Existing Data Standards Research Articles

A data standard for sourcing fit-for-purpose biological samples in an integrated virtual network of biobanks.

Human tissue biobanks are at the epicenter of clinical research, responsible for providing both clinical samples and annotated data. There is a need for large numbers of samples to provide statistical power to research studies, especially since treatment and diagnosis are becoming ever more personalized. A single biobank cannot provide sufficient numbers of samples to capture the full spectrum of any disease. Currently there is no infrastructure in the United Kingdom (UK) to integrate biobanks. Therefore the National Cancer Research Institute (NCRI) Confederation of Cancer Biobanks (CCB) Working Group 3 looked to establish a data standard to enable biobanks to communicate about the samples they hold and so facilitate the formation of an integrated national network of biobanks. The Working Group examined the existing data standards available to biobanks, such as the MIABIS standard, and compared these to the aims of the working group. The CCB-developed data standard has brought many improvements: (1) Where existing data standards have been developed, these have been incorporated, ensuring compatibility with other initiatives; (2) the standard was written with the expectation that it will be extended for specific disease areas, such as the Breast Cancer Campaign Tissue Bank (BCCTB) and the Strategic Tissue Repository Alliances Through Unified Methods (STRATUM) project; and (3) biobanks will be able to communicate about specific samples, as well as aggregated statistics. The development of this data standard will allow all biobanks to integrate and share information about the samples they hold, facilitating the possibility of a national portal for researchers to find suitable samples for research. In addition, the data standard will allow other clinical services, such as disease registries, to communicate with biobanks in a standardized format allowing for greater cross-discipline data sharing.

Building a high quality medical data architecture for multiple uses in an integrated health care environment

Objective: The aim was to create a reliable information provisioning system in healthcare for both care and research processes, based on existing data standards and standardized electronic messages. The research question is: How can a Clinical Data Ware House (CDWH) be developed for standardized basic patient data, generic nursing data and data about oncology nursing, allowing management of Electronic Health Record data, electronic data exchange and data analytics? Materials and methods: The main instrument used was the Detailed Clinical Model (DCM) data standardization approach. Further, data communication utilized HealthLevel7v3 (HL7v3) messages, transported by Mirth Connect. In an incremental, design-oriented research project, CDWH-prototypes were constructed using Enterprise Architect, a HL7v3 generator plug-in, SQL Server technology and PostgreSQL-based CDWH-technology. Results: The project resulted in 16 existing DCMs selected and 6 new DCMs defined. From those DCMs, a HL7v3 message structure was generated and a CDWH created. Implementing specialized Data Marts (DM) a connection between the CDWH and one Electronic Health Record system was built. Discussion: Detailed Clinical Models improve data quality, yet building them consumes time and resources. Some required data codes could not be identified in time and dummy codes were used instead. The existence of unstructured medical data in legacy systems may proof to be an obstacle in the future. Conclusion: The project shows that using Detailed Clinical Models as the sole source for system development leads to a sound design for a CDWH and HL7v3 messages, supporting a standards based health information system, suitable for multiple uses.

Lost in information? Ways of knowing and modes of representation in e-archaeology

This paper is concerned with the development of new cyberinfrastructures which are being constructed on the foundations of Web 2.0 looking forward to Web 3.0, employing semantic techniques for modelling archaeological information in order to enhance the location, retrieval and use of data. It argues that, as these techniques emerge, it is important to consider their origins, their underlying models and their consequent effects on the subject. Representations of archaeological information are predicated upon new and existing data standards and constitute additional layering or nesting of standards within each other. The paper identifies the need for an ethnographic study of the creation of these new technologies since they present the potential for radical change within the subject but presently lack proper evaluation.

Best practices for building interoperable systems for translational research

AbstractResearch databases, clinical systems, and lab systems all have different standards, formats and drivers for data capture, operation, analysis and integration. For interdisciplinary nutritional researchers, however, there is a dependence on all of these areas and technologies. While building and integrating these systems can be difficult, using agile practices including short iterations, testing and continuous integration methods, and close engagement with all stakeholders to create useful systems for translational research. Interoperability also requires good data standards, including the use of structured data dictionaries and existing data standards such as HL7, UMLS, LOINC, ICD, and OBO foundry ontologies.

