Progress of clinical decision support systems in stroke nursing care

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.

Optimizing Vaccine Access: A Web-Based Scheduling System with Geo-Tagging Integration and Decision Support for Local Health Centers

The system created aims to produce an online vaccination appointment scheduling system with geo-tagging integration and a decision-support mechanism for neighborhood health clinics. With a decision support mechanism that suggests the essential vaccines based on their account details, it is made to meet the unique vaccination needs of each patient. The system includes immunizations that are accessible locally, and patients and midwives can manage their own corresponding information through personal accounts. Viewers of websites can visualize the distribution of vaccines by purok thanks to geotagging. The Agile Scrum Methodology was modified by the researchers for early delivery, change flexibility, and continual system improvement in order to accomplish the study’s main goal. In order to assess the system’s acceptability in terms of functional adequacy, performance efficiency, compatibility, usability, reliability, security, maintainability, and portability, it was designed in accordance with the ISO 25010 Product Software Quality Standards. Following the assessment, the system was given an average total weighted mean score of 4.62, which represents a verbal interpretation of “strongly agree”. This score demonstrates that the evaluators were in agreement that the system met the requirements of ISO 25010 for Product Software Quality Standards.

Modelling an Efficient Clinical Decision Support System for Heart Disease Prediction Using Learning and Optimization Approaches

With the worldwide analysis,heart disease is considered a significant threat and extensively increases the mortality rate.Thus,the investigators mitigate to predict the occurrence of heart disease in an earlier stage using the design of a better Clinical Decision Support System(CDSS).Generally,CDSS is used to predict the individuals’heart disease and periodically update the condition of the patients.This research proposes a novel heart disease prediction system with CDSS composed of a clustering model for noise removal to predict and eliminate outliers.Here,the Synthetic Over-sampling prediction model is integrated with the cluster concept to balance the training data and the Adaboost classifier model is used to predict heart disease.Then,the optimization is achieved using the Adam Optimizer(AO)model with the publicly available dataset known as the Stalog dataset.This flowis used to construct the model,and the evaluation is done with various prevailing approaches like Decision tree,Random Forest,Logistic Regression,Naive Bayes and so on.The statistical analysis is done with theWilcoxon rank-summethod for extracting the p-value of the model.The observed results show that the proposed model outperforms the various existing approaches and attains efficient prediction accuracy.This model helps physicians make better decisions during complex conditions and diagnose the disease at an earlier stage.Thus,the earlier treatment process helps to eliminate the death rate.Here,simulation is done withMATLAB 2016b,and metrics like accuracy,precision-recall,F-measure,p-value,ROC are analyzed to show the significance of the model.

Bibliometrics analysis of clinical decision support systems research in nursing

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.

Clinical decision support for drug related events: Moving towards better prevention

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.

Advanced decision support for complex clinical decisions

A Physician’s decision-making skills are directly related to the patient’s positive outcomes. Therefore, a wealth of medical knowledge and clinical experience are key assets for a physician to have. The goal here is to use historical clinical data and relationships processed by Artificial Intelligence (AI) techniques to aid physicians in their decision making process. Presenting this information in a Clinical Decision Support System (CDSS) is an effective means to consolidate decision results. The CDSS provides a large number of medical support functions to help clinicians make the most reasonable diagnosis and choose the best treatment measures. Initial results have shown great promise in accurately predicting Fibrosis Stage in Hepatitis patients. Utilizing this tool could mitigate the need for some liver biopsies in the more than 170 million Hepatitis patients worldwide. The prototype is extendable to accommodate additional techniques (for example genetic algorithms and logistics regression) and additional medical domain solutions (for example HIV/AIDS).

Nursing decision support system:application in electronic health records

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.

A clinical decision support system using rough set theory and machine learning for disease prediction

