Coll Cardiol, 2020, 76(18):2170-2172. |
| [30] | 张山, 吴瑛. 临床实践指南依从性改善的干预方案研究进展[J]. 中国现代医生, 2024, 62(6):112-114,119. |
| Zhang S, Wu Y. Research progress of intervention schemes for improving compliance of clinical practice guidelines[J]. China Mod Dr, 2024, 62(6):112-114,119. | |
| [31] | Stone EL. Clinical decision support systems in the emergency department:opportunities to improve triage accuracy[J]. J Emerg Nurs, 2019, 45(2):220-222. |
| [32] | Sutriningsih A, Wahyuni CU, Haksama S. Factors affecting emergency nurses’ perceptions of the triage systems[J]. J Public Health Res, 2020, 9(2):1808. |
| [33] | Arslanian-Engoren C, Hagerty B, Antonakos CL, et al. The feasibility and utility of the aid to cardiac triage intervention to improve nurses’ cardiac triage decisions[J]. Heart Lung, 2010, 39(3):201-207. |
| [34] | Zachariasse JM, van der Hagen V, Seiger N, et al. Performance of triage systems in emergency care:a systematic review and meta-analysis[J]. BMJ Open, 2019, 9(5):e026471. |
| [35] | Wuytack F, Meskell P, Conway A, et al. The effectiveness of physiologically based early warning or track and trigger systems after triage in adult patients presenting to emergency departments:a systematic review[J]. BMC Emerg Med, 2017, 17(1):38. |
| [36] | Fernandes M, Vieira SM, Leite F, et al. Clinical decision support systems for triage in the emergency department using intelligent systems:a review[J]. Artif Intell Med, 2020, 102:101762. |
| [37] | Kuriyama A, Urushidani S, Nakayama T. Five-level emergency triage systems:variation in assessment of validity[J]. Emerg Med J, 2017, 34(11):703-710. |
| [38] | Bi?kin ?etin S, Cebeci F. Nurses’ experiences of using a computer-based triage decision support system in the emergency department[J]. Nurs Crit Care, 2024, 29(5):1078-1085. |
| [39] | Zaboli A, Ausserhofer D, Pfeifer N, et al. Triage of patients with fever:The Manchester triage system’s predictive validity for sepsis or septic shock and seven-day mortality[J]. J Crit Care, 2020, 59:63-69. |
| [40] | Ganesan A, Paul A, Nagabushnam G, et al. Human-in-the-loop predictive analytics using statistical learning[J]. J Healthc Eng, 2021, 2021:9955635. |
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