黑龙江省区域医疗影像数据中心项目的设计与建设

目的构建以哈尔滨医科大学附属第四医院为中心、以黑龙江垦区所辖医院为服务范围的医疗信息交换与共享平台,并在平台基础上拓展特色应用,实践医疗服务协同,建设成为数字化医疗区域示范工程。方法研究并建立数据模型,对异构数据进行标准化转换,实现共享、交换与互操作。解决数据中心的管理和实施,包括数据并发访问、存储、可靠性、容灾、监控、归档、分析等。结果使二级医院和基层医疗机构能通过客户端系统直接预约哈尔滨医科大学附属第四医院的检查项目,并将本地影像上传至哈尔滨医科大学附属第四医院医学影像中心,由哈尔滨医科大学附属第四医院医生集中阅片诊断。二级医院和基层医疗机构可查看诊断结果,并通过WEB浏览器调阅存储在区域医学影像中心的影像资料。结论该平台研究不仅具有很强的现实意义,且体现了先进性、前瞻性、可操作性和潜在的社会经济价值。     

数字化医院与超声科数字化建设

数字化医院建设是一个庞大的系统工程,是医院管理和疾病诊治由经验管理、经验医学到规范管理、标准化管理向数字化医学的转变,数字化医院是医院现代化的重要标志之一,也是超声科数字化建设的重要平台,超声影像科必须做好数字化建设,为医院现代化做出贡献.本文探讨了数字化医院建设的策略、意义以及建设PACS系统的意义、原则和策略.     

关于公立医院开展岗位管理的初步设想

医院岗位管理制度是医院实现由身份管理向岗位管理转变的重要改革.本文就我区公立医院在岗位设置与管理中存在的问题,建立新的用人制度,促进医院人力资源建设等问题进行思考,为医院人力资源管理机制提出粗浅的思考.     

加强医院管理提高服务能力

随着医学的发展，建设现代化军队医院，必须坚持科学的发展观，与时俱进，加快医院建设步伐，适应新形势挑战。医院如何通过良好的管理，提高竞争力，关键在与管理能否有所突破，有所创新。现就新形势下医院管理提出几点粗浅的看法。     

医院信息科在信息化建设中的作用及要处理好的几个关系

医院信息化已经成为医院医疗活动必不可少的支撑和手段,医院信息科作为医院信息化建设和管理的重要职能部门,承担着医院信息化建设一系列重要职责和任务.信息科的作用发挥的程度将直接影响医院信息化效果.认真探讨医院信息科在信息化建设中的作用与职责及要处理好的关系,对于促进医院信息化建设具有很强的指导作用.     

医院信息系统的建设与应用

本文介绍了医院信息系统的建设过程与应用,阐述了医院信息化管理的重要性。     

医院图书馆文献资源建设与数字图书馆的信息组织

医院图书馆的文献资源建设是数字图书馆建设的关键和核心,而数字化信息组织与管理是数字资源建设的重要组成部分.本文主要论述了医院图书馆的文献资源建设与数字图书馆信息组织的目标和原则及基本内容.     

浅谈医院局域网的安全与管理

在医院信息化建设规模不断扩大的同时,网络安全与管理日趋成为大家关心的重点。本文主要从软、硬件系统及病毒防治等方面阐述医院局域网的安全与管理。     

医院信息系统的组织与管理

医院信息系统的开发与建设是一个庞大的系统工程，而使一个医院信息系统能够正常的运行则是组织管理的关键。本文主要介绍四川省肿瘤医院计算机网络运行管理的体制与管理方法。     

浅谈医院信息网络管理中心管理职能

医院信息化建设不断发展,现代信息技术在医院管理、医疗护理、科研、教学等各方面广泛应用的环境下,在众多的职能部门中应运而生了信息网络管理中心。本文对信息网络管理中心的管理与服务职能进行阐述。进而说明在医院管理中,其不可取代的重要作用。     

医院临床数据分析系统的构建

目的 通过数据集成平台(data integration platform,DIP),整合医院不同业务系统的数据,构建临床数据共享库,为疾病的治疗及医院的管理提供支持.方法 采用J2EE技术以及ICE中间件通信技术实现DIP的数据通信,完成数据仓库建设工作.采用开源工具JasperReport,实现数据分析的展现.结果 数据集成是实现数据分析的前提,保障了医疗数据的来源真实可靠.结论 数据集成平台和数据分析系统结合的一体化流程,减少了临床和科研人员的二次录入工作,节约了数据处理的时间.数据挖掘的结果为医院的医疗以及管理提供了帮助.     

