Objective:Artificial intelligence(AI)has a big impact on healthcare now and in the future.Nurses play an important role in the medical field and will benefit greatly from this technology.AI-Enabled Clinical Decision S...Objective:Artificial intelligence(AI)has a big impact on healthcare now and in the future.Nurses play an important role in the medical field and will benefit greatly from this technology.AI-Enabled Clinical Decision Support Systems have received a great deal of attention recently.Bibliometric analysis can offer an objective,systematic,and comprehensive analysis of a specific field with a vast background.However,no bibliometric analysis has investigated AI-enabled clinical decision support systems research in nursing.The purpose of research to determine the characteristics of articles about the global performance and development of AI-enabled clinical decision support systems research in nursing.Methods:In this study,the bibliometric approach was used to estimate the searched data on clinical decision support systems research in nursing from 2009 to 2022,and we also utilized CiteSpace and VOSviewer software to build visualizing maps to assess the contribution of different journals,authors,et al.,as well as to identify research hot spots and promising future trends in this research field.Result:From 2009 to 2022,a total of 2,159 publications were retrieved.The number of publications and citations on AI-enabled clinical decision support systems research in nursing has increased obvious ly in recent years.However,they are understudied in the field of nursing and there is a compelling need to develop more high-quality research.Conclusion:AI-Enabled Nursing Decision Support System use in clinical practice is still in its early stages.These analyses and results hope to provide useful information and references for future research directions for researchers and nursing practitioners who use AI-enabled clinical decision support systems.展开更多
Stroke is characterized by high incidence,high recurrence,high disability,and high morbidity and mortality in China,resulting in a heavy social and clinical burden.A clinical decision support system,as an intelli-gent...Stroke is characterized by high incidence,high recurrence,high disability,and high morbidity and mortality in China,resulting in a heavy social and clinical burden.A clinical decision support system,as an intelli-gent computer system,can assist nurses in decision-mak-ing to collect information quickly,make the most suitable personalized decisions for patients,and improve nurses’decision-making judgment and quality of care.Promoting the development and application of decision support sys-tems in stroke nursing significantly enhances the nursing staff’s work quality and patients’prognosis.Therefore,this paper reviews the research progress of domestic and international clinical decision support systems in stroke nursing care to provide other researchers with specific research directions for developing and applying decision support systems in stroke nursing care.展开更多
Clinical decision support(CDS) systems with automated alerts integrated into electronic medical records demonstrate efficacy for detecting medication errors(ME) and adverse drug events(ADEs). Critically ill patients a...Clinical decision support(CDS) systems with automated alerts integrated into electronic medical records demonstrate efficacy for detecting medication errors(ME) and adverse drug events(ADEs). Critically ill patients are at increased risk for ME, ADEs and serious negative outcomes related to these events. Capitalizing on CDS to detect ME and prevent adverse drug related events has the potential to improve patient outcomes. The key to an effective medication safety surveillance system incorporating CDS is advancing the signals for alerts by using trajectory analyses to predict clinical events, instead of waiting for these events to occur. Additionally, incorporating cutting-edge biomarkers into alert knowledge in an effort to identify the need to adjust medication therapy portending harm will advance the current state of CDS. CDS can be taken a step further to identify drug related physiological events, which are less commonly included in surveillance systems. Predictive models for adverse events that combine patient factors with laboratory values and biomarkers are being established and these models can be the foundation for individualized CDS alerts to prevent impending ADEs.展开更多
The clinical decision support system makes electronic health records(EHRs)structured,intelligent,and knowledgeable.The nursing decision support system(NDSS)is based on clinical nursing guidelines and nursing process t...The clinical decision support system makes electronic health records(EHRs)structured,intelligent,and knowledgeable.The nursing decision support system(NDSS)is based on clinical nursing guidelines and nursing process to provide intelligent suggestions and reminders.The impact on nurses’work is mainly in shortening the recording time,improving the quality of nursing diagnosis,reducing the incidence of nursing risk events,and so on.However,there is no authoritative standard for the NDSS at home and abroad.This review introduces development and challenges of EHRs and recommends the application of the NDSS in EHRs,namely the nursing assessment decision support system,the nursing diagnostic decision support system,and the nursing care planning decision support system(including nursing intervene),hoping to provide a new thought and method to structure impeccable EHRs.展开更多
