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| Research on teaching reform for chronic disease prevention and control talents in grassroots healthcare institutions empowered by digital intelligence |
| LI Zhanyi, JIN Dian, ZHANG Xiaoting, ZHENG Siyuan, JIN Jiamei, FU Xiaoying, WANG Yuying, YAN Zhe'an, SHEN Yuhao, YANG Li, LI Yingjun, JING Zhengwei
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| School of Public Health, Hangzhou Medical University, Hangzhou, Zhejiang 311399, China |
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Abstract The accelerating population aging and the rejuvenation trend of chronic diseases in China have continuously aggravated the chronic disease burden, making chronic disease prevention and control increasingly essential. General practitioners are the core workforce for chronic disease prevention and control, and their training quality directly determines the performance of grassroots healthcare services. Digital intelligence, built on digital infrastructure, enables standardized data collection, analysis, and product transformation adapted to clinical scenarios. Leveraging artificial intelligence, big data, and machine learning, it mines high-value clinical and public health key information to support evidence-based medical decision-making and optimize the diagnosis, treatment, and management processes for chronic diseases. On the basis of the characteristics of chronic disease prevention and control talents in grassroots healthcare institutions and the background of digital intelligence development in the new era, this study systematically retrieves and reviews domestic and foreign research on the profile, training paths and digital intelligence-enabled teaching progress of grassroots medical talents engaged in chronic disease prevention and control. Focusing on the core challenges existing in current teaching and training for such talents, and combining practical experience in grassroots medical education, this study proposes a digital intelligence empowerment-oriented strategy to effectively align the supply of training resources with the actual demands of grassroots settings, so as to construct a context-adapted systematic training mechanism and optimized development path for chronic disease prevention and control talents in grassroots healthcare institutions.
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Received: 09 April 2026
Revised: 22 June 2026
Published: 21 August 2026
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[1] 徐喆,李胜,李书,等.1990—2023年中国癌症、心血管疾病、慢性呼吸系统疾病和糖尿病的疾病负担趋势、预测与风险因素分析[J].中国慢性病预防与控制,2026,34(3):201-212.
[2] 夏翠翠,李建新.健康老龄化还是病痛老龄化——健康中国战略视角下老年人口的慢性病问题[J].探索与争鸣,2018,(10):115-121,144.
[3] 中华人民共和国国家卫生健康委员会. 2024中国卫生健康统计年鉴[EB/OL].[2026-06-22].https://www.nhc.gov.cn/mohwsbwstjxxzx/tjtjnj/202601/4c453cf4278941ab8465846d1dc90e08.shtml.
[4] 王鑫,施培武,沈群红,等.我国慢性病防治人员素质分析[J].中国卫生资源,2021,24(3):293-297.
[5] 中华人民共和国国家卫生健康委办公厅.国家卫生健康委办公厅关于印发基层慢性病健康管理服务能力建设指引的通知[EB/OL].[2026-06-22].https://www.gov.cn/zhengce/zhengceku/202511/content_7049144.htm.
[6] 万晓文,曾程,李宗友,等.中国全科医生培养和使用发展报告(2022)[J].中国全科医学,2024,27(10):1153-1161.
[7] 线福华,路孝琴,吕兆丰.全科医生培养模式及其实施中相关问题的思考[J].中国全科医学,2012,15(22):2498-2501.
[8] 齐殿君,江南,于晓松.我国本科生全科医学教育教学情况调查研究[J].中国全科医学,2024,27(7):789-793.
[9] 侯姝羽,曾欣,王婷婷,等.近十年中国全科医生继续教育研究的系统评价[J].中国全科医学,2024,27(28):3476-3482.
[10] 赵稳稳,王荣英,张金佳,等.全科医学师资质量评价体系建设研究进展[J].中国全科医学,2024,27(7):784-788,800.
[11] Huang Z L,Semwal M,Lee S Y,et al.Digital health professions education on diabetes management:systematic review by the digital health education collaboration[J/OL].J Med Internet Res,2019,21(2)[2026-06-22].https://doi.org/10.2196/12997.