SMOTE算法在不平衡数据中的应用

目的临床数据在分析时多存在不平衡性,即阳性数据和阴性数据不相等,如果不加以预处理会使分析结果产生偏倚.处理有偏性数据的方法多,但多数方法存在过拟合或丢失数据等缺点.方法本文介绍了SMOTE算法的原理和R语言具体实现方式,并用SMOTE算法处理真实临床数据作为应用实例.结果原始数据良恶性比率为1/3,经过 SMOTE算法处理后,良恶性比率为1.结论 SMOTE算法可对不平衡数据进行有效纠偏.     

Data integration and analysis for circadian medicine

Data integration, data sharing, and standardized analyses are important enablers for data-driven medical research. Circadian medicine is an emerging field with a particularly high need for coordinated and systematic collaboration between researchers from different disciplines. Datasets in circadian medicine are multimodal, ranging from molecular circadian profiles and clinical parameters to physiological measurements and data obtained from (wearable) sensors or reported by patients. Uniquely, data spanning both the time dimension and the spatial dimension (across tissues) are needed to obtain a holistic view of the circadian system. The study of human rhythms in the context of circadian medicine has to confront the heterogeneity of clock properties within and across subjects and our inability to repeatedly obtain relevant biosamples from one subject. This requires informatics solutions for integrating and visualizing relevant data types at various temporal resolutions ranging from milliseconds and seconds to minutes and several hours. Associated challenges range from a lack of standards that can be used to represent all required data in a common interoperable form, to challenges related to data storage, to the need to perform transformations for integrated visualizations, and to privacy issues. The downstream analysis of circadian rhythms requires specialized approaches for the identification, characterization, and discrimination of rhythms. We conclude that circadian medicine research provides an ideal environment for developing innovative methods to address challenges related to the collection, integration, visualization, and analysis of multimodal multidimensional biomedical data.     

Data quality of a wearable vital signs monitor in the pre-hospital and emergency departments for enhancing prediction of needs for life-saving interventions in trauma patients

Abstract

This study was designed to investigate the quality of data in the pre-hospital and emergency departments when using a wearable vital signs monitor and examine the efficacy of a combined model of standard vital signs and respective data quality indices (DQIs) for predicting the need for life-saving interventions (LSIs) in trauma patients. It was hypothesised that prediction of needs for LSIs in trauma patients is associated with data quality. Also, a model utilizing vital signs and DQIs to predict the needs for LSIs would be able to outperform models using vital signs alone. Data from 104 pre-hospital trauma patients transported by helicopter were analysed, including means and standard deviations of continuous vital signs, related DQIs and Glasgow coma scale (GCS) scores for LSI and non-LSI patient groups. DQIs involved percentages of valid measurements and mean deviation ratios. Various multivariate logistic regression models for predicting LSI needs were also obtained and compared through receiver-operating characteristic (ROC) curves. Demographics of patients were not statistically different between LSI and non-LSI patient groups. In addition, ROC curves demonstrated better prediction of LSI needs in patients using heart rate and DQIs (area under the curve [AUC] of 0.86) than using heart rate alone (AUC of 0.73). Likewise, ROC curves demonstrated better prediction using heart rate, total GCS score and DQIs (AUC of 0.99) than using heart rate and total GCS score (AUC of 0.92). AUCs were statistically different (p?<?0.05). This study showed that data quality could be used in addition to continuous vital signs for predicting the need for LSIs in trauma patients. Importantly, trauma systems should incorporate processes to regulate data quality of physiologic data in the pre-hospital and emergency departments. By doing so, data quality could be improved and lead to better prediction of needs for LSIs in trauma patients.     

An instrument for recording and printing heart rate data

An instrument has been designed which will print maximum, minimum, and mean heart rate (HR) data for 10-sec sample periods. The basic components are three detector circuits which sample and hold voltage levels proportional to the values measured, a digital voltmeter for “reading” these values, and a solenoid-operated adding machine to print out HR data in beats per minute. This apparatus greatly reduces the time required for transcribing cardiotachometer tracings into digital form for statistical analysis.     

磁共振图像k空间数据分析与处理PC平台的开发与应用

目的：开发基于普通PC的软件包,实现磁共振图像k空间数据的读取分析与多种后处理。方法：根据磁共振原始数据的存储规则和文件结构,开发读取磁共振图象k空间数据的PC平台,读取文件头相关信息（图像层数,回波采样点数,相位编码步数,数据偏移量等关系后续图像重建、分析、处理的重点参数必须读出备用,其它辅助参数可不读出,若将来用到,可添加读出代码即可）,开辟内存空间,线性k空间数据读出到内存,根据前面参数提供的信息进行各层面数据重组。各种序列参数放在hd结构中,数据放在data矩阵中。对数据矩阵进行各种处理。结果：在该平台上,可成功进行磁共振原始k空间数据的读取、显示、分析和重建处理及各种后处理操作（图象重建;幅度、实部、虚部、相位的分别显示;锐化、模糊等各种处理）。结论：软件包成功实现了各项预先设计功能,利用该方法开发磁共振图像k空间数据分析与处理PC平台切实可行。     

