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中国防痨杂志 ›› 2026, Vol. 48 ›› Issue (5): 661-669.doi: 10.19982/j.issn.1000-6621.20250482

• 论著 • 上一篇    下一篇

结核病患者数字化中西医结合医院-家庭健康管理证据总结

高丹1(), 姚丽伟2, 黄金鹏1, 刘晓霞2, 张玥1, 凌琳1   

  1. 1杭州市红十字会医院结核病诊疗中心, 杭州 310003
    2杭州市红十字会医院护理部, 杭州 310003
  • 收稿日期:2025-12-04 出版日期:2026-05-10 发布日期:2026-04-27
  • 通信作者: 高丹 E-mail:332849649@qq.com
  • 基金资助:
    浙江省中医药科技计划项目(2025ZL443);浙江省医药卫生科技计划项目(2025KY1123)

Evidence summary on hospital-family digital health management with integrated traditional Chinese and Western medicine for tuberculosis patients

Gao Dan1(), Yao Liwei2, Huang Jinpeng1, Liu Xiaoxia2, Zhang Yue1, Ling Lin1   

  1. 1Tuberculosis Treatment Center, Hangzhou Red Cross Hospital, Hangzhou 310003, China
    2Department of Nursing, Hangzhou Red Cross Hospital, Hangzhou 310003, China
  • Received:2025-12-04 Online:2026-05-10 Published:2026-04-27
  • Contact: Gao Dan E-mail:332849649@qq.com
  • Supported by:
    Zhejiang Provincial Traditional Chinese Medicine Science and Technology Planning Project(2025ZL443);Zhejiang Provincial Medical and Health Science and Technology Project(2025KY1123)

摘要:

目的: 通过检索、筛选、评价和总结结核病患者医院-家庭中西医结合数字化健康管理的最佳证据,为临床开展结核病患者健康管理提供依据。方法: 基于结构化证据总结问题的PIPOST模式(即:研究对象-干预方法-实施者-结局指标-实践场所-证据类型)确定循证问题,依据循证检索资源“5S”模型,系统检索UpToDate、PubMed、Web of Science、Embase、Cochrane Library、CINAHL、BMJ Best Practices、JBI循证卫生保健中心数据库、中国知网、万方数据库、维普中文科技期刊数据库、中国生物医学文献服务系统及美国国立指南网、苏格兰国际指南网、World Health Organization网站、美国国家结核病控制协会、欧洲呼吸学会、医脉通指南网、中国防痨协会等专业学会网站中关于结核病患者健康管理的临床决策、指南、专家共识、证据总结、系统评价、Meta分析、随机对照试验,检索时限为建库至2025年7月30日,由2名接受过循证护理学培训的研究者分别对文献进行证据提取。结果: 共纳入25篇文献,包括2篇临床决策、10篇指南、3篇证据总结、3篇专家共识、5篇系统评价、2篇随机对照试验;通过文献证据提取,共形成28条最佳证据,涵盖组建多学科团队、疾病认知与预防管理、症状识别与服药管理、康复管理、出院准备与居家管理、数字化管理与效果评价等6个主题。结论: 研究系统整合了结核病患者医院-家庭中西医结合数字化健康管理的28条最佳证据,为优化中西医结合数字化健康管理模式提供了科学、可靠、时效性强的循证依据。医护人员可根据临床情境结合相关证据构建护理方案,进一步提高结核病患者的健康管理依从性。

关键词: 结核, 病人依从, 护理管理研究, 循证医学, 决策支持系统,管理

Abstract:

Objective: By searching, screening, evaluating and summarizing the best evidence on hospital-family digital health management with integrated traditional Chinese and Western medicine for tuberculosis patients, to provide a basis for clinical health management implementation. Methods: Evidence-based questions were identified with PIPOST model (Population-Intervention-Professional-Outcome-Setting-Type of evidence) based on structured evidence. Guided by the “5S” evidence hierarchy model, a systematic search was conducted in UpToDate, PubMed, Web of Science, Embase, the Cochrane Library, CINAHL, BMJ Best Practice, JBI, CNKI, WanFang Data, VIP, and CBM, as well as the professional society websites such as WHO, SIGN, NTCA, ERS, MedLive, and CATA, for clinical decision support tools, guidelines, expert consensus statements, evidence summaries, systematic reviews, meta-analyses, and RCTs on health management for tuberculosis patients. The search covered all available records from database establishment to July 30, 2025. Two researchers trained in evidence-based nursing independently extracted evidence from the literatures. Results: A total of 25 literatures were included, including 2 clinical decision-making articles, 10 guidelines, 3 evidence summaries, 3 expert consensus, 5 systematic reviews, and 2 randomized controlled trials. Therefore 28 best pieces of evidence were summarized, which covered 6 themes: multidisciplinary team building, disease cognition and prevention management, symptom identification and medication management, rehabilitation management, discharge preparation and home management, and digital management and effect evaluation. Conclusion: This study summarizes the best evidence of digital hospital-family health management with integrated traditional Chinese and Western medicine for tuberculosis patients, and provides scientific, reliable and highly timely evidence-based clinical management plans. Medical staff can construct nursing programs based on relevant evidence according to the clinical situations, improving the health management compliance of tuberculosis patients.

Key words: Tuberculosis, Patient compliance, Nursing administration research, Evidence-based medicine, Decision support systems, management

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