电子病历在临床应用中的问题探讨

电子病历在医院信息化发展中发挥了非常重要的作用。本文分析了医院电子病历的应用现状,探讨电子病历目前存在的问题,并提出相关解决方案及对策,借此推动医院电子病历的应用,确保电子病历信息的有效性、安全性和合法性。     

基于语义的电子病历智能全文检索

目的:研究检索结果更精确的电子病历检索算法,更好地促进电子病历在科研辅助方面的开发利用。方法:以基于关键字的全文检索技术为基础,通过对电子病历档案库的前期分析,自动学习相关领域词汇的语义关系并建立本体库,在此基础上对电子病历进行全文语义检索。结果:该技术强调关键词的语义性,检索结果更精确更智能,更符合人类的思维习惯。结论:使用基于语义的电子病历智能全文检索系统,可以有效地改善检索结果,提高电子病历的利用率。     

人工智能电子病历质量控制技术应用效果研究

目的:评价人工智能技术应用于电子病历(EMR)质量控制系统的效果,为推进人工智能EMR质量控制技术提供理论依据。方法:选取2022年5-10月医院EMR的运营数据,其中5月份为测试数据,从医院层面、医生层面、科室层面和核心医疗制度层面采用人工智能监控系统对6-10月份EMR质量控制的数据进行对比研究,检验人工智能EMR质量控制技术的应用效果。结果:医院EMR书写违规情况由6月份的48%降至10月份的37%,甲级病历比率由3.4%增长到5.6%;违规数量最高的前10个科室6-10月份的EMR书写违规数量均显著下降,差异有统计学意义(F=16.15,P<0.05);违规数量最高的前10名医生6-10月份的EMR书写违规数量均显著下降,差异有统计学意义(F=5.77,P<0.05);违规数量最高的前10名核心医疗制度6-10月份的违规数量均显著下降,差异有统计学意义(F=10.46,P<0.05)。结论:人工智能EMR质量控制技术能够降低病历缺陷率,提高病历质量。     

电子病历在HIS中的应用与分析

本文针对新版电子病历在我院信息系统中的应用进行了分析和展望。     

新技术在电子病历中的应用

本文介绍新的计算机技术在电子病历上的应用     

新版电子病历系统实施与体会

目的探讨新版电子病历系统在军队医院的实施情况。方法针对新版电子病历系统的特点,提出合理的实施计划,对系统进行多次测试,对全院医师、护士进行培训,以点带面,层层推进。结果顺利实施该系统,达到预期效果。结论保证系统顺利实施,领导重视是关键,全面测试是前提,注重培训是基础。     

电子病历在数字化医院中的应用

电子病历是数字化医院建设的核心,本文主要阐述电子病历在数字化医院建设中,提高医务人员工作效能,加强医院质量管理、信息化建设,和谐医疗单位-患者-上级管理部门关系。     

结构化电子病历的应用探讨

通过对比8000份纸质病历和结构化电子病历,从病案完成的速度和质量以及科研查询和质量等方面分析评价结构化电子病历的使用价值,讨论结构化电子病历的优势及实施过程中存在的问题。     

指纹识别技术在电子病历系统中的应用

在对指纹识别算法进行研究分析的基础上,提出了一个在电子病历系统中综合应用指纹识别技术的方案,能有效提高电子病历系统的安全性和医院的工作效率。     

电子病历系统在应用中的问题和对策

根据我院自身业务特点,本文重点阐述了我院电子病历（EMR）系统自建立以来,在使用过程中遇到的电子病历模板无法调用等问题及解决方法,保证电子病历系统正常工作。     

人工智能技术应用于电子病历质控的研究与思考

针对电子病历质控存在的问题,阐述了机器学习、自然语言处理（NLP）、图像识别、专家系统等人工智能核心技术。论述了质控专家与人工智能质控的关系、人工智能质控目标、人工智能质控隐私保护等问题,以期为提高电子病历质控水平提供参考。     

湖北省二级医院病案科(室)建设现状调查

目的了解湖北省二级医院病案科(室)建设现状,为加强病案科(室)建设提供依据。方法通过问卷法调查湖北省二级医院病案科(室)的组织管理、人员配置、科研培训、信息化管理及科室制度建设现状。结果 54.44%的病案科(室)归医务处管理,病案工作人员业务用房面积大于50m2病案科室仅占20.11%,病案管理者专业背景以护理(28.36%)和临床医学(16.36%)专业居多,10项病案科室制度的覆盖率均在70%以上。结论病案室制度建设较完善,但科室隶属关系不合理,基础设施严重不足,从业人员结构不合理,信息化管理水平参差不齐等。     