建立国家卫生与健康管理大数据平台的总体设想

在医疗卫生与健康领域,各类医疗机构、医疗信息平台、数字化医疗仪器、智能健康电子产品、可穿戴个人健康设备、健康信息平台会生成海量的健康医疗信息,形成健康医疗大数据.健康医疗大数据的发展与应用不仅可以完善我国医疗卫生服务体系,提升医疗服务质量和管理水平,而且可促进健康产业的发展,对国家经济发展具有现实的带动意义和长远的战略意义.对健康医疗大数据的基本特点进行了介绍,针对我国健康医疗大数据应用中存在的问题和挑战进行了分析.为提高我国健康管理的信息化水平和管理效率,提出一个能够融合医疗与健康信息的、覆盖我国国民的全方位、全周期的健康医疗数据平台——国家卫生与健康管理大数据平台,对建立该平台的总体设想进行了描述,并对该平台的建设方式、运营方式、数据来源及平台功能进行了介绍.     

基于openEHR的临床医疗数据仓库构建方法

医疗信息的复杂性和动态性给医疗信息系统带来巨大的挑战,openEHR规范的两层建模思想提高医疗信息系统的灵活性,适应医疗信息需求的变化。但面临查询数据量巨大、查询条件复杂多变的个性化操作需求时,基于openEHR两层建模方法实现医疗信息系统查询性能较低,这主要与底层数据存储模型有关。数据仓库多维数据模型具有高性能查询的优点,通过建立满足个性化需求的数据集市,可以加快数据查询速度。由于基于openEHR规范两层建模方法实现的医疗信息系统的特殊性,传统的数据仓库构建方法使得用户的参与性较低并且费时费力。针对这一问题,以openEHR规范的两层建模思想为基础,提出模板到动态数据仓库多维数据模型的映射方法,实现多维数据模型的用户可配置,利用映射路径加快ETL的实施,为医疗行业提供一种可操控、可扩展并真实反映用户需求的数据仓库构建方法。对该方法进行性能上的验证,结果表明,在10 861 522条数据中查询329条数据时,基于该方法构建的数据集市是基于openEHR两层建模方法生成的数据库的5.6倍,是基于传统方法构建的数据集市的0.97倍。     

A software package for the calculation and statistical analysis of 51Cr-release assays

A software package has been developed to calculate, store, and statistically analyze the results of 51Cr-release assays. It is written in Applesoft BASIC, is 'user friendly', and contains error trapping routines to catch most common mistakes. The greeting program contains a master menu from which one of the following programs may be called up: (1) 51Cr-release calculation, (2) statistical analysis, or (3) file manager. These programs give the user the capability to rapidly reduce cpm data to % specific release and statistically analyze the results using randomized design analysis of variance (ANOVA) and Turkey's honestly significant difference (HSD) test.     

The Use of Electronic Data Capture Tools in Clinical Trials: Web-Survey of 259 Canadian Trials

Background

Electronic data capture (EDC) tools provide automated support for data collection, reporting, query resolution, randomization, and validation, among other features, for clinical trials. There is a trend toward greater adoption of EDC tools in clinical trials, but there is also uncertainty about how many trials are actually using this technology in practice. A systematic review of EDC adoption surveys conducted up to 2007 concluded that only 20% of trials are using EDC systems, but previous surveys had weaknesses.

Objectives

Our primary objective was to estimate the proportion of phase II/III/IV Canadian clinical trials that used an EDC system in 2006 and 2007. The secondary objectives were to investigate the factors that can have an impact on adoption and to develop a scale to assess the extent of sophistication of EDC systems.

Methods

We conducted a Web survey to estimate the proportion of trials that were using an EDC system. The survey was sent to the Canadian site coordinators for 331 trials. We also developed and validated a scale using Guttman scaling to assess the extent of sophistication of EDC systems. Trials using EDC were compared by the level of sophistication of their systems.

Results

We had a 78.2% response rate (259/331) for the survey. It is estimated that 41% (95% CI 37.5%-44%) of clinical trials were using an EDC system. Trials funded by academic institutions, government, and foundations were less likely to use an EDC system compared to those sponsored by industry. Also, larger trials tended to be more likely to adopt EDC. The EDC sophistication scale had six levels and a coefficient of reproducibility of 0.901 (P< .001) and a coefficient of scalability of 0.79. There was no difference in sophistication based on the funding source, but pediatric trials were likely to use a more sophisticated EDC system.

Conclusion

The adoption of EDC systems in clinical trials in Canada is higher than the literature indicated: a large proportion of clinical trials in Canada use some form of automated data capture system. To inform future adoption, research should gather stronger evidence on the costs and benefits of using different EDC systems.