The CancerGrid experience: Metadata-based model-driven engineering for clinical trials

Davies, Jim; Gibbons, Jeremy; Harris, Steve

doi:10.1016/j.scico.2013.02.010

Cited by 14 publications

(8 citation statements)

References 34 publications

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“…Studies that use CDASH CRFs achieve semantic alignment through a shared data standard, rather than through specific semantics [66]. ODM does not provide a mechanism for capturing the logical relationships between data elements used on different CRFs [66, 67]. Again, Alias can be used to address this shortcoming, but Alias currently relies on a common naming strategy to be effective.…”

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

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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%

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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“…However, it is still early days in this endeavor. Greater standardization and trusted secure communication will revolutionize patient treatments and ensure greater healthcare provision through healthcare providers having efficient access to patient data anywhere and at any time; automatically generated tool support for clinical trials (Davies et al, 2014 ); availability of online psychological treatment and support 23 ; computational modeling of diseases to personalize medication 24 and treatment. While EHRs on their own may not provide granular enough detail for biophysical simulations, such simulations would not be considered using only EHR data.…”

Section: Discussionmentioning

confidence: 99%

“…However, if sufficiently many related trials have been conducted, each investigating a similar medical hypothesis, then the data could be integrated and the results would be more informative. Semantic interoperability in these cases requires more than common ontologies for the physiological markers but also information concerning the context of the data, such as how the data was collected and who collected it, in order for aggregation to be meaningful (Davies et al, 2014 ). On a day to day basis aggregation also simplifies the administrative workload of health providers.…”

Section: Interoperability Of Patient Datamentioning

confidence: 99%

The role of markup for enabling interoperability in health informatics

McKeever

Johnson

2015

Front. Physiol.

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Interoperability is the faculty of making information systems work together. In this paper we will distinguish a number of different forms that interoperability can take and show how they are realized on a variety of physiological and health care use cases. The last 15 years has seen the rise of very cheap digital storage both on and off site. With the advent of the Internet of Things people's expectations are for greater interconnectivity and seamless interoperability. The potential impact these technologies have on healthcare are dramatic: from improved diagnoses through immediate access to a patient's electronic health record, to in silico modeling of organs and early stage drug trials, to predictive medicine based on top-down modeling of disease progression and treatment. We will begin by looking at the underlying technology, classify the various kinds of interoperability that exist in the field, and discuss how they are realized. We conclude with a discussion on future possibilities that big data and further standardizations will enable.

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“…Figure 2 shows a high-level data description metamodel in the ISO/IEC 11179 specification 14 . The lower part of the metamodel is a representation layer, which describes how information about observations and values is represented, and the upper part of the metamodel is a conceptual layer, which describes how semantic meaning of the observations and values are represented unambiguously using standard domain ontologies 15 . A data element is one of the foundational concepts in the specification.…”

Section: Introductionmentioning

confidence: 99%

Developing a data element repository to support EHR-driven phenotype algorithm authoring and execution

Jiang

Kiefer

Rasmussen

et al. 2016

Journal of Biomedical Informatics

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The Quality Data Model (QDM) is an information model developed by the National Quality Forum for representing electronic health record (EHR)-based electronic clinical quality measures (eCQMs). In conjunction with the HL7 Health Quality Measures Format (HQMF), QDM contains core elements that make it a promising model for representing EHR-driven phenotype algorithms for clinical research. However, the current QDM specification is available only as descriptive documents suitable for human readability and interpretation, but not for machine consumption. The objective of the present study is to develop and evaluate a data element repository (DER) for providing machine-readable QDM data element service APIs to support phenotype algorithm authoring and execution. We used the ISO/IEC 11179 metadata standard to capture the structure for each data element, and leverage Semantic Web technologies to facilitate semantic representation of these metadata. We observed there are a number of underspecified areas in the QDM, including the lack of model constraints and pre-defined value sets. We propose a harmonization with the models developed in HL7 Fast Healthcare Interoperability Resources (FHIR) and Clinical Information Modeling Initiatives (CIMI) to enhance the QDM specification and enable the extensibility and better coverage of the DER. We also compared the DER with the existing QDM implementation utilized within the Measure Authoring Tool (MAT) to demonstrate the scalability and extensibility of our DER-based approach.

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The CancerGrid experience: Metadata-based model-driven engineering for clinical trials

Cited by 14 publications

References 34 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 role of markup for enabling interoperability in health informatics

Developing a data element repository to support EHR-driven phenotype algorithm authoring and execution

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