1.Development and application of a nursing diagnosis-based decision support system for clinical nursing plans
Zuyang XI ; Yongting WEI ; Chaxiang LI ; Jinglan LIU ; Kexiong CUI ; Lianghuan YU ; Hongjing ZHAN ; Jingjing LI ; Qing TANG
Chinese Journal of Nursing 2025;60(20):2458-2464
Objective To develop a decision support system for clinical nursing plans based on nursing diagno-sis 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 clini-cal 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 preven-tion,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 di-agnosis-based clinical decision support system effectively improves nurses'diagnostic implementation rates,enhances documentation accuracy of nursing plans,and elevates overall clinical nursing quality.
2.Sinicization of Evidence-Informed Decision-Making Competence Measure for nurses and its reliability and validity test
Yongting WEI ; Shumei TIAN ; Jiao YANG ; Lianghuan YU ; Fu NI ; Yuqing FAN ; Yao XIAO ; Zuyang XI ; Juyan SHA ; Cong LIU
Chinese Journal of Nursing 2025;60(6):736-742
Objective To translate Evidence-Informed Decision-Making Competence Measure for Chinese nurses and test its validity and reliability.Methods A research group was set up to use the Brislin translation model to translate the original scale into Chinese,and the back translation,cross-cultural adaptation,pre-experiment and cognitive interview were conducted to finally form the Chinese version of the Evidence-Informed Decision-Making Competence Measure for nurses.A total of 1 247 nurses from 7 tertiary A hospitals in Beijing,Hubei,Hunan and Xinjiang were selected by convenience sampling method in April 2024 to test its reliability and validity.Results 1 026 effective question-naires were collected,with an effective recovery rate of 82.28%.The Chinese version of the Evidence-Informed Decision-Making Competence Measure included 25 items,including knowledge/skill,attitude and behavior.A total of 3 common factors were extracted from exploratory factor analysis,and the cumulative variance contribution rate was 91.725%.The content validity index at the item level was 0.83-1.00;the content validity index at the scale level was 0.988;the calibration association validity was 0.496.The Cronbach's α coefficient of the whole scale was 0.992;the half-point reliability was 0.930;the retest reliability was 0.927.Conclusion The Chinese version of Evidence-Informed Decision-Making Competence Measure for nurses has good reliability and validity,and it can be used to evaluate the evidence-informed decision-making competence of Chinese nurses,provide references for promoting evidence-based nursing practice and evidence-informed decision-making.
3.Development and application of a nursing diagnosis-based decision support system for clinical nursing plans
Zuyang XI ; Yongting WEI ; Chaxiang LI ; Jinglan LIU ; Kexiong CUI ; Lianghuan YU ; Hongjing ZHAN ; Jingjing LI ; Qing TANG
Chinese Journal of Nursing 2025;60(20):2458-2464
Objective To develop a decision support system for clinical nursing plans based on nursing diagno-sis 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 clini-cal 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 preven-tion,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 di-agnosis-based clinical decision support system effectively improves nurses'diagnostic implementation rates,enhances documentation accuracy of nursing plans,and elevates overall clinical nursing quality.
4.Sinicization of Evidence-Informed Decision-Making Competence Measure for nurses and its reliability and validity test
Yongting WEI ; Shumei TIAN ; Jiao YANG ; Lianghuan YU ; Fu NI ; Yuqing FAN ; Yao XIAO ; Zuyang XI ; Juyan SHA ; Cong LIU
Chinese Journal of Nursing 2025;60(6):736-742
Objective To translate Evidence-Informed Decision-Making Competence Measure for Chinese nurses and test its validity and reliability.Methods A research group was set up to use the Brislin translation model to translate the original scale into Chinese,and the back translation,cross-cultural adaptation,pre-experiment and cognitive interview were conducted to finally form the Chinese version of the Evidence-Informed Decision-Making Competence Measure for nurses.A total of 1 247 nurses from 7 tertiary A hospitals in Beijing,Hubei,Hunan and Xinjiang were selected by convenience sampling method in April 2024 to test its reliability and validity.Results 1 026 effective question-naires were collected,with an effective recovery rate of 82.28%.The Chinese version of the Evidence-Informed Decision-Making Competence Measure included 25 items,including knowledge/skill,attitude and behavior.A total of 3 common factors were extracted from exploratory factor analysis,and the cumulative variance contribution rate was 91.725%.The content validity index at the item level was 0.83-1.00;the content validity index at the scale level was 0.988;the calibration association validity was 0.496.The Cronbach's α coefficient of the whole scale was 0.992;the half-point reliability was 0.930;the retest reliability was 0.927.Conclusion The Chinese version of Evidence-Informed Decision-Making Competence Measure for nurses has good reliability and validity,and it can be used to evaluate the evidence-informed decision-making competence of Chinese nurses,provide references for promoting evidence-based nursing practice and evidence-informed decision-making.

Result Analysis
Print
Save
E-mail