Impact of Laboratory Value Flowsheet in Electronic Health Record (EHR) Documentation Time

Research on the use of EHR is contradictory since it presents contradicting results regarding the time spent documenting. There is research that supports the use of electronic records as a tool to speed documentation;and research that found that it is time consuming. The purpose of this quantitative retrospective before-after project was to measure the impact of using the laboratory value flowsheet within the EHR on documentation time. The research question was: “Does the use of a laboratory value flowsheet in the EHR impact documentation time by primary care providers (PCPs)?” The theoretical framework utilized in this project was the Donabedian Model. The population in this research was the two PCPs in a small primary care clinic in the northwest of Puerto Rico. The sample was composed of all the encounters during the months of October 2019 and December 2019. The data was obtained through data mining and analyzed using SPSS 27. The evaluative outcome of this project is that there is a decrease in documentation time after implementation of the use of the laboratory value flowsheet in the EHR. However, patients per day increase therefore having an impact on the number of patients seen per day/week/month. The implications for clinical practice include the use of templates to improve workflow and documentation as well as decreasing documentation time while also increasing the number of patients seen per day. .

Documentation Concordance,Sharing and Utilization of Tea Germplasm Resources in Yunnan

In this paper,the research achievements and progress of Yunnan tea germplasm resource in past sixty years are systematically reviewed from the following aspects：exploration,collecting,conservation,protection,identification,evaluation and shared utilization.Simultaneously,the current problems and the suggestions about subsequent development of tea germplasm resources in Yunnan were discussed,including superior and rare germplasm collection,tea genetic diversity research,biotechnology utilization in tea germplasm innovation,super gene exploration and function,the construction of utilization platform,biological base of species and population conservation.

Smart Client与Office System整合应用研究

Smart Client既可以运用网络和Web Services存取各种外部系统的资源,又能使用本地计算机的所有资源,充分发挥客户端的运算能力,是一种全新软件模式。Office 2003 System中提供了一个Smart Document的功能,使用以文档为基础的方式扩充智能感知的XML标记功能,和用户交互更容易。微软还提出了一套InformationBridge Framework(IBF),来协助开发人员建立方案来连接Microsoft Office应用程序与Web服务。应用Microsoft的Smart Client,结合Office 2003 System以及.NET技术,构建一种部署成本低、维护容易、构架轻小、健壮的应用系统。

A Framework Based on the DAO and NFT in Blockchain for Electronic Document Sharing

In the information age,electronic documents(e-documents)have become a popular alternative to paper documents due to their lower costs,higher dissemination rates,and ease of knowledge sharing.However,digital copyright infringements occur frequently due to the ease of copying,which not only infringes on the rights of creators but also weakens their creative enthusiasm.Therefore,it is crucial to establish an e-document sharing system that enforces copyright protection.However,the existing centralized system has outstanding vulnerabilities,and the plagiarism detection algorithm used cannot fully detect the context,semantics,style,and other factors of the text.Digital watermark technology is only used as a means of infringement tracing.This paper proposes a decentralized framework for e-document sharing based on decentralized autonomous organization(DAO)and non-fungible token(NFT)in blockchain.The use of blockchain as a distributed credit base resolves the vulnerabilities inherent in traditional centralized systems.The e-document evaluation and plagiarism detection mechanisms based on the DAO model effectively address challenges in comprehensive text information checks,thereby promoting the enhancement of e-document quality.The mechanism for protecting and circulating e-document copyrights using NFT technology ensures effective safeguarding of users’e-document copyrights and facilitates e-document sharing.Moreover,recognizing the security issues within the DAO governance mechanism,we introduce an innovative optimization solution.Through experimentation,we validate the enhanced security of the optimized governance mechanism,reducing manipulation risks by up to 51%.Additionally,by utilizing evolutionary game analysis to deduce the equilibrium strategies of the framework,we discovered that adjusting the reward and penalty parameters of the incentive mechanism motivates creators to generate superior quality and unique e-documents,while evaluators are more likely to engage in assessments.

