基于OpenSDE的XML技术在电子病历结构化中的应用

介绍了XML技术和opensDE两种结构化方法的定义及特点,并展示了基于opensDE的XML技术在电子病历结构化中的具体应用过程,最后讨论了基于OpenSDE的XML技术在电子病历结构化方面的优点以及下一步研究发展方向.     

电子病历结构化引导式输入模式的设计和实现

本研究将电子病历中结构化输入模式与自由化输入模式结合,设计并实现了一种电子病历结构化引导式输入模式.该模式不仅能引导病历书写者完成书写过程,杜绝病历内容漏项和缺项,还能训练和强化严谨的临床思维模式,促进低年资医师的培养.     

Structured Data Entry in the Electronic Medical Record: Perspectives of Pediatric Specialty Physicians and Surgeons

The Epic electronic health record (EHR) platform supports structured data entry systems (SDES), which allow developers, with input from users, to create highly customized patient-record templates in order to maximize data completeness and to standardize structure. There are many potential advantages of using discrete data fields in the EHR to capture data for secondary analysis and epidemiological research, but direct data acquisition from clinicians remains one of the largest obstacles to leveraging the EHR for secondary use. Physician resistance to SDES is multifactorial. A 35-item questionnaire based on Unified Theory of Acceptance and Use of Technology, was used to measure attitudes, facilitation, and potential incentives for adopting SDES for clinical documentation among 25 pediatric specialty physicians and surgeons. Statistical analysis included chi-square for categorical data as well as independent sample t-tests and analysis of variance for continuous variables. Mean scores of the nine constructs demonstrated primarily positive physician attitudes toward SDES, while the surgeons were neutral. Those under 40 were more likely to respond that facilitating conditions for structured entry existed as compared to the two older age groups (p = .02). Pediatric surgeons were significantly less positive than specialty physicians about SDES effects on Performance (p = .01) and the effect of Social Influence (p = .02); but in more agreement that use of forms was voluntary (p = .02). Attitudinal differences likely reflect medical training, clinical practice workflows, and division specific practices. Identified resistance indicate efforts to increase SDES adoption should be discipline-targeted rather than a uniform approach.     

电子病历开放式结构化数据采集与临床医学知识表达策略研究

临床电子病历开放式结构化数据录入的可用性和临床知识表达策略的研究，是临床描述信息的结构化支持程序的使用途径。使用OpenSDE建立特定领域医学概念树，基于概念树设计临床数据录入界面。临床数据项目的相关范围可以依据特定专门领域概念树进行调整，OpenSDE使临床描述性信息结构化，并使临床医疗和科研充分利用这些数据成为可能。     

专家系统与结构化数据录入系统结合应用的可能性探讨

回顾了结构化数据录入与专家系统两者的研究进展，讨论了两者所面临的局限。分析了两者结合应用的可能性及其优势。认为两者结合应用有可能具有互补优势，从而促进两者在实践中扩大应用范围。     

A Case for Manual Entry of Structured,Coded Laboratory Data from Multiple Sources into an Ambulatory Electronic Health Record

Laboratory results provide necessary information for the management of ambulatory patients. To realize the benefits of an electronic health record (EHR) and coded laboratory data (e.g., decision support and improved data access and display), results from laboratories that are external to the health care enterprise need to be integrated with internal results. We describe the development and clinical impact of integrating external results into the EHR at Intermountain Health Care (IHC). During 2004, over 14,000 external laboratory results for 128 liver transplant patients were added to the EHR. The results were used to generate computerized alerts that assisted clinicians with managing laboratory tests in the ambulatory setting. The external results were sent from 85 different facilities and can now be viewed in the EHR integrated with IHC results. We encountered regulatory, logistic, economic, and data quality issues that should be of interest to others developing similar applications.     

A Data Preparation Methodology in Data Mining Applied to Mortality Population Databases

It is known that the data preparation phase is the most time consuming in the data mining process, using up to 50 % or up to 70 % of the total project time. Currently, data mining methodologies are of general purpose and one of their limitations is that they do not provide a guide about what particular task to develop in a specific domain. This paper shows a new data preparation methodology oriented to the epidemiological domain in which we have identified two sets of tasks: General Data Preparation and Specific Data Preparation. For both sets, the Cross-Industry Standard Process for Data Mining (CRISP-DM) is adopted as a guideline. The main contribution of our methodology is fourteen specialized tasks concerning such domain. To validate the proposed methodology, we developed a data mining system and the entire process was applied to real mortality databases. The results were encouraging because it was observed that the use of the methodology reduced some of the time consuming tasks and the data mining system showed findings of unknown and potentially useful patterns for the public health services in Mexico.     

