生成式人工智能结合情景模拟教学方式在重症医学科住院医师规范化培训中的应用

doi:10.3760/cma.j.cn115259-20251207-01585

摘要/Abstract

摘要： 目的探讨生成式人工智能(generative artificial intelligence, GenAI)结合情景模拟教学方式在重症医学科住院医师规范化培训(简称住培)中的应用效果。方法采用试验对照研究方法。选取2024年1至12月在广州中医药大学第二附属医院(广东省中医院)总院重症医学科接受住培的128名学员为研究对象,根据轮训时间进行分组,2024年1至6月轮训的学员纳入对照组(63名),采用传统教学方式,将2024年7至12月轮训的住培学员纳入试验组(65名),采用GenAI结合情景模拟教学方式。教学结束后,通过比较两组学员的出科考核成绩及迷你临床演练评估(Mini-clinical evaluation exercise,Mini-CEX)量表评分和教学满意度问卷调查结果评价培训效果。采用独立样本t检验、χ²检验或 Fisher 确切概率法分析相关数据。结果试验组学员的出科考核成绩[(85.74±1.69)分]高于对照组学员[(80.94±0.78)分],其差异具有统计学意义(P<0.001)。Mini-CEX评分结果显示,试验组学员在病史询问[(7.28±1.13)分]、体格检查[(7.54±0.87)分]、临床判断[(6.92±0.96)分]及整体表现[(7.65±0.82)分]等方面评分均高于对照组学员[(6.87±0.98)分、(7.10±1.13)分、(6.46±1.44)分、(7.21±1.12)分],其差异均具有统计学意义(均P<0.05)。教学满意度问卷调查结果显示,试验组学员在提升学习兴趣和效率[(4.38±0.60)分]、提高临床工作能力[(4.43±0.61)分]、提高医患沟通能力[(4.32±0.73)分]等方面的评分均高于对照组学员[(4.14±0.74)分、(4.14±0.74)分、(4.03±0.82)分],其差异均具有统计学意义(均P<0.05)。结论 GenAI结合情景模拟教学方式有助于提高重症医学科住培学员的培训效果,可以为优化重症医学住培教学模式提供参考。

关键词: 重症监护, 住院医师规范化培训, 生成式人工智能, 情景模拟教学

Abstract: Objective To investigate the application effect of combining Generative Artificial Intelligence (GenAI) with scenario-based simulation teaching for standardized residency training in the intensive care unit (ICU). Methods This controlled experimental study included 128 resident physicians who underwent standardized residency training in the Department of ICU at the Second Affiliated Hospital of Guangzhou University of Chinese Medicine (Guangdong Provincial Hospital of Chinese Medicine) from January to December 2024. Participants were grouped according to their rotation period: residents rotating from January to June 2024 were assigned to the control group (n = 63) and received traditional teaching, while those rotating from July to December 2024 were assigned to the experimental group (n = 65) and received GenAI with scenario-based simulation teaching. After the teaching intervention, training efficacy were evaluated by comparing end-of-rotation assessment scores, Mini-clinical evaluation exercise (Mini-CEX) scale scores, and teaching satisfaction questionnaire results between the two groups. Data were analyzed using the independent-samples t test, Chi-square tests, or Fisher′s exact test. Results The end-of-rotation assessment score of the experimental group (85.74±1.69) was higher than that of the control group (80.94±0.78), with a statistically significant difference (P<0.001). The Mini-CEX assessment showed that the scores of the experimental group in history taking (7.28±1.13), physical examination (7.54±0.87), clinical judgment (6.92±0.96), and overall clinical performance (7.65±0.82) were significantly higher than those of the control group (6.87±0.98, 7.10±1.13, 6.46±1.44, and 7.21±1.12), with all differences reaching statistical significance (all P<0.05). The teaching satisfaction survey showed that the scores of the experimental group in improving learning interest and efficiency (4.38±0.60), enhancing clinical work competence (4.43±0.61), and improving doctor-patient communication skills (4.32±0.73) were significantly higher than those of the control group (4.14±0.74, 4.14±0.74, and 4.03±0.82), with all differences statistically significant (all P<0.05). Conclusions GenAI-integrated scenario-based simulation teaching can effectively improve the training outcomes of standardized residency trainees in critical care medicine, and provide a reference for optimizing the teaching model of critical care residency training.

Key words: Intensive care, Standardized residency training, Generative artificial intelligence, Scenario simulation teaching

中图分类号:

何健卓, 吴铁生, 古惠文, 张敏州, 郭力恒. 生成式人工智能结合情景模拟教学方式在重症医学科住院医师规范化培训中的应用[J]. 中华医学教育杂志, 2026, 46(9): 684-689, DOI: 10.3760/cma.j.cn115259-20251207-01585.

He Jianzhuo, Wu Tiesheng, Gu Huiwen, Zhang Minzhou, Guo Liheng. Application of generative artificial intelligence combined with scenario-based simulation teaching in standardized residency training in critical care medicine[J]. Chinese Journal of Medical Education, 2026, 46(9): 684-689, DOI: 10.3760/cma.j.cn115259-20251207-01585.

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