目的探索脑血管病临床决策支持系统(clinical decision support system,CDSS)对卒中医疗服务质量关键绩效指标改进是否具有积极作用。方法回顾性连续纳入首都医科大学附属北京天坛医院血管神经病学2病区应用脑血管病CDSS前(2020年1—11...目的探索脑血管病临床决策支持系统(clinical decision support system,CDSS)对卒中医疗服务质量关键绩效指标改进是否具有积极作用。方法回顾性连续纳入首都医科大学附属北京天坛医院血管神经病学2病区应用脑血管病CDSS前(2020年1—11月)住院治疗的缺血性卒中患者作为对照组,应用脑血管病CDSS后(2021年1—11月)收治的缺血性卒中患者作为干预组,比较两组患者的基线特征和缺血性卒中医疗服务质量关键绩效指标,评估脑血管病CDSS对卒中医疗服务质量的影响。结果本研究共纳入1331例患者,其中对照组651例,干预组680例。对照组平均年龄为(71.7±11.8)岁,男性490例(75.3%);干预组平均年龄(72.3±10.2)岁,男性498例(73.2%)。缺血性卒中医疗服务质量关键绩效指标中,干预组入院48 h内不能行走患者进行深静脉血栓预防率(86.3%vs.65.0%,P<0.01)、出院时患者抗栓治疗率(98.1%vs.96.2%,P=0.03)、出院时合并心房颤动的患者抗凝治疗率(70.1%vs.44.2%,P<0.01)均高于对照组。结论脑血管病CDSS有助于改善缺血性卒中患者的医疗服务质量关键绩效指标。展开更多
目的/意义整体分析国内临床决策支持系统(clinical decision support system,CDSS)领域专利权人技术情况,为CDSS相关技术研究提供借鉴。方法/过程从公开趋势、专利分布、技术领域分布等方面对CDSS公开专利数据进行分析,划分专利权人类别...目的/意义整体分析国内临床决策支持系统(clinical decision support system,CDSS)领域专利权人技术情况,为CDSS相关技术研究提供借鉴。方法/过程从公开趋势、专利分布、技术领域分布等方面对CDSS公开专利数据进行分析,划分专利权人类别,依靠词频和词间向量分析其技术关注情况,基于活跃专利权人分析技术关联和专利独占相关情况。结果/结论CDSS专利公开数量整体呈增长趋势,该领域在国内尚未形成技术垄断,政府部门和研究机构应关注可能产生的金融风险。展开更多
In the era of advanced machine learning techniques,the development of accurate predictive models for complex medical conditions,such as thyroid cancer,has shown remarkable progress.Accurate predictivemodels for thyroi...In the era of advanced machine learning techniques,the development of accurate predictive models for complex medical conditions,such as thyroid cancer,has shown remarkable progress.Accurate predictivemodels for thyroid cancer enhance early detection,improve resource allocation,and reduce overtreatment.However,the widespread adoption of these models in clinical practice demands predictive performance along with interpretability and transparency.This paper proposes a novel association-rule based feature-integratedmachine learning model which shows better classification and prediction accuracy than present state-of-the-artmodels.Our study also focuses on the application of SHapley Additive exPlanations(SHAP)values as a powerful tool for explaining thyroid cancer prediction models.In the proposed method,the association-rule based feature integration framework identifies frequently occurring attribute combinations in the dataset.The original dataset is used in trainingmachine learning models,and further used in generating SHAP values fromthesemodels.In the next phase,the dataset is integrated with the dominant feature sets identified through association-rule based analysis.This new integrated dataset is used in re-training the machine learning models.The new SHAP values generated from these models help in validating the contributions of feature sets in predicting malignancy.The conventional machine learning models lack interpretability,which can hinder their integration into clinical decision-making systems.In this study,the SHAP values are introduced along with association-rule based feature integration as a comprehensive framework for understanding the contributions of feature sets inmodelling the predictions.The study discusses the importance of reliable predictive models for early diagnosis of thyroid cancer,and a validation framework of explainability.The proposed model shows an accuracy of 93.48%.Performance metrics such as precision,recall,F1-score,and the area under the receiver operating characteristic(AUROC)are also higher than the baseline models.The results of the proposed model help us identify the dominant feature sets that impact thyroid cancer classification and prediction.The features{calcification}and{shape}consistently emerged as the top-ranked features associated with thyroid malignancy,in both association-rule based interestingnessmetric values and SHAPmethods.The paper highlights the potential of the rule-based integrated models with SHAP in bridging the gap between the machine learning predictions and the interpretability of this prediction which is required for real-world medical applications.展开更多
医学的实践性、疾病的个体性导致误诊率居高不下,而基于人工智能和医学大数据的临床决策支持系统(clinical decision support system,CDSS)能在疾病诊断时为医生提供决策支持,成为解决此类问题的一个重要手段,且现已取得一定的成效。不...医学的实践性、疾病的个体性导致误诊率居高不下,而基于人工智能和医学大数据的临床决策支持系统(clinical decision support system,CDSS)能在疾病诊断时为医生提供决策支持,成为解决此类问题的一个重要手段,且现已取得一定的成效。不过,尽管CDSS在提高医疗决策的准确性和效率方面具有潜在优势,但其在实施过程中也存在一系列的问题,这些问题可能影响CDSS的可靠性、可用性和安全性。本文对诊断类CDSS的应用现状、面临的挑战和未来发展趋势进行总结分析,以期对我国CDSS向智能化和知识化方向发展提供参考。展开更多
基金Lan-Fang Qin was supported by National Innovation and Entrepreneurship Training Program for College Students(2022KYCX69)Rui Wang was supported by the Nursing Subject(Zhejiang Province"13th Five-Year Plan"Characteristic Specialty Construction Project)under Grant(JY30001)Chong-Bin Liu supported by the grants from National Natural Science Foundation of Zhejiang Province,No.LY21H260005 and No.2017290-40.