An explorative study on document type assignment of review articles in Web of Science,Scopus and journals’websites

Purpose:Accurately assigning the document type of review articles in citation index databases like Web of Science(WoS)and Scopus is important.This study aims to investigate the document type assignation of review articles in Web of Science,Scopus and Publisher’s websites on a large scale.Design/methodology/approach:27,616 papers from 160 journals from 10 review journal series indexed in SCI are analyzed.The document types of these papers labeled on journals’websites,and assigned by WoS and Scopus are retrieved and compared to determine the assigning accuracy and identify the possible reasons for wrongly assigning.For the document type labeled on the website,we further differentiate them into explicit review and implicit review based on whether the website directly indicates it is a review or not.Findings:Overall,WoS and Scopus performed similarly,with an average precision of about 99% and recall of about 80%.However,there were some differences between WoS and Scopus across different journal series and within the same journal series.The assigning accuracy of WoS and Scopus for implicit reviews dropped significantly,especially for Scopus.Research limitations:The document types we used as the gold standard were based on the journal websites’labeling which were not manually validated one by one.We only studied the labeling performance for review articles published during 2017-2018 in review journals.Whether this conclusion can be extended to review articles published in non-review journals and most current situation is not very clear.Practical implications:This study provides a reference for the accuracy of document type assigning of review articles in WoS and Scopus,and the identified pattern for assigning implicit reviews may be helpful to better labeling on websites,WoS and Scopus.Originality/value:This study investigated the assigning accuracy of document type of reviews and identified the some patterns of wrong assignments.

基于本体驱动的航空情报表格信息结构化研究

航空资料汇编是国际民航组织推荐的呈现各国航空信息的主要载体,其中以表格数据形式汇总了大量航空数据与航空运行限制信息。为实现航空汇编资料的智能查询,以及对航空资料汇编中静态数据的挖掘与利用,需要对航空汇编资料中的表格信息予以特征提取与结构化处理。将航空资料汇编中表格信息作为研究对象,提出了一种基于本体驱动的航空情报表格信息结构化抽取方法。首先构建航空情报领域信息的本体框架,实现对领域知识统一规范的描述;其次,利用Document AI对表格文档的布局结构进行研究与预处理,并利用随机森林算法与条件随机场模型进行特征实体提取验证与分析。实验结果表明,所提方法能够有效提取航空情报表格中的特征实体,为航空情报领域静态数据深入挖掘提供参考。

Hybrid Optimization Algorithm for Handwritten Document Enhancement

The Gannet Optimization Algorithm (GOA) and the Whale Optimization Algorithm (WOA) demonstrate strong performance;however, there remains room for improvement in convergence and practical applications. This study introduces a hybrid optimization algorithm, named the adaptive inertia weight whale optimization algorithm and gannet optimization algorithm (AIWGOA), which addresses challenges in enhancing handwritten documents. The hybrid strategy integrates the strengths of both algorithms, significantly enhancing their capabilities, whereas the adaptive parameter strategy mitigates the need for manual parameter setting. By amalgamating the hybrid strategy and parameter-adaptive approach, the Gannet Optimization Algorithm was refined to yield the AIWGOA. Through a performance analysis of the CEC2013 benchmark, the AIWGOA demonstrates notable advantages across various metrics. Subsequently, an evaluation index was employed to assess the enhanced handwritten documents and images, affirming the superior practical application of the AIWGOA compared with other algorithms.

Enhanced Topic-Aware Summarization Using Statistical Graph Neural Networks

The rapid expansion of online content and big data has precipitated an urgent need for efficient summarization techniques to swiftly comprehend vast textual documents without compromising their original integrity.Current approaches in Extractive Text Summarization(ETS)leverage the modeling of inter-sentence relationships,a task of paramount importance in producing coherent summaries.This study introduces an innovative model that integrates Graph Attention Networks(GATs)with Transformer-based Bidirectional Encoder Representa-tions from Transformers(BERT)and Latent Dirichlet Allocation(LDA),further enhanced by Term Frequency-Inverse Document Frequency(TF-IDF)values,to improve sentence selection by capturing comprehensive topical information.Our approach constructs a graph with nodes representing sentences,words,and topics,thereby elevating the interconnectivity and enabling a more refined understanding of text structures.This model is stretched to Multi-Document Summarization(MDS)from Single-Document Summarization,offering significant improvements over existing models such as THGS-GMM and Topic-GraphSum,as demonstrated by empirical evaluations on benchmark news datasets like Cable News Network(CNN)/Daily Mail(DM)and Multi-News.The results consistently demonstrate superior performance,showcasing the model’s robustness in handling complex summarization tasks across single and multi-document contexts.This research not only advances the integration of BERT and LDA within a GATs but also emphasizes our model’s capacity to effectively manage global information and adapt to diverse summarization challenges.

