基于护理诊断的临床护理计划决策支持系统的研发与应用

doi:10.3761/j.issn.0524-1769.2025.20.004

摘要/Abstract

摘要：

目的基于护理诊断研发临床护理计划决策支持系统，并探讨其应用效果，以期为优化临床护理流程、提高护理质量提供参考。方法组建多学科研究团队，从护理诊断、护理措施、目标追踪3个方面构建临床护理计划决策支持系统的知识库，基于深度学习算法开发人工智能捕获技术、实现决策支持功能，并生成护理计划单。2024年10月在宜昌市某三级甲等医院进行正式应用。比较系统应用前（2024年6—8月）及应用后（2024年10—12月）的护理诊断制订率、护理计划单书写正确率、临床护理质量评分。结果系统应用后，护理诊断制订率从94.88%提高至97.25%，护理计划单书写正确率从90.38%提高至95.34%，且临床护理质量评价表各维度评分均高于系统应用前，差异具有统计学意义（均P<0.05）。结论使用该系统可提高护理诊断制订率及护理计划单书写正确率，同时有助于改善临床护理质量。

关键词: 护理诊断, 护理计划, 决策支持, 信息系统, 护理管理研究

Abstract:

Objective To develop a decision support system for clinical nursing plans based on nursing diagnosis and explore its application effects，in order to provide references for optimizing the clinical nursing process and improving the quality of nursing. Methods A multidisciplinary research team was established to construct a clinical nursing plan decision support system framework from 3 aspects，namely nursing diagnosis，nursing interventions，and outcome tracking. The system built a clinical nursing diagnosis decision knowledge base through 3 dimensions，namely basic nursing diagnoses，specialty disease nursing diagnoses，and nursing-related technical diagnoses. Deep learning-based artificial intelligence capture technology was developed to achieve intelligent matching and generate clinical nursing plan forms. Implemented in a tertiary hospital in Yichang City，Hubei Province，a control group（June to August 2024） and an experimental group（October to December 2024） were compared regarding nursing diagnosis implementation rate，nursing plan documentation accuracy，and clinical nursing quality scores. Results This research showed a significant improvements for nursing diagnosis implementation rate increased from 94.88% to 97.25%，and nursing plan documentation accuracy improved from 90.38% to 95.33%. Compared with the control group，the experimental group demonstrated statistically significant enhancements in deep vein thrombosis prevention，fall prevention，pressure injury management，unplanned extubation control，bloodstream infection control，catheter-related infection prevention，and key specialty nursing quality indicators（all P<0.05）. Conclusion The nursing diagnosis-based clinical decision support system effectively improves nurses’ diagnostic implementation rates，enhances documentation accuracy of nursing plans，and elevates overall clinical nursing quality.

Key words: Nursing Diagnosis, Nursing Plan, Decision Support, Information System, Nursing Administration Research

席祖洋, 魏永婷, 李茶香, 刘静兰, 崔克雄, 余良欢, 詹鸿静, 李晶晶, 唐青. 基于护理诊断的临床护理计划决策支持系统的研发与应用[J]. 中华护理杂志, 2025, 60(20): 2458-2463.

XI Zuyang, WEI Yongting, LI Chaxiang, LIU Jinglan, CUI Kexiong, YU Lianghuan, ZHAN Hongjing, LI Jingjing, TANG Qing. Development and application of a nursing diagnosis-based decision support system for clinical nursing plans[J]. Chinese Journal of Nursing, 2025, 60(20): 2458-2463.

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