Objective Technological advances have led to drastic changes in daily life,and particularly healthcare,while traditional diagnosis methods are being replaced by technology-oriented models and paper-based patient health-care records with digital files.Using the latest technology and data mining techniques,we aimed to develop an automated clinical decision support system(CDSS),to improve patient prognoses and healthcare delivery.Our proposed approach placed a strong emphasis on improvements that meet patient,parent,and physician expec-tations.We developed a flexible framework to identify hepatitis,dermatological conditions,hepatic disease,and autism in adults and provide results to patients as recommendations.The novelty of this CDSS lies in its inte-gration of rough set theory(RST)and machine learning(ML)techniques to improve clinical decision-making accuracy and effectiveness.Methods Data were collected through various web-based resources.Standard preprocessing techniques were applied to encode categorical features,conduct min-max scaling,and remove null and duplicate entries.The most prevalent feature in the class and standard deviation were used to fill missing categorical and continuous feature values,respectively.A rough set approach was applied as feature selection,to remove highly redundant and irrelevant elements.Then,various ML techniques,including K nearest neighbors(KNN),linear support vector machine(LSVM),radial basis function support vector machine(RBF SVM),decision tree(DT),random forest(RF),and Naive Bayes(NB),were employed to analyze four publicly available benchmark medical datasets of different types from the UCI repository and Kaggle.The model was implemented in Python,and various validity metrics,including precision,recall,F1-score,and root mean square error(RMSE),applied to measure its performance.Results Features were selected using an RST approach and examined by RF analysis and important features of hepatitis,dermatology conditions,hepatic disease,and autism determined by RST and RF exhibited 92.85%,90.90%,100%,and 80%similarity,respectively.Selected features were stored as electronic health records and various ML classifiers,such as KNN,LSVM,RBF SVM,DT,RF,and NB,applied to classify patients with hepatitis,dermatology conditions,hepatic disease,and autism.In the last phase,the performance of proposed classifiers was compared with that of existing state-of-the-art methods,using various validity measures.RF was found to be the best approach for adult screening of:hepatitis with accuracy 88.66%,precision 74.46%,recall 75.17%,F1-score 74.81%,and RMSE value 0.244;dermatology conditions with accuracy 97.29%,precision 96.96%,recall 96.96%,F1-score 96.96%,and RMSE value,0.173;hepatic disease,with accuracy 91.58%,precision 81.76%,recall 81.82%,F1-Score 81.79%,and RMSE value 0.193;and autism,with accuracy 100%,precision 100%,recall 100%,F1-score 100%,and RMSE value 0.064.Conclusion The overall performance of our proposed framework may suggest that it could assist medical experts in more accurately identifying and diagnosing patients with hepatitis,dermatology conditions,hepatic disease,and autism.

基于新型CDSS辅助的病案首页填报及质控

基于人工智能技术的新型临床决策支持系统,可通过构建设计编码规则与知识库系统、优化质控逻辑与质控指标体系,对医疗过程进行智能监测和管控,实时监控病案首页的填写情况与质量,强化医疗过程质量稽查,对存在质量缺陷与不符质控规则的诊疗流程进行预警提醒,有助于实现更有效的病案管理与医疗质控。

CDSS辅助医疗质量管理的设计与实践探索

在医疗决策支持的基础上,进行基于医疗管理的专家意见收集与验证,建立适合本地的规则并转化为医疗管理知识库。探索运用于医疗管理的临床决策支持系统功能设计,使其具有与临床业务、医疗管理业务高度融合、有效协助诊疗质量管理等特点,该尝试在医院取得了良好的应用效果,有助于提升医疗机构工作效率和医疗服务质量,一定程度上可改善该系统在医院应用中存在的问题。本文探讨医疗质量管理的实际运用中的具体问题,为解决医疗质量智慧化管理提供新思路。

Computerized decision support in adult and pediatric critical care

Computerized decision support(CDS) is the most advanced form of clinical decision support available and has evolved with innovative technologies to provide meaningful assistance to medical professionals. Critical care clinicians are in unique environments where vast amounts of data are collected on individual patients, and where expedient and accurate decisions are paramount to the delivery of quality healthcare. Many CDS tools are in use today among adult and pediatric intensive care units as diagnostic aides, safety alerts, computerized protocols, and automated recommendations for management. Some CDS use have significantly decreased adverse events and improved costs when carefully implemented and properly operated. CDS tools integrated into electronic health records are also valuable to researchers providing rapid identification of eligible patients, streamlining data-gathering and analysis, and providing cohorts for study of rare and chronic diseases through data-warehousing. Although the need for human judgment in the daily care of critically ill patients has limited the study and realization ofmeaningful improvements in overall patient outcomes, CDS tools continue to evolve and integrate into the daily workflow of clinicians, and will likely provide advancements over time. Through novel technologies, CDS tools have vast potential for progression and will significantly impact the field of critical care and clinical research in the future.

