Biomedical Research Integrated Domain Group Research Articles

Design and Implementation of a Multipurpose Information System for Hematopoietic Stem-Cell Transplantation on the Basis of the Biomedical Research Integrated Domain Group Model.

An important obstacle to cancer research is that nearly all academic cancer centers maintain substantial collections of highly duplicative, poorly quality-assured, nonintercommunicating, difficult-to-access data repositories. It is inherently clear that this state of affairs increases costs and reduces quality and productivity of both research and nonresearch activities. We hypothesized that designing and implementing a multipurpose cancer information system on the basis of the Biomedical Research Integrated Domain (BRIDG) model developed by the National Cancer Institute and its collaborators might lessen the duplication of effort inherent in capturing, quality-assuring, and accessing data located in multiple single-purpose systems, and thereby increases productivity while reducing costs. We designed and implemented a core data structure on the basis of the BRIDG model and incorporated multiple entities, attributes, and functionalities to support the multipurpose functionality of the system. We used the resultant model as a foundation upon which to design and implement modules for importing preexisting data, capturing data prospectively, quality-assuring data, exporting data to analytic files, and analyzing the quality-assured data to support multiple functionalities simultaneously. To our knowledge, our system, which we refer to as the Cancer Informatics Data System, is the first multipurpose, BRIDG-harmonized cancer research information system implemented at an academic cancer center. We describe the BRIDG-harmonized system that simultaneously supports patient care, teaching, research, clinical decision making, administrative decision making, mandated volume-and-outcomes reporting, clinical quality assurance, data quality assurance, and many other functionalities. Implementation of a highly quality-assured, multipurpose cancer information system on the basis of the BRIDG model at an academic center is feasible and can increase access to accurate data to support research integrity and productivity as well as nonresearch activities.

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

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.

Design and Implementation of a Multipurpose Hematopoietic Stem Cell Information System Based on the Biomedical Research Integrated Domain Model

Many hematopoietic stem cell programs capture transplant-related data into multiple data repositories simultaneously, including electronic medical record systems, repositories to support specific research projects, repositories to support specific quality management initiatives, repositories to track and guide the pre-transplant evaluation and workup, repositories to guide stem cell collection and harvest, repositories to support reporting to internal and external regulatory entities, and others. This state of affairs increases costs and decreases productivity as a result of marked duplication of data-capturing effort and poor quality, accessibility, and interoperability of captured data. An important barrier to addressing this issue is the fact that most data repositories in academic medical centers are based on different underlying data models, which constitutes a so-called chasm of semantic with regard to sharing and/or combining data residing in two or more data repositories, both within and between institutions. To address this issue, NCI, FDA, ISO, HL7, and C-DISC have developed a Biomedical Research Integrated Domain Group (BRIDG) Model. The purpose of the BRIDG model is to bridge the multiple chasms of semantic despair that exist between data repositories maintained by basic researchers, translational researchers, clinical researchers, pharmaceutical companies, and government regulators. To our knowledge, we are the first academic cancer center in the world to design and implement a cancer research system based on the NCI-BRIDG model. Important aspects of our implementation derived from the BRIDG model include extensive utilization of the NCI/BRIDG standard data elements, and resolution of the many-to-many relationship between patients and treatment protocols with a patient treatment course instance table. The latter feature provides powerful support for quality assurance of both clinical documentation and patient management by enabling our software to continually compare what is supposed to be happening to each patient, based on the treatment protocol or protocols on which the patient is registered, against what the clinical documentation indicates is happening to each patient. In addition to enhancing data sharing between researchers, we have also begun to use the system to support clinical decision-making, administrative decision-making, and clinical quality assurance.

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

It is critical to integrate and analyze data from biological, translational, and clinical studies with data from health systems; however, electronic artifacts are stored in thousands of disparate systems that are often unable to readily exchange data. To facilitate meaningful data exchange, a model that presents a common understanding of biomedical research concepts and their relationships with health care semantics is required. The Biomedical Research Integrated Domain Group (BRIDG) domain information model fulfills this need. Software systems created from BRIDG have shared meaning "baked in," enabling interoperability among disparate systems. For nearly 10 years, the Clinical Data Standards Interchange Consortium, the National Cancer Institute, the US Food and Drug Administration, and Health Level 7 International have been key stakeholders in developing BRIDG. BRIDG is an open-source Unified Modeling Language-class model developed through use cases and harmonization with other models. With its 4+ releases, BRIDG includes clinical and now translational research concepts in its Common, Protocol Representation, Study Conduct, Adverse Events, Regulatory, Statistical Analysis, Experiment, Biospecimen, and Molecular Biology subdomains. The model is a Clinical Data Standards Interchange Consortium, Health Level 7 International, and International Standards Organization standard that has been utilized in national and international standards-based software development projects. It will continue to mature and evolve in the areas of clinical imaging, pathology, ontology, and vocabulary support. BRIDG 4.1.1 and prior releases are freely available at https://bridgmodel.nci.nih.gov .

