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• Artifact Management
• Service Lifecycle Management and Governance
• CRF ModelingForms Defintion & Modeling
• Conformance Testing
• P/S/T & Terminology Integration

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• HL7 SAIF behavioral model
• Orchestrations & Workflows
• Rules - Drools, etcRules.

Content:

Content includes all instructured text and other forms of content that make up a service specification, storyboards, etc. Content is an integral part of service specification that is leveraged across multiple initiatives.

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The requirements listed above are derived from the following use cases in the previous section:

caEHR: The caEHR project has adopted ECCF for specifications and CDA documents for interoperability. The caEHR project requirements include, the need for an infrastructure for managing all the artifacts generated during specification process, including HL7 models and documents. The caEHR project also intends to publish these artifacts to the community and vendors. The infrastructure needs to support better discovery, making all the relevant information avaialble in the right context.

ONC and Other external EHR adopters: ONC has adopted CCD and CCR for meaninfuly use. All national EHR implementations are expected to support forms and the semantics of these forms play a critical role in interoperability. The semantic infrastructure must provide a mechanism to create, store and manage these forms.

Clinical Trails: Clinical trails use forms to capture clinical information, and the semantics captured by these forms are critical for interoperability and reporting. The semantic infrastructure must provide a mechanism to manage the lifecycle of these forms. 

Service Lifecycle Management and Governance

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Link to use case: the services may be located at an institution or hosted externally by service providers.

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Forms Defintion & Modeling

Forms  

A form by contrast to a Document is a construct which is used to capture the information in the document.  A form defines skip patterns, validation rules, and any other aspect required to render or capture information for a document

A document is specifically a clinical document which represents information about a clinical activity.  The document contains the specific information gained during that clinical activity and supports the broader definitions of Document and Clinical Documentation.  This includes the ability to transformed into human readable form, and be transferred, or transmitted for use 

This is a transactional database that contains the component definitions of the documentation process.  This includes the core form definitions which will be represented using XML.

This data will likely contain a number of different components, however, the primary elements will focus on two structures.  One is the form definition that contains the layout and definition with rules for validation, bindings to pick lists, and skip patterns.  The second  is the schema definition that is required to capture and represent the data

Discovery includes service discovery, data discovery, and policy discovery. Service discovery allows primary users as well as secondary users to locate a service specification and instances based on attributes in the service metadata (for example, via a search for specific micro-array analysis services). Data discovery enables secondary users to find the types of data available in the ecosystem as well as summary-level information about available data sets. Policy discovery allows application developers to find and retrieve policies on services.

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Link to use case: Image analysis as services will need to adequately describe the actions image analysis performs and the required input and expected output, so a human or a computer may discover appropriate analysis algorithms to be used on an image.

Service Generation

Service Discovery & Utilization

This group of requirements focuses on enabling developers of composite services and applications to discover, compose, and invoke services. This includes the discovery of published services based on service metadata and the generation of client APIs in multiple languages to provide cross-platform access to existing services. This also includes the ability to use an "analytical" service locally in the case where the data to be processed is too large to move to a remote service.

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• Data representation and information models
• Data management
• Data exploration and query
• High-throughput data
• Provenance
• Data semantics

Data Representation and Information Models

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Link to use case: The oncologist must be able to quickly find glioblastoma data sets, indicating the fields that he is interested in comparing from his clinical data in order to find similar disease conditions and associated treatment plans. Temporal queries allow clinicians to identify changes in patient condition and treatment over time.

High-Throughput Data

An extremely important data requirement is to store and access emerging large data sets (for example, next-generation sequencing data). The key non-functional requirements in this area are efficient storage and access of enormous amount of data, potentially via streaming, and potentially performance of computation at the location where the data is stored, if the volume of data is too large to be transferred. As much of this data is binary data, this forms the requirement for a standards-based approach to binary data transfer.

Link to use case: High-resolution digital images must be transferred to other sites during review.

Provenance

Provenance encompasses the origin and traceability of data throughout an ecosystem. This is a clear requirement directly from the use case in order to ensure that all steps of patient care and research are clearly linked via the patient record.

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-         Semantic Infrastructure Users and Roles
-         Functional Requirements
   Artifact Management
§  Static Models
§  Behavioral Models
§  Forms

Data Semantics

In a diverse information environment, semantics must be used to clearly indicate the meaning of data. This requirement is expected to be addressed by the Semantics Infrastructure, although there will be a touchpoint between the caGrid 2.0 and the semantics infrastructure to annotate data with semantics. Integration with the semantics infrastructure will enable reasoning, semantic query, data mediation (for example, ad hoc data transformation) and other powerful capabilities.

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