文摘Objective:Artificial intelligence(AI)has a big impact on healthcare now and in the future.Nurses play an important role in the medical field and will benefit greatly from this technology.AI-Enabled Clinical Decision Support Systems have received a great deal of attention recently.Bibliometric analysis can offer an objective,systematic,and comprehensive analysis of a specific field with a vast background.However,no bibliometric analysis has investigated AI-enabled clinical decision support systems research in nursing.The purpose of research to determine the characteristics of articles about the global performance and development of AI-enabled clinical decision support systems research in nursing.Methods:In this study,the bibliometric approach was used to estimate the searched data on clinical decision support systems research in nursing from 2009 to 2022,and we also utilized CiteSpace and VOSviewer software to build visualizing maps to assess the contribution of different journals,authors,et al.,as well as to identify research hot spots and promising future trends in this research field.Result:From 2009 to 2022,a total of 2,159 publications were retrieved.The number of publications and citations on AI-enabled clinical decision support systems research in nursing has increased obvious ly in recent years.However,they are understudied in the field of nursing and there is a compelling need to develop more high-quality research.Conclusion:AI-Enabled Nursing Decision Support System use in clinical practice is still in its early stages.These analyses and results hope to provide useful information and references for future research directions for researchers and nursing practitioners who use AI-enabled clinical decision support systems.
文摘Stroke is characterized by high incidence,high recurrence,high disability,and high morbidity and mortality in China,resulting in a heavy social and clinical burden.A clinical decision support system,as an intelli-gent computer system,can assist nurses in decision-mak-ing to collect information quickly,make the most suitable personalized decisions for patients,and improve nurses’decision-making judgment and quality of care.Promoting the development and application of decision support sys-tems in stroke nursing significantly enhances the nursing staff’s work quality and patients’prognosis.Therefore,this paper reviews the research progress of domestic and international clinical decision support systems in stroke nursing care to provide other researchers with specific research directions for developing and applying decision support systems in stroke nursing care.
基金Supported by The Agency for Healthcare Research and Quality,No.R18HS02420-01
文摘Clinical decision support(CDS) systems with automated alerts integrated into electronic medical records demonstrate efficacy for detecting medication errors(ME) and adverse drug events(ADEs). Critically ill patients are at increased risk for ME, ADEs and serious negative outcomes related to these events. Capitalizing on CDS to detect ME and prevent adverse drug related events has the potential to improve patient outcomes. The key to an effective medication safety surveillance system incorporating CDS is advancing the signals for alerts by using trajectory analyses to predict clinical events, instead of waiting for these events to occur. Additionally, incorporating cutting-edge biomarkers into alert knowledge in an effort to identify the need to adjust medication therapy portending harm will advance the current state of CDS. CDS can be taken a step further to identify drug related physiological events, which are less commonly included in surveillance systems. Predictive models for adverse events that combine patient factors with laboratory values and biomarkers are being established and these models can be the foundation for individualized CDS alerts to prevent impending ADEs.
基金This project was supported by the Development and application of nursing decision support system based on artificial intelligence(No.2019ZD006).
文摘The clinical decision support system makes electronic health records(EHRs)structured,intelligent,and knowledgeable.The nursing decision support system(NDSS)is based on clinical nursing guidelines and nursing process to provide intelligent suggestions and reminders.The impact on nurses’work is mainly in shortening the recording time,improving the quality of nursing diagnosis,reducing the incidence of nursing risk events,and so on.However,there is no authoritative standard for the NDSS at home and abroad.This review introduces development and challenges of EHRs and recommends the application of the NDSS in EHRs,namely the nursing assessment decision support system,the nursing diagnostic decision support system,and the nursing care planning decision support system(including nursing intervene),hoping to provide a new thought and method to structure impeccable EHRs.