A Case-Finding Clinical Decision Support System to Identify Subjects with Chronic Obstructive Pulmonary Disease Based on Public Health Data

Chronic obstructive pulmonary disease(COPD)is a serious chronic respiratory disease.Improving the ability to identify patients with COPD in primary medical institutions is important to prevent and treat the disease.With the continuous development of medical digitization,the application of big data informatization in the medical and health fields has become possible.Recently,applying innovative technologies such as big data analysis,machine learning,and artificial intelligence-assisted decision-making in the medical field has become an interdisciplinary research hotspot.Based on the identification and diagnosis of COPD in the high-risk population,this study proposes a convenient and effective clinical decision support system to help identify patients with COPD in primary health institutions.The results of the preliminary experiments show that the proposed method is convenient and effective compared with the existing methods.

A Decision Support Model for Predicting Avoidable Re-Hospitalization of Breast Cancer Patients in Kenyatta National Hospital

This study aimed to develop a clinical Decision Support Model (DSM) which is software that provides physicians and other healthcare stakeholders with patient-specific assessments and recommendation in aiding clinical decision-making while discharging Breast cancer patient since the diagnostics and discharge problem is often overwhelming for a clinician to process at the point of care or in urgent situations. The model incorporates Breast cancer patient-specific data that are well-structured having been attained from a prestudy’s administered questionnaires and current evidence-based guidelines. Obtained dataset of the prestudy’s questionnaires is processed via data mining techniques to generate an optimal clinical decision tree classifier model which serves physicians in enhancing their decision-making process while discharging a breast cancer patient on basic cognitive processes involved in medical thinking hence new, better-formed, and superior outcomes. The model also improves the quality of assessments by constructing predictive discharging models from code attributes enabling timely detection of deterioration in the quality of health of a breast cancer patient upon discharge. The outcome of implementing this study is a decision support model that bridges the gap occasioned by less informed clinical Breast cancer discharge that is based merely on experts’ opinions which is insufficiently reinforced for better treatment outcomes. The reinforced discharge decision for better treatment outcomes is through timely deployment of the decision support model to work hand in hand with the expertise in deriving an integrative discharge decision and has been an agreed strategy to eliminate the foreseeable deteriorating quality of health for a discharged breast cancer patients and surging rates of mortality blamed on mistrusted discharge decisions. In this paper, we will discuss breast cancer clinical knowledge, data mining techniques, the classifying model accuracy, and the Python web-based decision support model that predicts avoidable re-hospitalization of a breast cancer patient through an informed clinical discharging support model.

Framework for a Computer-Aided Treatment Prediction (CATP) System for Breast Cancer

This study offers a framework for a breast cancer computer-aided treat-ment prediction(CATP)system.The rising death rate among women due to breast cancer is a worldwide health concern that can only be addressed by early diagno-sis and frequent screening.Mammography has been the most utilized breast ima-ging technique to date.Radiologists have begun to use computer-aided detection and diagnosis(CAD)systems to improve the accuracy of breast cancer diagnosis by minimizing human errors.Despite the progress of artificial intelligence(AI)in the medical field,this study indicates that systems that can anticipate a treatment plan once a patient has been diagnosed with cancer are few and not widely used.Having such a system will assist clinicians in determining the optimal treatment plan and avoid exposing a patient to unnecessary hazardous treatment that wastes a significant amount of money.To develop the prediction model,data from 336,525 patients from the SEER dataset were split into training(80%),and testing(20%)sets.Decision Trees,Random Forest,XGBoost,and CatBoost are utilized with feature importance to build the treatment prediction model.The best overall Area Under the Curve(AUC)achieved was 0.91 using Random Forest on the SEER dataset.

To scan or not to scan:Use of transient elastography in an integrated health system

BACKGROUND Non-invasive tests,such as Fibrosis-4 index and transient elastography(com-monly FibroScan),are utilized in clinical pathways to risk stratify and diagnose non-alcoholic fatty liver disease(NAFLD).In 2018,a clinical decision support tool(CDST)was implemented to guide primary care providers(PCPs)on use of FibroScan for NAFLD.AIM To analyze how this CDST impacted health care utilization and patient outcomes.METHODS We performed a retrospective review of adults who had FibroScan for NAFLD indication from January 2015 to December 2017(pre-CDST)or January 2018 to December 2020(post-CDST).Outcomes included FibroScan result,laboratory tests,imaging studies,specialty referral,patient morbidity and mortality.RESULTS We identified 958 patients who had FibroScan,115 before and 843 after the CDST was implemented.The percentage of FibroScans ordered by PCPs increased from 33%to 67.1%.The percentage of patients diagnosed with early F1 fibrosis,on a scale from F0 to F4,increased from 7.8%to 14.2%.Those diagnosed with ad-vanced F4 fibrosis decreased from 28.7%to 16.5%.There were fewer laboratory tests,imaging studies and biopsy after the CDST was implemented.Though there were more specialty referrals placed after the CDST was implemented,multivariate analysis revealed that healthcare utilization aligned with fibrosis score,whereby patients with more advanced disease had more referrals.Very few patients were hospitalized or died.CONCLUSION This CDST empowered PCPs to diagnose and manage patients with NAFLD with appropriate allocation of care towards patients with more advanced disease.