A metadata schema for data objects in clinical research.

BackgroundA large number of stakeholders have accepted the need for greater transparency in clinical research and, in the context of various initiatives and systems, have developed a diverse and expanding number of repositories for storing the data and documents created by clinical studies (collectively known as data objects). To make the best use of such resources, we assert that it is also necessary for stakeholders to agree and deploy a simple, consistent metadata scheme.MethodsThe relevant data objects and their likely storage are described, and the requirements for metadata to support data sharing in clinical research are identified. Issues concerning persistent identifiers, for both studies and data objects, are explored.ResultsA scheme is proposed that is based on the DataCite standard, with extensions to cover the needs of clinical researchers, specifically to provide (a) study identification data, including links to clinical trial registries; (b) data object characteristics and identifiers; and (c) data covering location, ownership and access to the data object. The components of the metadata scheme are described.ConclusionsThe metadata schema is proposed as a natural extension of a widely agreed standard to fill a gap not tackled by other standards related to clinical research (e.g., Clinical Data Interchange Standards Consortium, Biomedical Research Integrated Domain Group). The proposal could be integrated with, but is not dependent on, other moves to better structure data in clinical research.Electronic supplementary materialThe online version of this article (doi:10.1186/s13063-016-1686-5) contains supplementary material, which is available to authorized users.

Using Semantic Web technologies for the generation of domain-specific templates to support clinical study metadata standards.

BackgroundThe Biomedical Research Integrated Domain Group (BRIDG) model is a formal domain analysis model for protocol-driven biomedical research, and serves as a semantic foundation for application and message development in the standards developing organizations (SDOs). The increasing sophistication and complexity of the BRIDG model requires new approaches to the management and utilization of the underlying semantics to harmonize domain-specific standards. The objective of this study is to develop and evaluate a Semantic Web-based approach that integrates the BRIDG model with ISO 21090 data types to generate domain-specific templates to support clinical study metadata standards development.MethodsWe developed a template generation and visualization system based on an open source Resource Description Framework (RDF) store backend, a SmartGWT-based web user interface, and a “mind map” based tool for the visualization of generated domain-specific templates. We also developed a RESTful Web Service informed by the Clinical Information Modeling Initiative (CIMI) reference model for access to the generated domain-specific templates.ResultsA preliminary usability study is performed and all reviewers (n = 3) had very positive responses for the evaluation questions in terms of the usability and the capability of meeting the system requirements (with the average score of 4.6).ConclusionsSemantic Web technologies provide a scalable infrastructure and have great potential to enable computable semantic interoperability of models in the intersection of health care and clinical research.

FDA SEND in non-US countries responses to the standard for exchange of nonclinical data (SEND) in non-US countries

The Biomedical Research Integrated Domain Group (BRIDG) project is a collaborative initiative between the National Cancer Institute (NCI), the Clinical Data Interchange Standards Consortium (CDISC), the Regulated Clinical Research Information Management Technical Committee (RCRIM TC) of Health Level 7 (HL7), and the Food and Drug Administration (FDA) to develop a model of the shared understanding of the semantics of clinical research.The BRIDG project is based on open-source collaborative principles and an implementation-independent, use-case driven approach to model development. In the BRIDG model, declarative and procedural knowledge are represented using the Unified Modeling Language (UML) class, activity and state diagrams.The BRIDG model currently contains harmonized semantics from four project use cases: the caXchange project and the patient study calendar project from caBIG™; the standard data tabular model (SDTM) from CDISC; and the regulated products submission model (RPS) from HL7. Scalable harmonization processes have been developed to expand the model with content from additional use cases.The first official release of the BRIDG model was published in June 2007. Use of the BRIDG model by the NCI has supported the rapid development of semantic interoperability across applications within the caBIG™ program.The BRIDG project has brought together different standards communities to clarify the semantics of clinical research across pharmaceutical, regulatory, and research organizations. Currently, the NCI uses the BRIDG model to support interoperable application development in the caBIG™, and CDISC and HL7 are using the BRIDG model to support standards development.

Extension of the primary care research object model (PCROM) as clinical research information model (CRIM) for the "learning healthcare system".