Assessing Data Quality in Manual Entry of Ventilator Settings

Objective

To evaluate the data quality of ventilator settings recorded by respiratory therapists using a computer charting application and assess the impact of incorrect data on computerized ventilator management protocols.

Design

An analysis of 29,054 charting events gathered over 12 months from 678 ventilated patients (1,736 ventilator days) in four intensive care units at a tertiary care hospital.

Measurements

Ten ventilator settings were examined, including fraction of inspired oxygen (Fio ₂), positive end-expiratory pressure (PEEP), tidal volume, respiratory rate, peak inspiratory flow, and pressure support. Respiratory therapists entered values for each setting approximately every two hours using a computer charting application. Manually entered values were compared with data acquired automatically from ventilators using an implementation of the ISO/IEEE 11073 Medical Information Bus (MIB). Data quality was assessed by measuring the percentage of time that the two sources matched. Charting delay, defined as the interval between data observation and data entry, also was measured.

Results

The percentage of time that settings matched ranged from 99.0% (PEEP) to 75.9% (low tidal volume alarm setting). The average charting delay for each charting event was 6.1 minutes, including an average of 1.8 minutes spent entering data in the charting application. In 559 (3.9%) of 14,263 suggestions generated by computerized ventilator management protocols, one or more manually charted setting values did not match the MIB data.

Conclusion

Even at institutions where manual charting of ventilator settings is performed well, automatic data collection can eliminate delays, improve charting efficiency, and reduce errors caused by incorrect data.For patients treated with continuous positive-pressure ventilation in the intensive care unit (ICU), numerous ventilator settings are adjusted to provide appropriate oxygenation and ventilation and to facilitate weaning. Nurses or respiratory therapists periodically observe settings on the ventilator that control oxygen, flow, volume, and pressures, and chart these values either on paper or into electronic patient records. Variation exists in how and when such values are observed and documented. ^1–3 For ICU staff, access to timely and accurate ventilator settings is essential for making treatment decisions and for maintaining situation awareness. Computerized decision support tools, such as ventilator management protocols, require accurate and timely data to generate instructions for caregivers and to effect changes in patient care. ^4,5 We hypothesized that the ventilator settings entered manually into a computer charting application would not always match the settings automatically acquired from ventilators at 5-second intervals. To test this hypothesis, we measured the percentage of time that the manually charted settings matched 5-second ventilator values. To assess the possible impact of incorrect data on computerized ventilator management protocols, we measured the number of suggestions generated by computerized ventilator management protocols where one or more manually charted setting values did not match the automatically acquired data.     

混合法用于基因芯片数据的分类分析

目的 研究广义似然比检验(GLRT)与支持向量机(SVM)相结合的混合法在基因芯片表达数据分类研究中的应用效果.方法 结合 Golub研究的白血病基因表达数据,利用GLRT寻找白血病表达水平存在显著性差异的基因,再使用SVM对白血病进行分类.结果 基于GLRT鉴别得到的特征基因有很强的代表性,SVM分类的效果明显优于其他方法.结论 混合法在解决数据量大、维数高、样本量小、非线性问题中具有很强的优势,可以有效的用于基因芯片表达数据的分类研究.     

医疗大数据应用于真实世界研究现状及展望

基于文献调研,分析医疗大数据应用于真实世界研究的开展情况,阐述大数据时代真实世界研究优势,包括外部真实性高、目标人群广泛、证据整体性强、获取证据高效等方面,提出医疗大数据应用于真实世界研究在基础架构和具体开展两方面面临的挑战。     

数据流技术用于ICU监测数据处理的研究

ICU是临床科室中信息量最大且最复杂的科室。如何最合理地解决ICU临床信息系统冗余问题,最大限度地采集有价值的病人信息,剔除无效信息,是面临的最严峻的挑战之一。数据流技术的应用为解决这一难题提供了有力的手段。在被采集的数据被存储到数据库以前。运用数据流技术对浩繁的流数据进行预处理。剔除干扰数据并做合理修补,根据临床设定的反映病人病情不同危重程度的分级规则,以及数据反映病人危重程度的权重,来触发数据存储到数据库时不同的记录密度。这在一定程度上实现了patjent—dependem的数据采集方式,从而最有效地记录高质量的病人信息,同时保持较高的系统资源利用效益。     

Data Mining to Support Simulation Modeling of Patient Flow in Hospitals

Spiraling health care costs in the United States are driving institutions to continually address the challenge of optimizing the use of scarce resources. One of the first steps towards optimizing resources is to utilize capacity effectively. For hospital capacity planning problems such as allocation of inpatient beds, computer simulation is often the method of choice. One of the more difficult aspects of using simulation models for such studies is the creation of a manageable set of patient types to include in the model. The objective of this paper is to demonstrate the potential of using data mining techniques, specifically clustering techniques such as K-means, to help guide the development of patient type definitions for purposes of building computer simulation or analytical models of patient flow in hospitals. Using data from a hospital in the Midwest this study brings forth several important issues that researchers need to address when applying clustering techniques in general and specifically to hospital data.     

