Application of big data-based appropriate Traditional Chinese Medicine nursing techniques (基于大数据的中医护理适宜技术应用现状)
A bibliometric analysis was conducted to identify the key diseases, techniques, and outcome indicators associated with appropriate Traditional Chinese Medicine nursing techniques, and a literature review was conducted to summarize the current status of the combination of big data technology and Trad...
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| Format: | Article |
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Journal of International Translational Medicine Limited
2025-04-01
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| Series: | 中西医结合护理 |
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| Online Access: | http://www.zxyjhhl.hk/thesisDetails#10.55111/j.issn2709-1961.20231225002 |
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| author | LUN Jun (陆俊) ZHANG Chengrui (张呈蕊) XING Ying (邢滢) DONG Qiuting (董秋婷) LUAN Wei (栾伟) |
| author_facet | LUN Jun (陆俊) ZHANG Chengrui (张呈蕊) XING Ying (邢滢) DONG Qiuting (董秋婷) LUAN Wei (栾伟) |
| author_sort | LUN Jun (陆俊) |
| collection | DOAJ |
| description | A bibliometric analysis was conducted to identify the key diseases, techniques, and outcome indicators associated with appropriate Traditional Chinese Medicine nursing techniques, and a literature review was conducted to summarize the current status of the combination of big data technology and Traditional Chinese Medicine nursing. At present, the intelligent medicine and healthcare with the core of “connecting clinical diagnosis and treatment” has been formed in the field of Traditional Chinese Medicine. There is a lack of researches on the appropriate techniques of Traditional Chinese Medicine nursing. Combination of the big data and its derivatives with the appropriate techniques of Traditional Chinese Medicine nursing has become an inevitable trend. However, the development bottleneck still exists due to the non-standard information collection, inadequate information sharing and the shortage of clinical human resources. (本研究采用文献计量学分析中医护理适宜技术领域内关注的重点疾病、技术与结局指标, 采用文献回顾梳理中医及中医适宜护理技术与大数据技术的结合现状。目前中医领域内已形成以“对接临床诊疗”为核心的智慧医疗, 中医护理适宜技术作为中医细分领域, 此类研究并不充分, 但将大数据及其衍生技术应用于中医护理适宜技术已成为必然趋势。中医护理技术与大数据结合具有广阔的应用前景, 但由于目前信息收集不规范、共享不充分及人力资源问题, 仍存在发展瓶颈。) |
| format | Article |
| id | doaj-art-603eae6ef0df406dae13709473dddb38 |
| institution | OA Journals |
| issn | 2709-1961 |
| language | zho |
| publishDate | 2025-04-01 |
| publisher | Journal of International Translational Medicine Limited |
| record_format | Article |
| series | 中西医结合护理 |
| spelling | doaj-art-603eae6ef0df406dae13709473dddb382025-08-20T02:31:33ZzhoJournal of International Translational Medicine Limited中西医结合护理2709-19612025-04-0111410511010.55111/j.issn2709-1961.20231225002CJIN20231225002Application of big data-based appropriate Traditional Chinese Medicine nursing techniques (基于大数据的中医护理适宜技术应用现状)LUN Jun (陆俊)0ZHANG Chengrui (张呈蕊)1XING Ying (邢滢)2DONG Qiuting (董秋婷)3LUAN Wei (栾伟)4Department of Nursing, Shuguang Hospital Affiliated to Shanghai University of Traditional Chinese Medicine, Shanghai, 200021 (上海中医药大学附属曙光医院 护理部, 上海, 200021)Shanghai Jiaotong University School of Nursing, Shanghai, 200025 (上海交通大学护理学院, 上海, 200025)Shanghai Jiaotong University School of Nursing, Shanghai, 200025 (上海交通大学护理学院, 上海, 200025)Department of Nursing, Shuguang Hospital Affiliated to Shanghai University of Traditional Chinese Medicine, Shanghai, 200021 (上海中医药大学附属曙光医院 护理部, 上海, 200021)Department of Nursing, Shuguang Hospital Affiliated to Shanghai University of Traditional Chinese Medicine, Shanghai, 200021 (上海中医药大学附属曙光医院 护理部, 上海, 200021)A bibliometric analysis was conducted to identify the key diseases, techniques, and outcome indicators associated with appropriate Traditional Chinese Medicine nursing techniques, and a literature review was conducted to summarize the current status of the combination of big data technology and Traditional Chinese Medicine nursing. At present, the intelligent medicine and healthcare with the core of “connecting clinical diagnosis and treatment” has been formed in the field of Traditional Chinese Medicine. There is a lack of researches on the appropriate techniques of Traditional Chinese Medicine nursing. Combination of the big data and its derivatives with the appropriate techniques of Traditional Chinese Medicine nursing has become an inevitable trend. However, the development bottleneck still exists due to the non-standard information collection, inadequate information sharing and the shortage of clinical human resources. (本研究采用文献计量学分析中医护理适宜技术领域内关注的重点疾病、技术与结局指标, 采用文献回顾梳理中医及中医适宜护理技术与大数据技术的结合现状。目前中医领域内已形成以“对接临床诊疗”为核心的智慧医疗, 中医护理适宜技术作为中医细分领域, 此类研究并不充分, 但将大数据及其衍生技术应用于中医护理适宜技术已成为必然趋势。中医护理技术与大数据结合具有广阔的应用前景, 但由于目前信息收集不规范、共享不充分及人力资源问题, 仍存在发展瓶颈。)http://www.zxyjhhl.hk/thesisDetails#10.55111/j.issn2709-1961.20231225002appropriate traditional chinese medicine nursing techniquesbig datanursing by syndrome differentiationtraditional chinese medicine nursing中医护理适宜技术大数据辨证施护中医护理 |
| spellingShingle | LUN Jun (陆俊) ZHANG Chengrui (张呈蕊) XING Ying (邢滢) DONG Qiuting (董秋婷) LUAN Wei (栾伟) Application of big data-based appropriate Traditional Chinese Medicine nursing techniques (基于大数据的中医护理适宜技术应用现状) 中西医结合护理 appropriate traditional chinese medicine nursing techniques big data nursing by syndrome differentiation traditional chinese medicine nursing 中医护理适宜技术 大数据 辨证施护 中医护理 |
| title | Application of big data-based appropriate Traditional Chinese Medicine nursing techniques (基于大数据的中医护理适宜技术应用现状) |
| title_full | Application of big data-based appropriate Traditional Chinese Medicine nursing techniques (基于大数据的中医护理适宜技术应用现状) |
| title_fullStr | Application of big data-based appropriate Traditional Chinese Medicine nursing techniques (基于大数据的中医护理适宜技术应用现状) |
| title_full_unstemmed | Application of big data-based appropriate Traditional Chinese Medicine nursing techniques (基于大数据的中医护理适宜技术应用现状) |
| title_short | Application of big data-based appropriate Traditional Chinese Medicine nursing techniques (基于大数据的中医护理适宜技术应用现状) |
| title_sort | application of big data based appropriate traditional chinese medicine nursing techniques 基于大数据的中医护理适宜技术应用现状 |
| topic | appropriate traditional chinese medicine nursing techniques big data nursing by syndrome differentiation traditional chinese medicine nursing 中医护理适宜技术 大数据 辨证施护 中医护理 |
| url | http://www.zxyjhhl.hk/thesisDetails#10.55111/j.issn2709-1961.20231225002 |
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