BackgroundPatient data from general practices is already used for many types of epidemiological research and increasingly, primary care systems to facilitate randomized clinical trials. The EU funded project TRANSFoRm aims to create a “Learning Healthcare System” at a European level that is able to support all types of research using primary care data, to recruit patients and follow patients in clinical studies and to improve diagnosis and therapy. The implementation of such a Learning Healthcare System needs an information model for clinical research (CRIM), as an informational backbone to integrate aspects of primary care with clinical trials and database searches.MethodsWorkflow descriptions and corresponding data objects of two clinical use cases (Gastro-Oesophageal Reflux Disease and Type 2 Diabetes) were described in UML activity diagrams. The components of activity diagrams were mapped to information objects of PCROM (Primary Care Research Object Model) and BRIDG (Biomedical Research Integrated Domain Group) and evaluated. The class diagram of PCROM was adapted to comply with workflow descriptions.ResultsThe suitability of PCROM, a primary care information model already used for clinical trials, to act as an information model for TRANSFoRm was evaluated and resulted in its extension with 14 new information object types, two extensions of existing objects and the introduction of two new high-ranking concepts (CARE area and ENTRY area). No PCROM component was redundant. Our result illustrates that in primary care based research an important but underestimated portion of research activity takes place in the area of care (e.g. patient consultation, screening, recruitment and response to adverse events). The newly introduced CARE area for care-related research activities accounts for this shift and includes Episode of Care and Encounter as two new basic elements. In the ENTRY area different aspects of data collection were combined, including data semantics for observations, assessment activities, intervention activities and patient reporting to enable case report form (CRF) based data collection combined with decision support.ConclusionsResearch with primary care data needs an extended information model that covers research activities at the care site which are characteristic for primary care based research and the requirements of the complicated data collection processes.

Identifying Appropriate Reference Data Models for Comparative Effectiveness Research (CER) Studies Based on Data from Clinical Information Systems

The need for a common format for electronic exchange of clinical data prompted federal endorsement of applicable standards. However, despite obvious similarities, a consensus standard has not yet been selected in the comparative effectiveness research (CER) community. Using qualitative metrics for data retrieval and information loss across a variety of CER topic areas, we compare several existing models from a representative sample of organizations associated with clinical research: the Observational Medical Outcomes Partnership (OMOP), Biomedical Research Integrated Domain Group, the Clinical Data Interchange Standards Consortium, and the US Food and Drug Administration. While the models examined captured a majority of the data elements that are useful for CER studies, data elements related to insurance benefit design and plans were most detailed in OMOP's CDM version 4.0. Standardized vocabularies that facilitate semantic interoperability were included in the OMOP and US Food and Drug Administration Mini-Sentinel data models, but are left to the discretion of the end-user in Biomedical Research Integrated Domain Group and Analysis Data Model, limiting reuse opportunities. Among the challenges we encountered was the need to model data specific to a local setting. This was handled by extending the standard data models. We found that the Common Data Model from the OMOP met the broadest complement of CER objectives. Minimal information loss occurred in mapping data from institution-specific data warehouses onto the data models from the standards we assessed. However, to support certain scenarios, we found a need to enhance existing data dictionaries with local, institution-specific information.

Life sciences domain analysis model

ObjectiveMeaningful exchange of information is a fundamental challenge in collaborative biomedical research. To help address this, the authors developed the Life Sciences Domain Analysis Model (LS DAM), an information model that provides a framework for communication among domain experts and technical teams developing information systems to support biomedical research. The LS DAM is harmonized with the Biomedical Research Integrated Domain Group (BRIDG) model of protocol-driven clinical research. Together, these models can facilitate data exchange for translational research.Materials and methodsThe content of the LS DAM was driven by analysis of life sciences and translational research scenarios and the concepts in the model are derived from existing information models, reference models and data exchange formats. The model is represented in the Unified Modeling Language and uses ISO 21090 data types.ResultsThe LS DAM v2.2.1 is comprised of 130 classes and covers several core areas including Experiment, Molecular Biology, Molecular Databases and Specimen. Nearly half of these classes originate from the BRIDG model, emphasizing the semantic harmonization between these models. Validation of the LS DAM against independently derived information models, research scenarios and reference databases supports its general applicability to represent life sciences research.DiscussionThe LS DAM provides unambiguous definitions for concepts required to describe life sciences research. The processes established to achieve consensus among domain experts will be applied in future iterations and may be broadly applicable to other standardization efforts.ConclusionsThe LS DAM provides common semantics for life sciences research. Through harmonization with BRIDG, it promotes interoperability in translational science.