文摘目的探索脑血管病临床决策支持系统(clinical decision support system,CDSS)对卒中医疗服务质量关键绩效指标改进是否具有积极作用。方法回顾性连续纳入首都医科大学附属北京天坛医院血管神经病学2病区应用脑血管病CDSS前(2020年1—11月)住院治疗的缺血性卒中患者作为对照组,应用脑血管病CDSS后(2021年1—11月)收治的缺血性卒中患者作为干预组,比较两组患者的基线特征和缺血性卒中医疗服务质量关键绩效指标,评估脑血管病CDSS对卒中医疗服务质量的影响。结果本研究共纳入1331例患者,其中对照组651例,干预组680例。对照组平均年龄为(71.7±11.8)岁,男性490例(75.3%);干预组平均年龄(72.3±10.2)岁,男性498例(73.2%)。缺血性卒中医疗服务质量关键绩效指标中,干预组入院48 h内不能行走患者进行深静脉血栓预防率(86.3%vs.65.0%,P<0.01)、出院时患者抗栓治疗率(98.1%vs.96.2%,P=0.03)、出院时合并心房颤动的患者抗凝治疗率(70.1%vs.44.2%,P<0.01)均高于对照组。结论脑血管病CDSS有助于改善缺血性卒中患者的医疗服务质量关键绩效指标。
文摘目的/意义整体分析国内临床决策支持系统(clinical decision support system,CDSS)领域专利权人技术情况,为CDSS相关技术研究提供借鉴。方法/过程从公开趋势、专利分布、技术领域分布等方面对CDSS公开专利数据进行分析,划分专利权人类别,依靠词频和词间向量分析其技术关注情况,基于活跃专利权人分析技术关联和专利独占相关情况。结果/结论CDSS专利公开数量整体呈增长趋势,该领域在国内尚未形成技术垄断,政府部门和研究机构应关注可能产生的金融风险。
文摘In the era of advanced machine learning techniques,the development of accurate predictive models for complex medical conditions,such as thyroid cancer,has shown remarkable progress.Accurate predictivemodels for thyroid cancer enhance early detection,improve resource allocation,and reduce overtreatment.However,the widespread adoption of these models in clinical practice demands predictive performance along with interpretability and transparency.This paper proposes a novel association-rule based feature-integratedmachine learning model which shows better classification and prediction accuracy than present state-of-the-artmodels.Our study also focuses on the application of SHapley Additive exPlanations(SHAP)values as a powerful tool for explaining thyroid cancer prediction models.In the proposed method,the association-rule based feature integration framework identifies frequently occurring attribute combinations in the dataset.The original dataset is used in trainingmachine learning models,and further used in generating SHAP values fromthesemodels.In the next phase,the dataset is integrated with the dominant feature sets identified through association-rule based analysis.This new integrated dataset is used in re-training the machine learning models.The new SHAP values generated from these models help in validating the contributions of feature sets in predicting malignancy.The conventional machine learning models lack interpretability,which can hinder their integration into clinical decision-making systems.In this study,the SHAP values are introduced along with association-rule based feature integration as a comprehensive framework for understanding the contributions of feature sets inmodelling the predictions.The study discusses the importance of reliable predictive models for early diagnosis of thyroid cancer,and a validation framework of explainability.The proposed model shows an accuracy of 93.48%.Performance metrics such as precision,recall,F1-score,and the area under the receiver operating characteristic(AUROC)are also higher than the baseline models.The results of the proposed model help us identify the dominant feature sets that impact thyroid cancer classification and prediction.The features{calcification}and{shape}consistently emerged as the top-ranked features associated with thyroid malignancy,in both association-rule based interestingnessmetric values and SHAPmethods.The paper highlights the potential of the rule-based integrated models with SHAP in bridging the gap between the machine learning predictions and the interpretability of this prediction which is required for real-world medical applications.
文摘医学的实践性、疾病的个体性导致误诊率居高不下,而基于人工智能和医学大数据的临床决策支持系统(clinical decision support system,CDSS)能在疾病诊断时为医生提供决策支持,成为解决此类问题的一个重要手段,且现已取得一定的成效。不过,尽管CDSS在提高医疗决策的准确性和效率方面具有潜在优势,但其在实施过程中也存在一系列的问题,这些问题可能影响CDSS的可靠性、可用性和安全性。本文对诊断类CDSS的应用现状、面临的挑战和未来发展趋势进行总结分析,以期对我国CDSS向智能化和知识化方向发展提供参考。