BRIDG: a domain information model for translational and clinical protocol-driven research

Becnel, Lauren B.; Hastak, Smita; Hoef, Wendy Ver; Milius, Robert P.; Slack, MaryAnn; Wold, Diane; Glickman, Michael L.; Brodsky, Boris; Jaffe, Charles; Kush, Rebecca; Helton, Edward D.

doi:10.1093/jamia/ocx004

Cited by 24 publications

(15 citation statements)

References 25 publications

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“…CDISC, HL7, and others work collaboratively through an HL7 working group now called the Biomedical Research and Regulations (BR&R). These standards development organizations, partnering with the National Cancer Institute (NCI), and the FDA have developed and continue to maintain an information model to bridge research and healthcare—the above‐mentioned Biomedical Research Integrated Domain Group (BRIDG) model . It is now a research‐healthcare standard through the International Standards Organization (ISO), CDISC, and HL7 with a broad scope of protocol‐driven research, including genomics.…”

Section: Introductionmentioning

confidence: 99%

“…Realizing the value and return on investment of a prospective regulated research study, successfully leveraging EHRs for data exchange between healthcare and research via FHIR will rely on FHIR Resources that align with global research standards. These are currently in development, using the BRIDG model (an ISO, HL7, and CDISC standard) as the common information model. The exchange of information while retaining meaning is at the heart of semantic interoperability.…”

Section: Introductionmentioning

confidence: 99%

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Use of EHRs data for clinical research: Historical progress and current applications

Nordo¹,

Levaux²,

Becnel

et al. 2019

Learning Health Systems

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The benefits of reusing EHR data for clinical research studies are numerous. They portend the opportunity to bring new therapies to patients sooner, potentially at a lower cost, and to accelerate learning health cycles—through faster data acquisition in clinical research studies. Metrics have proven that time can be saved, workflow and processes streamlined, and data quality increased significantly. Pilot projects and now actual investigational trials used for regulatory submissions have shown that these benefits support the transformation of clinical research by leveraging EHRs for research. Panelists at a recent collaborative focused on bridging clinical research and clinical care offered varying perspectives on how the latest standards and technologies could be leveraged to facilitate data transfer from EHR systems into clinical research databases, as well as the associated improvements in data quality. Panelists also discussed other avenues to leverage EHR in clinical research. Improvements and exciting possibilities notwithstanding, much work remains. Data ownership and access, attention to metadata and structured data for data sharing, and broader adoption of global standards are key areas for collaboration. With the steady increase in adoption of EHRs around the world, this is an excellent time for all stakeholders to work together and create an environment such that EHRs can be used more readily for research. The capacity for research can thus be increased to provide more high‐quality information that will contribute to rapid continuous learning health systems from which all patients can benefit.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Use of EHRs data for clinical research: Historical progress and current applications

Nordo¹,

Levaux²,

Becnel

et al. 2019

Learning Health Systems

View full text Add to dashboard Cite

show abstract

“…Si l'utilisation du standard FHIR a permis de simplifier la résolution des aspects syntaxiques de l'interopérabilité, les aspects sémantiques ont nécessité de procéder manuellement à des alignements entre les termes du référentiel du DESIMS et les concepts du BCKM. Ce constat plaide une fois encore en faveur du partage de référentiels termino-ontologiques, mais également de modèles d'informations cliniques (Becnel et al, 2017), éventuellement dédiés à un domaine particulier comme le cancer (Hochheiser et al, 2016).…”

Section: Discussion Et Conclusionunclassified

Une plateforme multimodale d’aide à la décision. Application à la prise en charge du cancer du sein dans le cadre du projet DESIREE

Séroussi¹,

Lamy²,

Prebet³

et al. 2018

Techniques et sciences informatiques

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“…To reduce the mapping burden, the CIBMTR developed a physical data model based on the Biomedical Research Integrated Domain Group (BRIDG) model [4] and mapped some of their CDEs to this model. In collaboration with multiple organizations such as the NCI and the Clinical Data Interchange Standards Consortium (CDISC) [5], the BRIDG model was developed to “produce a shared view of the dynamic and static semantics of a common domain-of-interest, specifically the domain of protocol-driven research and its associated regulatory artifacts” [6].…”

Section: Introductionmentioning

confidence: 99%

“…In addition to helping create a physical database model, the BRIDG model can facilitate mapping to other standards. For example, the BRIDG model has been harmonized and mapped to CDISC’s Clinical Data Acquisition Standards Harmonization (CDASH) and Study Data Tabulation Model (SDTM) [5]. As a result, the CIBMTR’s mappings to BRIDG can be used to facilitate mapping to CDISC.…”

Section: Introductionmentioning

confidence: 99%

Using an artificial neural network to map cancer common data elements to the biomedical research integrated domain group model in a semi-automated manner

Renner

Huang

et al. 2019

BMC Med Inform Decis Mak

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BackgroundThe medical community uses a variety of data standards for both clinical and research reporting needs. ISO 11179 Common Data Elements (CDEs) represent one such standard that provides robust data point definitions. Another standard is the Biomedical Research Integrated Domain Group (BRIDG) model, which is a domain analysis model that provides a contextual framework for biomedical and clinical research data. Mapping the CDEs to the BRIDG model is important; in particular, it can facilitate mapping the CDEs to other standards. Unfortunately, manual mapping, which is the current method for creating the CDE mappings, is error-prone and time-consuming; this creates a significant barrier for researchers who utilize CDEs.MethodsIn this work, we developed a semi-automated algorithm to map CDEs to likely BRIDG classes. First, we extended and improved our previously developed artificial neural network (ANN) alignment algorithm. We then used a collection of 1284 CDEs with robust mappings to BRIDG classes as the gold standard to train and obtain the appropriate weights of six attributes in CDEs. Afterward, we calculated the similarity between a CDE and each BRIDG class. Finally, the algorithm produces a list of candidate BRIDG classes to which the CDE of interest may belong.ResultsFor CDEs semantically similar to those used in training, a match rate of over 90% was achieved. For those partially similar, a match rate of 80% was obtained and for those with drastically different semantics, a match rate of up to 70% was achieved.DiscussionOur semi-automated mapping process reduces the burden of domain experts. The weights are all significant in six attributes. Experimental results indicate that the availability of training data is more important than the semantic similarity of the testing data to the training data. We address the overfitting problem by selecting CDEs randomly and adjusting the ratio of training and verification samples.ConclusionsExperimental results on real-world use cases have proven the effectiveness and efficiency of our proposed methodology in mapping CDEs with BRIDG classes, both those CDEs seen before as well as new, unseen CDEs. In addition, it reduces the mapping burden and improves the mapping quality.

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BRIDG: a domain information model for translational and clinical protocol-driven research

Cited by 24 publications

References 25 publications

Use of EHRs data for clinical research: Historical progress and current applications

Use of EHRs data for clinical research: Historical progress and current applications

Une plateforme multimodale d’aide à la décision. Application à la prise en charge du cancer du sein dans le cadre du projet DESIREE

Using an artificial neural network to map cancer common data elements to the biomedical research integrated domain group model in a semi-automated manner

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