CDISC Standard-based Electronic Archiving of Clinical Trials

Kuchinke, Wolfgang; Aerts, Jozef; Semler, Sebastian C.; Ohmann, Christian

doi:10.3414/me9236

Cited by 44 publications

(25 citation statements)

References 3 publications

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“…Archival in a proprietary format demanded that the data collection software and its operating environment, which often included the hardware, be archived to provide a validated platform to work with the data. ODM provides a non-proprietary means to archive data that meets the US federal regulations, and does not require the archival of proprietary software to support the use of the data [74, 97]. …”

Section: 0 Resultsmentioning

confidence: 99%

“…ODM maintains the clinical data collected for a study, a full audit trail, electronic signatures, and the basic information needed for 21 CFR Part 11 and good clinical practice compliance [10, 73]. Kuchinke, Aerts, et al [97] describe ODM as a structure that organizes the archived metadata and data together into a hierarchy based on the “CRF metaphor”. An increasing number of EDC systems directly export to ODM facilitating its use for archival [86].…”

Section: 0 Resultsmentioning

confidence: 99%

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Current applications and future directions for the CDISC Operational Data Model standard: A methodological review

Hume

Aerts

Sarnikar

et al. 2016

Journal of Biomedical Informatics

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Introduction In order to further advance research and development on the Clinical Data Interchange Standards Consortium (CDISC) Operational Data Model (ODM) standard, the existing research must be well understood. This paper presents a methodological review of the ODM literature. Specifically, it develops a classification schema to categorize the ODM literature according to how the standard has been applied within the clinical research data lifecycle. This paper suggests areas for future research and development that address ODM’s limitations and capitalize on its strengths to support new trends in clinical research informatics. Methods A systematic scan of the following databases was performed: (1) ABI/Inform, (2) ACM Digital, (3) AIS eLibrary, (4) Europe Central PubMed, (5) Google Scholar, (5) IEEE Xplore, (7) PubMed, and (8) ScienceDirect. A Web of Science citation analysis was also performed. The search term used on all databases was “CDISC ODM.” The two primary inclusion criteria were: (1) the research must examine the use of ODM as an information system solution component, or (2) the research must critically evaluate ODM against a stated solution usage scenario. Out of 2,686 articles identified, 266 were included in a title level review, resulting in 183 articles. An abstract review followed, resulting in 121 remaining articles; and after a full text scan 69 articles met the inclusion criteria. Results As the demand for interoperability has increased, ODM has shown remarkable flexibility and has been extended to cover a broad range of data and metadata requirements that reach well beyond ODM’s original use cases. This flexibility has yielded research literature that covers a diverse array of topic areas. A classification schema reflecting the use of ODM within the clinical research data lifecycle was created to provide a categorized and consolidated view of the ODM literature. The elements of the framework include: (1) EDC (Electronic Data Capture) and EHR (Electronic Health Record) infrastructure; (2) planning; (3) data collection; (4) data tabulations and analysis; and (5) study archival. The analysis reviews the strengths and limitations of ODM as a solution component within each section of the classification schema. This paper also identifies opportunities for future ODM research and development, including improved mechanisms for semantic alignment with external terminologies, better representation of the CDISC standards used end-to-end across the clinical research data lifecycle, improved support for real-time data exchange, the use of EHRs for research, and the inclusion of a complete study design. Conclusions ODM is being used in ways not originally anticipated, and covers a diverse array of use cases across the clinical research data lifecycle. ODM has been used as much as a study metadata standard as it has for data exchange. A significant portion of the literature addresses integrating EHR and clinical research data. The simplicity and readability of ODM has likely contributed to its succe...

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Section: 0 Resultsmentioning

confidence: 99%

Section: 0 Resultsmentioning

confidence: 99%

Current applications and future directions for the CDISC Operational Data Model standard: A methodological review

Hume

Aerts

Sarnikar

et al. 2016

Journal of Biomedical Informatics

View full text Add to dashboard Cite

show abstract

“…Over the next year, we plan to add many more attributes to our schema and to allow for different methods of measurement. Rather than require data partners to conform to a new standardized ontology, we intend instead to map their attributes to a set of attributes that are common across data partners [25] and then incorporate attributes that are specific to each data partner without modification. We also encountered cases where subjects appeared across data repositories; for example, some subjects in the National Alzheimer’s Coordinating Center (NACC) were also subjects in LAADC.…”

Section: Resultsmentioning

confidence: 99%

The Global Alzheimer's Association Interactive Network

Toga

Neu

Bhatt

et al. 2015

Alzheimer's & Dementia

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INTRODUCTION The Global Alzheimer’s Association Interactive Network (GAAIN) is consolidating the efforts of independent Alzheimer’s disease data repositories around the world with the goals of revealing more insights into the causes of Alzheimer’s disease, improving treatments, and designing preventative measures that delay the onset of physical symptoms. METHODS We developed a system for federating these repositories that is reliant upon the tenets that (a) its participants require incentives to join, (b) joining the network is not disruptive to existing repository systems, and (c) the data ownership rights of its members are protected. RESULTS We are currently in various phases of recruitment with over 55 data repositories in North America, Europe, Asia and Australia and can presently query 250,000+ subjects using GAAIN’s search interfaces. DISCUSSION GAAIN’s data sharing philosophy, which guided our architectural choices, is conducive to motivating membership in a voluntary data sharing network.

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“…The CDISC consortium defined a set of standards for data capture, data transfer, and data analysis to facilitate data exchange between different study sites and their respective EDC systems. These standards include the XML-based Operational Data Model (ODM) to construct and model customized eCRF, and the Clinical Data Acquisition Standards Harmonization (CDASH) model, which defines the recommended data collection fields for 16 domains (version 1.1) such as patient demographics, concomitant medications, laboratory test results, or adverse events [116,117].…”

Section: Clinical Datamentioning

confidence: 99%

Clinical Data Reuse or Secondary Use: Current Status and Potential Future Progress

Meystre¹,

Lovis²,

Bürkle³

et al. 2017

Yearb Med Inform

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SummaryObjective: To perform a review of recent research in clinical data reuse or secondary use, and envision future advances in this field. Methods: The review is based on a large literature search in MEDLINE (through PubMed), conference proceedings, and the ACM Digital Library, focusing only on research published between 2005 and early 2016. Each selected publication was reviewed by the authors, and a structured analysis and summarization of its content was developed. Results: The initial search produced 359 publications, reduced after a manual examination of abstracts and full publications. The following aspects of clinical data reuse are discussed: motivations and challenges, privacy and ethical concerns, data integration and interoperability, data models and terminologies, unstructured data reuse, structured data mining, clinical practice and research integration, and examples of clinical data reuse (quality measurement and learning healthcare systems). Conclusion: Reuse of clinical data is a fast-growing field recognized as essential to realize the potentials for high quality healthcare, improved healthcare management, reduced healthcare costs, population health management, and effective clinical research.

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CDISC Standard-based Electronic Archiving of Clinical Trials

Cited by 44 publications

References 3 publications

Current applications and future directions for the CDISC Operational Data Model standard: A methodological review

Current applications and future directions for the CDISC Operational Data Model standard: A methodological review

The Global Alzheimer's Association Interactive Network

Clinical Data Reuse or Secondary Use: Current Status and Potential Future Progress

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