Multimodal Deep Neural Networks for Digitized Document Classification

As digital technologies have advanced more rapidly,the number of paper documents recently converted into a digital format has exponentially increased.To respond to the urgent need to categorize the growing number of digitized documents,the classification of digitized documents in real time has been identified as the primary goal of our study.A paper classification is the first stage in automating document control and efficient knowledge discovery with no or little human involvement.Artificial intelligence methods such as Deep Learning are now combined with segmentation to study and interpret those traits,which were not conceivable ten years ago.Deep learning aids in comprehending input patterns so that object classes may be predicted.The segmentation process divides the input image into separate segments for a more thorough image study.This study proposes a deep learning-enabled framework for automated document classification,which can be implemented in higher education.To further this goal,a dataset was developed that includes seven categories:Diplomas,Personal documents,Journal of Accounting of higher education diplomas,Service letters,Orders,Production orders,and Student orders.Subsequently,a deep learning model based on Conv2D layers is proposed for the document classification process.In the final part of this research,the proposed model is evaluated and compared with other machine-learning techniques.The results demonstrate that the proposed deep learning model shows high results in document categorization overtaking the other machine learning models by reaching 94.84%,94.79%,94.62%,94.43%,94.07%in accuracy,precision,recall,F-score,and AUC-ROC,respectively.The achieved results prove that the proposed deep model is acceptable to use in practice as an assistant to an office worker.

Pre-training transformer with dual-branch context content module for table detection in document images

Background Document images such as statistical reports and scientific journals are widely used in information technology.Accurate detection of table areas in document images is an essential prerequisite for tasks such as information extraction.However,because of the diversity in the shapes and sizes of tables,existing table detection methods adapted from general object detection algorithms,have not yet achieved satisfactory results.Incorrect detection results might lead to the loss of critical information.Methods Therefore,we propose a novel end-to-end trainable deep network combined with a self-supervised pretraining transformer for feature extraction to minimize incorrect detections.To better deal with table areas of different shapes and sizes,we added a dualbranch context content attention module(DCCAM)to high-dimensional features to extract context content information,thereby enhancing the network's ability to learn shape features.For feature fusion at different scales,we replaced the original 3×3 convolution with a multilayer residual module,which contains enhanced gradient flow information to improve the feature representation and extraction capability.Results We evaluated our method on public document datasets and compared it with previous methods,which achieved state-of-the-art results in terms of evaluation metrics such as recall and F1-score.https://github.com/Yong Z-Lee/TD-DCCAM.

Mechanism of imipenem-induced mental disorder: A meta-analysis

BACKGROUND Imipenem is a highly effective carbapenem antibiotic,which is widely used in the treatment of many serious bacterial infections.At the same time,it can also cause some adverse reactions,mental abnormalities are the most concerned central nervous system adverse reactions.Different patients respond differently to imipenem,and the effect of imipenem on psychiatric disorders is unclear.Therefore,meta-analysis summarizing the results of multiple previous studies can provide stronger evidence support for clinical guidelines to guide clinical rational use of imipenem to minimize risks.After reviewing the literature published between 2003 and 2017,seven controlled trials with a total of 550 patients were included,with 273 and 277 patients in the control and experimental groups,respectively.The sample size of the study ranged from a minimum of 30 cases to a maximum of 61 cases.Patients in the experimental group were treated with imipenem while the control group was treated with conventional drugs.Meta-analysis showed that the incidence of mental disorders in the experimental group was higher than that in the control group(odds ratio=3.66,95%confidence interval:1.11-12.11,P=0.030);however,there was no significant difference in the incidence of adverse reactions between the two groups(odds ratio=0.05,95%confidence interval:0.00 to 0.10,P=0.060).Funnel diagrams showed that the scattered points of each study were symmetrical and distributed in an inverted funnel shape;therefore,there was no publication bias.CONCLUSION Imipenem can cause mental disorders in patients.However,the low quality of the included literature may have affected the final results.Therefore,it is necessary to conduct a high-quality randomized controlled study with multiple samples to further confirm the mechanism of imipenem-induced mental disorders and provide effective guidance for clinical treatment.