Best practices for building interoperable systems for translational research

Research databases, clinical systems, and lab systems all have different standards, formats and drivers for data capture, operation, analysis and integration. For interdisciplinary nutritional researchers, however, there is a dependence on all of these areas and technologies. While building and integrating these systems can be difficult, using agile practices including short iterations, testing and continuous integration methods, and close engagement with all stakeholders to create useful systems for translational research. Interoperability also requires good data standards, including the use of structured data dictionaries and existing data standards such as HL7, UMLS, LOINC, ICD, and OBO foundry ontologies.

Challenges that limit meaningful use of health information technology

Health information technology (HIT) is perceived as an essential component for addressing inefficiencies in healthcare. Without understanding the challenges that limit meaningful use of HIT, there is a high chance that institutions will convert complex paper-based systems to expensive digital chaos. Clinical information systems do not communicate with each other automatically because integration of existing data standards is lacking. Data standards for medical specialties need further development. Database architectures are often designed to support single clinical applications and are not easily modified to meet the enterprise-wide needs desired by all end-users. Despite the improvements in charge capture and better access to health information the realized savings and impact on patient throughput is not enough to cover the cost of the technology, maintenance, and support. HIT is necessary for improved quality of care but it increases the cost of doing business. Poor user interface and system design hinders clinical workflow and can result in wasted time, poor data collection, misleading data analysis, and potentially negative clinical outcomes. Healthcare organizations have little recourse if a vendor fails to deliver as intended once the vendor's system becomes embedded into the organization. Decisions on technology acquisitions and implementations are often made by individuals or groups that lack clinical informatics expertise. Government incentives to increase HIT will likely result in a more computerized clinical environment. Understanding the challenges can help avoid costly mistakes. The future looks promising but caution is warranted, as achievement of full benefits of HIT requires addressing significant challenges.

Implementing Single Source: The STARBRITE Proof-of-Concept Study

ObjectiveInefficiencies in clinical trial data collection cause delays, increase costs, and may reduce clinician participation in medical research. In this proof-of-concept study, we examine the feasibility of using point-of-care data capture for both the medical record and clinical research in the setting of a working clinical trial. We hypothesized that by doing so, we could increase reuse of patient data, eliminate redundant data entry, and minimize disruption to clinic workflow. DesignWe developed and used a point-of-care electronic data capture system to record data during patient visits. The standards-based system was used for clinical research and to generate the clinic note for the medical record. The system worked in parallel with data collection procedures already in place for an ongoing multicenter clinical trial. Our system was iteratively designed after analyzing case report forms and clinic notes, and observing clinic workflow patterns and business procedures. Existing data standards from CDISC and HL7 were used for database insertion and clinical document exchange. ResultsOur system was successfully integrated into the clinic environment and used in two live test cases without disrupting existing workflow. Analyses performed during system design yielded detailed information on practical issues affecting implementation of systems that automatically extract, store, and reuse healthcare data. ConclusionAlthough subject to the limitations of a small feasibility study, our study demonstrates that electronic patient data can be reused for prospective multicenter clinical research and patient care, and demonstrates a need for further development of therapeutic area standards that can facilitate researcher use of healthcare data.

ACC/AHA Key Data Elements and Definitions for Measuring the Clinical Management and Outcomes of Patients With Chronic Heart Failure

Table of Contents 1. Uses for HF Data Standards 1892 2. Balance Between Focus and Comprehensiveness 1893 3. Balance Between “Primary” and “Summary” Data Elements 1893 4. Variety of Disease States Leading to HF 1893 5. Acute and Chronic Care, Inpatient and Outpatient Care Venues 1893 6. Therapy for HF 1893 7. Outcomes Assessment for HF 1893 1. III. HF Clinical Data Standards Elements and Definitions 1893 The American College of Cardiology (ACC) and the American Heart Association (AHA) recognize …

DATA AND METADATA REPORTING STANDARDS FOR THE U.S. ENVIRONMENTAL PROTECTION AGENCY'S PM SUPERSITES RESEARCH PROGRAM

The EPA Supersites Research Program needs consistency of metadata and data structures to facilitate information sharing among investigators, analysts, and ultimately secondary data users. Under the auspices of NARSTO a successful mechanism was created to develop and implement reporting standards. The development effort included working closely with Supersites data coordinators, investigators, and technical experts, and also leveraging from existing data standards and practices. Overall, the standards are getting good acceptance from the atmospheric research community.