面向心律失常疾病的临床决策支持系统交互性研究与实现

目的/意义针对当前临床决策支持系统使用过程中存在的问题,提出界面设计原则并将其应用于心律失常疾病临床决策支持系统建设中。方法/过程从交互设计角度,针对可解释性、时效性、可用性、相关性、尊重性和循证性6个维度,提出界面设计原则。选取心律失常疾病决策支持作为临床场景,设计临床决策支持系统交互界面原型,阐述界面功能、信息功能、交互功能设计与实现过程。结果/结论本研究提出的界面设计原则可有效缓解6类问题,经论证具有应用于心律失常疾病临床决策支持原型系统的可行性以及可泛化性,可用于指导多种疾病辅助诊疗工具的交互设计。

ICU患者身体约束决策支持系统的研发与应用

目的:研发ICU患者身体约束决策支持系统并评价其临床应用效果,为护士进行患者身体约束决策提供便利。方法:成立研发小组,构建ICU患者身体约束影响因素预测模型并进行验证,以此为基础开发ICU患者身体约束决策支持系统并投入使用。选取2022年8月至2023年3月江苏省某三级甲等医院ICU的764例患者为研究对象,将患者随机分为干预组(n=366)和对照组(n=398),对照组护士实施基于经验性评估的常规身体约束流程,干预组护士实施基于ICU患者身体约束决策支持系统的身体约束流程,比较两组患者的身体约束率、谵妄发生率、非计划性拔管发生率;同时验证ICU患者身体约束决策支持系统与约束决策轮的一致性。结果:使用ICU患者身体约束决策支持系统后,干预组患者的约束率为24.9%,谵妄发生率为2.2%,均低于对照组(P<0.05);两组非计划性拔管发生率差异无统计学意义(P>0.05)。ICU患者身体约束决策支持系统与约束决策轮Kappa一致性检验结果为0.853(P<0.001),显示结果一致性较好。结论:ICU患者身体约束决策支持系统可以应用于临床并辅助护士决策,在有效降低身体约束率及谵妄发生率的同时不会增加非计划性拔管发生率。

社区血液透析中心临床决策支持系统的全流程构建与应用

目的:设计一种临床辅助决策支持系统用于社区血液透析中心的管理,并评价其应用效果。方法:构建临床辅助决策支持系统,该系统具备实时数据采集、智能预警、远程指导等技术特点。采用系统可用性问卷(PSSUQ)评价该系统的可用性及满意度,并比较实施该系统前后两组患者血液透析敏感质量指标。结果:在系统的评价中,PSSUQ单项题目平均得分及99%置信区间均低于平均数3.5分的分界点,单项题目平均得分和4个维度得分均低于PSSUQ最佳基准;实施后患者血液透析敏感指标均有不同程度的改善。结论:临床辅助决策系统具有较高的可用性及满意度,能够显著提升患者透析质量,减少不良事件的发生,具有良好的应用前景。

中西医结合临床决策支持系统的关键技术研究

临床决策支持系统日益成为医务人员的重要工具,但目前多数落地应用集中在合理用药方面,因为在诊断辅助、治疗安全和疗效分析3个重要环节依然存在诸多技术难点,缺乏系统性和工程化解决方案。本文基于中医智能系统性的发展和临床中西医结合的现实需求,探讨中西医结合临床决策支持系统(CDSS)实现闭环的关键技术,即中西医多源异构数据分析与交叉验证;中医知识图谱和算法模型的智能辅助辨证论治;疑难杂症治疗的智能推荐和用药模型构建;疗效分析的语义理解、隐私安全与数据保护机制等。通过部分工程实例,提出中西医结合CDSS关键技术解决方案,为卫生信息管理和研究人员、企业级产品开发和临床实践应用提供参考。