Providing Semantic Interoperability Between Clinical Care and Clinical Research Domains

Improving the efficiency with which clinical research studies are conducted can lead to faster medication innovation and decreased time to market for new drugs. To increase this efficiency, the parties involved in a regulated clinical research study, namely, the sponsor, the clinical investigator and the regulatory body, each with their own software applications, need to exchange data seamlessly. However, currently, the clinical research and the clinical care domains are quite disconnected because each use different standards and terminology systems. In this article, we describe an initial implementation of the Semantic Framework developed within the scope of SALUS project to achieve interoperability between the clinical research and the clinical care domains. In our Semantic Framework, the core ontology developed for semantic mediation is based on the shared conceptual model of both of these domains provided by the BRIDG initiative. The core ontology is then aligned with the extracted semantic models of the existing clinical care and research standards as well as with the ontological representations of the terminology systems to create a model of meaning for enabling semantic mediation. Although SALUS is a research and development effort rather than a product, the current SALUS knowledge base contains around 4.7 million triples representing BRIDG DAM, HL7 CDA model, CDISC standards and several terminology ontologies. In order to keep the reasoning process within acceptable limits without sacrificing the quality of mediation, we took an engineering approach by developing a number of heuristic mechanisms. The results indicate that it is possible to build a robust and scalable semantic framework with a solid theoretical foundation for achieving interoperability between the clinical research and clinical care domains.

The Primary Care Research Object Model (PCROM): A Computable Information Model for Practice-based Primary Care Research

ObjectivesChronic disease prevalence and burden is growing, as is the need for applicable large community-based clinical trials of potential interventions. To support the development of clinical trial management systems for such trials, a community-based primary care research information model is needed. We analyzed the requirements of trials in this environment, and constructed an information model to drive development of systems supporting trial design, execution, and analysis. We anticipate that this model will contribute to a deeper understanding of all the dimensions of clinical research and that it will be integrated with other clinical research modeling efforts, such as the Biomedical Research Integrated Domain Group (BRIDG) model, to complement and expand on current domain models. DesignWe used unified modeling language modeling to develop use cases, activity diagrams, and a class (object) model to capture components of research in this setting. The initial primary care research object model (PCROM) scope was the performance of a randomized clinical trial (RCT). It was validated by domain experts worldwide, and underwent a detailed comparison with the BRIDG clinical research reference model. ResultsWe present a class diagram and associated definitions that capture the components of a primary care RCT. Forty-five percent of PCROM objects were mapped to BRIDG, 37% differed in class and/or subclass assignment, and 18% did not map. ConclusionThe PCROM represents an important link between existing research reference models and the real-world design and implementation of systems for managing practice-based primary care clinical trials. Although the high degree of correspondence between PCROM and existing research reference models provides evidence for validity and comprehensiveness, existing models require object extensions and modifications to serve primary care research.

The BRIDG Project: A Technical Report

ObjectivesThe Biomedical Research Integrated Domain Group (BRIDG) project is a collaborative initiative between the National Cancer Institute (NCI), the Clinical Data Interchange Standards Consortium (CDISC), the Regulated Clinical Research Information Management Technical Committee (RCRIM TC) of Health Level 7 (HL7), and the Food and Drug Administration (FDA) to develop a model of the shared understanding of the semantics of clinical research. DesignThe BRIDG project is based on open-source collaborative principles and an implementation-independent, use-case driven approach to model development. In the BRIDG model, declarative and procedural knowledge are represented using the Unified Modeling Language (UML) class, activity and state diagrams. MeasurementsThe BRIDG model currently contains harmonized semantics from four project use cases: the caXchange project and the patient study calendar project from caBIG™; the standard data tabular model (SDTM) from CDISC; and the regulated products submission model (RPS) from HL7. Scalable harmonization processes have been developed to expand the model with content from additional use cases. ResultsThe first official release of the BRIDG model was published in June 2007. Use of the BRIDG model by the NCI has supported the rapid development of semantic interoperability across applications within the caBIG™ program. ConclusionsThe BRIDG project has brought together different standards communities to clarify the semantics of clinical research across pharmaceutical, regulatory, and research organizations. Currently, the NCI uses the BRIDG model to support interoperable application development in the caBIG™, and CDISC and HL7 are using the BRIDG model to support standards development.

A Standard Computable Clinical Trial Protocol: The Role of the BRIDG Model

Today’s clinical trial processes are inefficiently mired in excessive documentation and unnecessary human intervention. The protocol lies at the heart of these operations, making it a prime candidate for the benefits afforded by computer processing. With a vision to develop a standard machine-readable protocol, the Protocol Representation (PR) group, a team within the Clinical Data Interchange Standards Consortium (CDISC), identified a set of elements common to regulated clinical research protocols. These elements are being incorporated into the Biomedical Research Integrated Domain Group (BRIDG) model, an information model of biomedical research initiated by CDISC and collaboratively developed and maintained by CDISC, Health Level Seven (HL7), the National Cancer Institute (NCI), the Food and Drug Administration (FDA), and other stakeholders. The PR group plans to use BRIDG as a pathway to develop a standard structured protocol representation so that protocol information can be repurposed across multiple clinical research documents, databases, and systems from study start-up through reporting and regulatory submissions. The BRIDG model provides a means of knowledge acquisition, a tool for communication, a focus for collaboration, a starting point for application development, a means to standards development and harmonization, and an informatics research platform to support clinical research.