Objectives: Big data-based multicenter medical research is expected to bring significant advances to cancer treatment worldwide. However, there are concerns related to data sharing among multicenter networks. Clinical...
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Objectives: Big data-based multicenter medical research is expected to bring significant advances to cancer treatment worldwide. However, there are concerns related to data sharing among multicenter networks. Clinical data can be shielded by firewalls using distributed research networks (DRNs). We attempted to develop DRNs for multicenter research that can be easily installed and used by any institution. Patients and Methods: We propose a DRN for multicenter cancer research called the cancer research line (CAREL) and present a data catalog based on a common data model (CDM). CAREL was validated using 1723 patients with prostate cancer and 14 990 patients with lung cancer in a retrospective study. We used the attribute-value pairs and array data type JavaScript object notation (JSON) format to interface third-party security solutions such as blockchain. Results: We developed visualized data catalogs of prostate and lung cancer based on the observational medical outcomes partnership (OMOP) CDM, from which researchers can easily browse and select relevant data. We made the CAREL source code readily available for download and application for relevant purposes. In addition, it is possible to realize a multicenter research network using CAREL development sources. Conclusion: CAREL source can enable medical institutions to participate in multicenter cancer research. Our technology is open source, so small institutions that cannot afford to spend high costs can use it to develop a platform for multicenter research.
The solar-terrestrial physics distributed database for the ICSU World data Centers, and the NCEP/NCAR climate re-analysis data have been integrated into standard Grid environments using the OGSA-DAI framework. A set o...
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The solar-terrestrial physics distributed database for the ICSU World data Centers, and the NCEP/NCAR climate re-analysis data have been integrated into standard Grid environments using the OGSA-DAI framework. A set of algorithms and software tools for distributed querying and mining environmental archives using the UNIdatacommon data model concepts has been developed. In addition, the toolkit enables querying the data using meaningful 'human linguistic' terms. Copyright (c) 2007 John Wiley & Sons, Ltd.
Background:Electronic health record (EHR) data have many quality problems that may affect the outcome of research results and decision support systems. Many methods have been used to evaluate EHR data quality. However...
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Background:Electronic health record (EHR) data have many quality problems that may affect the outcome of research results and decision support systems. Many methods have been used to evaluate EHR data quality. However, there has yet to be a consensus on the best practice. We used a rule-based approach to assess the variability of EHR data quality across multiple healthcare systems. Methods:To quantify data quality concerns across healthcare systems in a PCORnet Clinical Research Network, we used a previously tested rule-based framework tailored to the PCORnet common data model to perform data quality assessment at 13 clinical sites across eight states. Results were compared with the current PCORnet data curation process to explore the differences between both methods. Additional analyses of testosterone therapy prescribing were used to explore clinical care variability and quality. Results:The framework detected discrepancies across sites, revealing evident data quality variability between sites. The detailed requirements encoded the rules captured additional data errors with a specificity that aids in remediation of technical errors compared to the current PCORnet data curation process. Other rules designed to detect logical and clinical inconsistencies may also support clinical care variability and quality programs. Conclusion:Rule-based EHR data quality methods quantify significant discrepancies across all sites. Medication and laboratory sources are causes of data errors.
Introduction: Quality indicators play an essential role in a learning health system. They help healthcare providers to monitor the quality and safety of care delivered and to identify areas for improvement. Clinical q...
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Introduction: Quality indicators play an essential role in a learning health system. They help healthcare providers to monitor the quality and safety of care delivered and to identify areas for improvement. Clinical quality indicators, therefore, need to be based on real world data. Generating reliable and actionable data routinely is challenging. Healthcare data are often stored in different formats and use different terminologies and coding systems, making it difficult to generate and compare indicator reports from different ***: The Observational Health Sciences and Informatics community maintains the Observational Medical Outcomes Partnership common data model (OMOP). This is an open data standard providing a computable and interoperable format for real world data. We implemented a Computable Biomedical Knowledge Object (CBK) in the Piano Platform based on OMOP. The CBK calculates an inpatient quality indicator and was illustrated using synthetic electronic health record (EHR) data in the open OMOP ***: The CBK reported the in-hospital mortality of patients admitted for acute myocardial infarction (AMI) for the synthetic EHR dataset and includes interactive visualizations and the results of calculations. Value sets composed of OMOP concept codes for AMI and comorbidities used in the indicator calculation were also ***: Computable biomedical knowledge (CBK) objects that operate on OMOP data can be reused across datasets that conform to OMOP. With OMOP being a widely used interoperability standard, quality indicators embedded in CBKs can accelerate the generation of evidence for targeted quality and safety management, improving care to benefit larger populations.
Introduction: Electronic health record (EHR) data have emerged as an important resource for population health and clinical research. There have been significant efforts to leverage EHR data for research;however, given...
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Introduction: Electronic health record (EHR) data have emerged as an important resource for population health and clinical research. There have been significant efforts to leverage EHR data for research;however, given data security concerns and the complexity of the data, EHR data are frequently difficult to access and use for clinical studies. We describe the development of a Clinical Research datamart (CRDM) that was developed to provide well-curated and easily accessible EHR data to Duke University investigators. Methods: The CRDM was designed to (1) contain most of the patient-level data elements needed for research studies;(2) be directly accessible by individuals conducting statistical analyses (including Biostatistics, Epidemiology, and Research Design (BERD) core members);(3) be queried via a code-based system to promote reproducibility and consistency across studies;and (4) utilize a secure protected analytic workspace in which sensitive EHR data can be stored and analyzed. The CRDM utilizes data transformed for the PCORnet data network, and was augmented with additional data tables containing site-specific data elements to provide additional contextual information. Results: We provide descriptions of ideal use cases and discuss dissemination and evaluation methods, including future work to expand the user base and track the use and impact of this data resource. Conclusions: The CRDM utilizes resources developed as part of the Clinical and Translational Science Awards (CTSAs) program and could be replicated by other institutions with CTSAs.
现有标准格式雷达基数据解析工具在设计上存在通用性和抽象性不足的问题,不便于雷达数据的解析和处理。为了解决这个问题,本文基于Unidata的CDM(common data model),设计和构建了中国天气雷达基数据模型,在数据模型层面实现了对天气雷...
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现有标准格式雷达基数据解析工具在设计上存在通用性和抽象性不足的问题,不便于雷达数据的解析和处理。为了解决这个问题,本文基于Unidata的CDM(common data model),设计和构建了中国天气雷达基数据模型,在数据模型层面实现了对天气雷达标准格式基数据的访问,并以Unidata开源的NetCDF Java库和IDV(Integrated data Viewer)可视化软件为基础,形成了一套基于CDM的天气雷达标准格式基数据内容提取和可视化分析工具。本研究以广州雷达新旧两种格式基本反射率数据对比为例,展示了研究成果在多普勒天气雷达标准格式基数据评估中的应用。结果表明:本研究成果方便了雷达标准格式基数据的使用,对雷达标准格式基数据的业务应用起到了促进作用。本研究成果亦可应用于雷达基数据处理与分析相关的实际业务和科研工作中,为雷达资料的应用提供基础支持。
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