几所军队医院电子病历应用情况调查分析

本文通过对5所不同类型军队三甲医院应用电子病历的水平、层次、存在问题、影响因素、认识和评价的调查分析，提出医院建设电子病历的要点。     

应用PCA和BP神经网络的医学彩色图像语义标注

目的：针对目前计算机辅助诊断的需要以及较难实现语义检索和较少涉及医学彩色图像分类的问题,设计医学内窥镜图像语义标注分类器。方法：基于C#/C＋＋编程语言、Windows 7、.NET、Visual-Studio（VS2008）平台,通过CBIR特征提取,主成分分析（principal component analysis,PCA）降维处理,然后通过BP神经网络训练进行分类,开发应用分类器。结果：分类器对7类医学彩色内窥镜图像分类准确性达到80%,而训练时间只有几秒或几十毫秒。结论：主成分分析和BP神经网络的结合使用,克服了低级特征和高级语义间的语义鸿沟,降维处理大大减小了系统存储量,提高了训练速度,获得了更好的标注结果。     

Evaluation of Electronic Ambulatory Care Data for Use in the Influenza-like Illness Surveillance Network (ILINet)

Objective

To determine if a syndromic influenza-like illness (ILI) definition previously validated for emergency department (ED) data accurately identified ILI visits in electronic ambulatory care data.

Introduction

During summer 2012, Washington State Department of Health (WA DOH) surveyed ILINet providers and found that more than half either utilize their electronic medical record system (EMRS) to gather and report weekly ILINet data, or intend to implement queries to do so in the future. There are a variety of EMRS being used state-wide, and providers that currently utilize these systems to report ILINet data apply a wide range of methods to query their data. There exists great interest in the evaluation of ambulatory care data within the context of Meaningful Use and little research is published in this area. WA DOH sought to evaluate electronic data from WA outpatient clinic networks in order to determine if a syndromic ILI definition previously validated for emergency department (ED) data accurately identified ILI visits in electronic ambulatory care data.

Methods

Public Health Seattle King County (PHSKC) receives electronic health data from the University of Washington Physicians Network (UWPN), comprised of ten outpatient clinics, on an automated basis. Data are sent daily for all outpatient visits that occurred the previous day and include clinic name, visit date and time, patient age, sex, zip code, chief complaint and diagnoses, and both a visit and patient key. Outpatient data from August 2007 through August 2012 were queried for ILI visits using the syndromic category for ILI previously validated for ED syndromic surveillance data: (1) ICD codes for influenza or mention of “flu” in chief complaint or diagnosis, or (2) a chief complaint or diagnosis of fever plus cough, or (3) a chief complaint or diagnosis of fever plus sore throat.Using this definition, we assessed the correlation between the proportion of visits for ILI in the UWPN data and number and percentage of positive influenza laboratory tests reported by the University of Washington (UW) Virology Laboratory. We plan to apply this methodology to evaluate outpatient data from an additional clinic network, with statewide locations, and present these findings.

Results

The median number of weekly visits captured in the data was 6,622. Three clinics were excluded from further analyses due to insufficient data, leaving seven clinics remaining in the dataset (median number of weekly visits: 6,167). Overall, the proportion of ILI visits in the UWPN data strongly correlated with the number and percentage of positive influenza tests reported by the UW Laboratory during August 2007 through August 2012 (correlation coefficients 0.85 and 0.77, respectively). The correlation between proportion of ILI visits and number positive influenza tests among individual clinics ranged from 0.62 — 0.83. Overall, the proportion of ILI visits among the age category 5 to 24 years most strongly correlated with number positive influenza tests (correlation coefficient: 0.86).

Conclusions

During August 2007 through August 2012, the percentage of ILI visits detected in UWPN data using a previously validated definition for ILI in ED syndromic surveillance data strongly correlated with influenza activity in the community. Based on these findings, data from the UWPN network will be incorporated into ILINet during the 2012–2013 Influenza season. Findings from our analysis support the validity of using syndromic ambulatory data for ILI surveillance. Furthermore, we plan to use these results to formulate guidance for ILINet providers who want to utilize EMRS for weekly ILINet reporting.Open in a separate windowProportion of ILI visits within electronic clinic network data and number positive influenza tests, August 2007 – August 2012, Washington State