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Analyze Functional Profile
The use of well defined service metadata promotes better discovery and reuse of services during design.
Capability Elaborations
This Functional Profile includes, but is not limited to, the following capability elaborations:
Derived From Requirements
- Gap Analysis::Analyze::126 - Query and retrieve Value Sets Query and retrieve Value Sets
- *Gap Analysis::Analyze::146 - Support integrative analysis across multiple disciplines * Support integrative analysis across multiple disciplines
- Semantic Infrastructure Requirements::Service Discovery and Governance::Analyze Services The use of well defined service metadata promotes better discovery and reuse of services during design and run time. Service metadata includes information about service interactions and dependencies. It also includes a classification scheme for organizing services based on business objectives, domain, and usage. It also links services to all the supporting artifacts in the specification and provides a placeholder for conformance statements. This enables better reuse across the enterprise and eliminates redundancy. A capability to analyze data was explicitly cited as a critical goal by CDISC and caEHR stakeholders. From the perspective of the KR, this requirement should perhaps be entitled “Enable Data Integration through Model Alignment,” since it allows users to provide the metadata necessary to enable integration of heterogeneous data sets. Requirements include: * Query and retrieve Value Sets * Support integrative analysis across multiple disciplines * Query and aggregate data across organizations, data sets, time, and geographies. * Integrate clinical trial data with health record data. By multiple data sets CDISC means different clinical trials, data sets from different EHR systems, and the interoperability of clinical trial and EHR data sets. At a minimum the Knowledge Repository should be able to identify those data sets that are constructed from or map to the Knowledge Repository's information models, model elements, and alignment assertions among these models. Thus, an approved alignment between a local information model and (for example) a specific CDISC standard would be an assertion that new models could map to. This linkage from the KR to specific data sets that instantiate information models sets the stage for full model analysis.
- Gap Analysis::CDISC::CDISC-19 - Use CDISC standards to integrate clinical trial data with health record data A key long-term CDISC business goal is to integrate Clinical Trial Data with Electronic Health Record (EHR) data. These two types of data sets collect similar data about individuals in order to diagnose and treat disease. This integration would allow much larger data sets to be explored on the outcomes of specific treatment on a diverse range of individuals. The clinical data set form naturally occurring "experiments," and allows the longer term observation of approved treatments as well as off-label treatments. This would start to integrate medical care from (research) bench to (patient) bed.
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integrativeAnalysis capability elaboration
Support integrative analysis across multiple disciplines Query and retrieve Value Sets
The use of well defined service metadata promotes better discovery and reuse of services during design and run time. Service metadata includes information about service interactions and dependencies. It also includes a classification scheme for organizing services based on business objectives, domain, and usage. It also links services to all the supporting artifacts in the specification and provides a placeholder for conformance statements. This enables better reuse across the enterprise and eliminates redundancy.
A capability to analyze data was explicitly cited as a critical goal by CDISC and caEHR stakeholders. From the perspective of the KR, this requirement should perhaps be entitled “Enable Data Integration through Model Alignment,” since it allows users to provide the metadata necessary to enable integration of heterogeneous data sets.
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Integrate clinical trial data with health record data.
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Support integrative analysis across multiple disciplines
The use of well defined service metadata promotes better discovery and reuse of services during design and run time. Service metadata includes information about service interactions and dependencies. It also includes a classification scheme for organizing services based on business objectives, domain, and usage. It also links services to all the supporting artifacts in the specification and provides a placeholder for conformance statements. This enables better reuse across the enterprise and eliminates redundancy.
A capability to analyze data was explicitly cited as a critical goal by CDISC and caEHR stakeholders. From the perspective of the KR, this requirement should perhaps be entitled “Enable Data Integration through Model Alignment,” since it allows users to provide the metadata necessary to enable integration of heterogeneous data sets. Integrate clinical trial data with health record data.
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Query and retrieve Value Sets
The use of well defined service metadata promotes better discovery and reuse of services during design and run time. Service metadata includes information about service interactions and dependencies. It also includes a classification scheme for organizing services based on business objectives, domain, and usage. It also links services to all the supporting artifacts in the specification and provides a placeholder for conformance statements. This enables better reuse across the enterprise and eliminates redundancy.
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