视觉显示终端数据输入作业对机体影响的研究

选择29名受试者,随机分成简单数据组及复杂数据组,在实验室模拟视觉显示终端(VDT)数据输入作业,持续150min。结果表明,作业效率随时间延长而变动,50min后作业效率明显下降,并存在反弹和终末激发现象。作业后的生理指标变化表明机体出现不同程度的疲劳。简单数据组尿中肾上腺素有增加的趋势,复杂数据组尿中去甲肾上腺素有降低的趋势,表明复杂数据输入作业负荷比简单数据作业大。     

Defining and Improving Data Quality in Medical Registries: A Literature Review, Case Study, and Generic Framework

Over the past years the number of medical registries has increased sharply. Their value strongly depends on the quality of the data contained in the registry. To optimize data quality, special procedures have to be followed. A literature review and a case study of data quality formed the basis for the development of a framework of procedures for data quality assurance in medical registries. Procedures in the framework have been divided into procedures for the co-ordinating center of the registry (central) and procedures for the centers where the data are collected (local). These central and local procedures are further subdivided into (a) the prevention of insufficient data quality, (b) the detection of imperfect data and their causes, and (c) actions to be taken / corrections. The framework can be used to set up a new registry or to identify procedures in existing registries that need adjustment to improve data quality.Several developments in healthcare, such as progress in information technology and increasing demands for accountability, have led to an increase in the number of medical registries over recent years. We define a medical registry as a systematic collection of a clearly defined set of health and demographic data for patients with specific health characteristics, held in a central database for a predefined purpose (based on Solomon et al.¹). The specific patient characteristics (e.g., the presence of a disease or whether an intervention has taken place) determine which patients should be registered. Medical registries can serve different purposes—for instance, as a tool to monitor and improve quality of care or as a resource for epidemiological research.² One example is the National Intensive Care Evaluation (NICE) registry, which contains data from patients who have been admitted to Dutch intensive care units (ICUs) and provides insight into the effectiveness and efficiency of Dutch intensive care.³To be useful, data in a medical registry must be of good quality. In practice, however, quite frequently incorrect patients are registered or data items can be inaccurately recorded or not recorded at all.^4–8 To optimize the quality of medical registry data, participatory centers should follow certain procedures designed to minimize inaccurate and incomplete data. The objective of this study was to identify causes of insufficient data quality and to make a list of procedures for data quality assurance in medical registries and put them in a framework. By data quality assurance we mean the whole of planned and systematic procedures that take place before, during, and after data collection, to guarantee the quality of data in a database. Our proposed framework for procedures for data quality assurance is intended to serve as a reference during the start-up of a registry. Furthermore, comparing current procedures in existing medical registries with the proposed procedures in the framework should allow the identification of possible adjustments in the organization to improve data quality.     

化工数据预处理及其在建模中的应用

对化工数据预处理问题进行了研究,阐述了化工数据智能建模中辅助变量选择、数据采集、数据校正、输入数据降维等各种数据预处理的思路与方法。仿真实验表明:对数据进行有效的预处理,可以改善模型的精确度。     

对医学领域层次结构数据拟合线性回归模型时几个问题的探讨

在医学领域存在大量具有层次结构特征的资料,但通常仍采用各类传统线性回归模型分析这类数据。作者探讨了层次结构数据拟合常见的三类线性回归模型所存在的问题,三类模型参数估计间的相互关系以及参数估计精度的校正。结果显示,参数估计及其精度取决于自变量在水平２单位间和水平２单位内的变异大小,残差估计的差别与参数估计的差别有关;当数据具有层次结构时,三类常见的线性回归模型均不适宜,在一定条件下,可采用方差膨胀因子对水平１合并模型的标准误进行校正。     

代谢组学在儿科疾病中的应用

儿童期是人类个体生命周期中的起始阶段,儿科疾病有其特有的生理病理机制及诊疗方法。代谢组学通过监测生物体内源性代谢产物的变化来反应机体的生理或病理状态,并发现疾病相关的生物标记物。近年来,代谢组学技术逐渐被应用于儿科疾病,为疾病的预防、诊断和治疗带来新的契机。文章从“治未病”和“治已病”两个角度出发,综述了代谢组学技术在研究儿科疾病生理病理机制中的应用,并提出其应用于儿科疾病证候学研究的设想。