[12] Shaw K,Henning M A,Webster C S.Artificial intelligence in medical education:a scoping review of the evidence for efficacy and future directions[J].Med Sci Educ,2025,35(3):1803-1816.
[13] 陈国湘,李俊,韦华,等.基于人工智能技术的全科医生培养模式探索[J].中华全科医学,2021,19(2):167-170.
[14] 张璇,陈琦,王佳贺.人工智能赋能全科医生继续教育改革的研究[J].中国继续医学教育,2024,16(14):18-21.
[15] 清华大学万科公共卫生与健康学院,北京大学公共卫生学院,中国医师协会全科医师分会,等.智能全科医生中国专家共识[J].中国全科医学,2025,28(2):135-142.
[16] 张先庚,梁小利,周佳丽,等.民族地区卫生人才数智化培养:现状、挑战与未来趋势[J].医药高职教育与现代护理,2026,9(3):204-208.
[17] 于芮晴. 依托数字化,探索卫生事业管理人才培养新路径[J].人力资源,2025(12):62-63.
[18] 李小勇,段蕾蕾,武轶群,等.鄞州区伤害智能监测系统介绍及应用[J].预防医学,2023,35(11):1009-1012.
[19] Tudor Car L,Kyaw B M,Nannan Panday R S,et al.Digital health training programs for medical students:scoping review[J/OL].JMIR Med Educ,2021,7(3)[2026-06-22].https://doi.org/10.2196/28275.
[20] Car J,Ong Q C,Erlikh Fox T,et al.The digital health competencies in medical education framework:an international consensus statement based on a Delphi study[J/OL].JAMA Netw Open,2025[2026-06-22].https://doi.org/10.1001/jamanetworkopen. 2024.53131.
[21] 李建周,汪丽燕.新医科背景下智能医学工程专业人才培养现状与问题分析[J].医学信息学杂志,2025,46(5):98-102.
[22] 陈玲,吴颖敏,李家伟,等.民族地区基层医疗卫生机构信息化建设现状与效能研究[J].中国全科医学,2022,25(16):2003-2007,2020.
[23] 王启帆. 数字化赋能医学教育:应用、困境与路径[J].医学与哲学,2024,45(10):65-68.
[24] 潘慧,弓孟春,卢景辉,等.医学教师的人工智能素养专家共识(2025年版)[J].中国医学科学院学报,2026,48(1):1-12.
[25] 胡艳蓓. 数字化转型赋能区域教师高质量发展的逻辑与进路[J].教学与管理,2024(10):32-35.
[26] 杨阳,李雯宇,王婷,等.AGIL模型视域下数智化医学师资队伍建设的研究[J].中国病案,2025,26(3):96-99.
[27] 林梓,顾海.数智赋能视域下医共体医防融合的创新机制与实现路径[J].南京社会科学,2024(6):47-54.
[28] 孙宝志. 医学精准教育概念的提出及启示[J].医学与哲学,2024,45(19):63-67.
[29] 郑晓博,张中伟.人工智能在重症医学教学中的应用展望[J].西南医科大学学报,2023,46(6):468-472.
[30] 蔡虹,胡西厚,李建昌.人工智能时代医学院校教学改革的坚守与创新[J].中国继续医学教育,2021,13(29):4-8.
[31] Maicher K R,Stiff A,Scholl M,et al.Artificial intelligence in virtual standardized patients:combining natural language understanding and rule based dialogue management to improve conversational fidelity[J].Med Teach,2023,45(3):279-285.
[32] 中华人民共和国国务院.医疗卫生强基工程实施方案[EB/OL].[2026-06-22].https://www.gov.cn/zhengce/zhengceku/202509/content_7039981.htm.
[33] Park T I,Seo J,Yoon H J,et al.Institutionalizing convergence education for medical artificial intelligence[J].Biomed Eng Lett,2025,15(6):1073-1083.
[34] 石蕊. “智汇AI+”时代医学人才培养模式变革及实践[J].陕西教育(高教),2025(8):64-66.
[35] 韩标,刘文静,孙立元,等.“多中心”协同构建“医工融合”人才培养模式[J].华夏医学,2025,38(5):182-187. |
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