Research and Analysis of Grammatical Error Correction Technology for Chinese Documents

With the widespread use of Chinese globally, the number of Chinese learners has been increasing, leading to various grammatical errors among beginners. Additionally, as domestic efforts to develop industrial information grow, electronic documents have also proliferated. When dealing with numerous electronic documents and texts written by Chinese beginners, manually written texts often contain hidden grammatical errors, posing a significant challenge to traditional manual proofreading. Correcting these grammatical errors is crucial to ensure fluency and readability. However, certain special types of text grammar or logical errors can have a huge impact, and manually proofreading a large number of texts individually is clearly impractical. Consequently, research on text error correction techniques has garnered significant attention in recent years. The advent and advancement of deep learning have paved the way for sequence-to-sequence learning methods to be extensively applied to the task of text error correction. This paper presents a comprehensive analysis of Chinese text grammar error correction technology, elaborates on its current research status, discusses existing problems, proposes preliminary solutions, and conducts experiments using judicial documents as an example. The aim is to provide a feasible research approach for Chinese text error correction technology.

生成标准PDF文件的方法

PDF为Portable Document Format的英文缩写,即可携带文件格式,是由Adobe公司在1993年用于文件交换所发展出的文件格式。它是一种跨操作系统平台的文件格式,可将文字、字体、图形、图像、色彩、版式及与印刷设备相关的参数等封装在一个文件中,在网络传输、打印和制版输出中保持页面元素不变,还可包含超文本链接、音频和视频等电子信息,集成度和安全可靠性都较高。

Assessing adequacy of emergency provider documentation among interhospital transferred patients with acute aortic dissection

BACKGROUND: Acute aortic dissection(AoD) is a hypertensive emergency often requiring the transfer of patients to higher care hospitals; thus, clinical care documentation and compliance with the Emergency Medical Treatment and Active Labor Act(EMTALA) is crucial. The study assessed emergency providers(EP) documentation of clinical care and EMTALA compliance among interhospital transferred AoD patients.METHODS: This retrospective study examined adult patients transferred directly from a referring emergency department(ED) to a quaternary academic center between January 1, 2011 and September 30, 2015. The primary outcome was the percentage of records with adequate documentation of clinical care(ADoCC). The secondary outcome was the percentage of records with adequate documentation of EMTALA compliance(ADoEMTALA). RESULTS: There were 563 electronically identified patients with 287 included in the final analysis. One hundred and five(36.6%) patients had ADoCC while 166(57.8%) patients had ADoEMTALA. Patients with inadequate documentation of EMTALA(IDoEMTALA) were associated with a higher likelihood of not meeting the American Heart Association(AHA) ED Departure SBP guideline(OR 1.8, 95% CI 1.03–3.2, P=0.04). Male gender, handwritten type of documentation, and transport by air were associated with an increased risk of inadequate documentation of clinical care(IDoCC), while receiving continuous infusion was associated with higher risk of IDoEMTALA.CONCLUSION: Documentation of clinical care and EMTALA compliance by Emergency Providers is poor. Inadequate EMTALA documentation was associated with a higher likelihood of patients not meeting the AHA ED Departure SBP guideline. Therefore, Emergency Providers should thoroughly document clinical care and EMTALA compliance among this critically ill group before transfer.

On the study of ecosystem health:state of the art

Ecosystem health is a newly proposed concept that sets new goals for environmental management. Its definition, assessment indicators, and assessment methods are reviewed in this paper. Literature shows that the definitions and the assessment indicators cover a wide range of ecosystem health, and they differ in terms of researchers different scientific background. It is concluded that the concept of ecosystem health cannot be defined or understood simply in biological or ethical or aesthetic or historical terms and the assessment should be based on applying several indicators simultaneously to get overall picture of the health or integrity state of an ecosystem.

A METHODOLOGY FOR INTEGRATED MANAGEMENT SYSTEM

ISO 9000 certification has been considered as an effective competitive edge for manufacturing industries. In Hong Kong, the ISO 9000 certification has been widely adopted and extended to service industries for the sole purpose of enhancing corporate image. However, complaints have been voiced by many certified organizations about increasing paperwork, non direct labour and excessive resources channelled into non core business. This paper is to propound the integrated management system, a single management system focusing on business purpose yet being flexible enough to integrate the requirements of various management standards such as the ISO 9000, ISO 14000 and future ISO 18000. This paper describes how the proposed system could be implemented to achieve the desired business results.

Evaluation of a Mobile Station Electronic Health Record on Documentation Compliance and Nurses’ Attitudes

Electronic Health Record (EHR) Systems have been adopted by healthcare organizations for documentation of patient care. Often these information systems are embedded in mobile nurse stations. As part of assessing the impact of this technology it is important to determine the effect it has on charting compliance and user acceptance. Data were collected at a medical center in Taiwan in two stages. The first stage involved use of a 28-item medical review tool to measure charting compliance in 99 charts before and after implementation of the EHR system. In stage two, a survey was conducted with 709 nurse users to determine their level of mobile EHR acceptance 3 months after this documentation technology was initiated. Results demonstrated that EHR significantly improved documentation compliance in standardized data entry format (name, date, time), abbreviation, content correction/revision, patient care needs, and care goals. Analysis of data from the five categories of a user acceptance survey revealed the following results (based on a 4-point Likert scale): patient care (2.92), nursing efficiency (2.78), education and training (2.98), usability (2.61), and usage benefits (2.87). The study concluded that use of mobile nurse stations with EHR can improve documentation compliance and that although frequent system downtime needs improvement, nurses generally have positive attitudes toward this technology application.

Assessing an Educational Program to Improve Documentation and Reduce Pain in Hospitalized Patients

Few experimental studies have evaluated the efficacy of continuing educational programs aimed at the improvement of nurses’ pain-management skills. This study assessed whether a standardized educational program aimed at nurses could increase the use of the Numeric Rating Scale-11 in both documenting and reducing postoperative pain-intensity levels in hospitalized surgical patients. The study had a quasi-experimental pre- and post-intervention design. Data were collected from records of surgical patients prior to and after the standardized educational program was completed. There were no significant differences between pre- and post-intervention groups in terms of either pain-documentation frequency or pain-intensity level. The study showed no increase in the frequency of postoperative pain documentation and no reduction of surgical patients’ postoperative pain-intensity level. This finding indicates that the standardized educational program on postoperative pain management was insufficient to bring about changes in clinical practice.

A Multi-Interface Remote Monitoring and Control System Architecture Based on Embedded Server

This paper presents a multi-interface embedded server architecture for remote real-time monitoring system and distributed monitoring applications. In the scheme,an embedded microprocessor( LPC3250 from NXP) is chosen as the CPU of the embedded server with a linux operation system( OS) environment. The embedded server provides multiple interfaces for supporting various application scenarios. The whole network is based on local area network and adopts the Browser / Server( B / S) model. The monitoring and control node is as a browser endpoint and the remote node with an embedded server is as a server endpoint. Users can easily acquire various sensors information through writing Internet protocol address of remote node on the computer browser. Compared with client / server( C / S) mode,B / S model needs less maintain and can be applicable to large user group. In addition,a simple network management protocol( SNMP) is used for management of devices in Internet protocol( IP) networks. The results of the demonstration experiment show that the proposed system gives good support to manage the network from different user terminals and allows the users to better interact with the ambient environment.

Hadoop-Based Similarity Computation System for Composed Documents

There exist a large number of composed documents in universities in the teaching process. Most of them are required to check the similarity for validation. A kind of similarity computation system is constructed for composed documents with images and text information. Firstly, each document is split and outputs two parts as images and text information. Then, these documents are compared by computing the similarities of images and text contents independently. Through Hadoop system, the text contents are easily and quickly separated. Experimental results show that the proposed system is efficient and practical.