Architecture for Interoperability
Data is core to everything in healthcare - from procedures to results to diagnosis, healthcare IT systems store and represent clinical and business concepts in various ways. Unfortunately, the explosive growth in HIT has resulted in patient data beingscattered across numerous, rapidly proliferating IT systems - each with their own way of capturing and representing clinical data.
Interoperability, as defined by HIMSS, is the ability of different systems and software applications to communicate, exchange data, and use the information that has been shared in a meaningful way. Moving from transaction based to value-based care models requires a shift to patient-centered care, and a longitudinal view of that patient's health history, regardless of where the care was provided. Common standards allow disparate pieces of patient information to be pulled together, providing a complete picture of the patients health status. Without agreed upon standards, interoperability is not possible.
Two 'levels' of interoperability are needed in order to ensure that both the sender and receiver of the information have the same understanding of that information. The first level of interoperability, called syntactic interoperability, is achieved through structural standards which define the format of the messages that move data uniformly from system to system. Uniform movement of data is achieved by parsing the clinical and operational data elements into separate pieces, and then wrapping those elements in a specific message format/standard. Parsing and wrapping is what preserves the concepts and actions that the data represents. Standardization of message formats alone however, does not address the meaning or context of the data points themselves. HL7, XML, and JSON are a few of the common messaging standards in healthcare today.
The second level of interoperability, semantic interoperability, addresses the definition and meaning of the specific concepts and actions themselves. Achieving semantic interoperability requires one to combine data from different sources, and then translate or "normalize" it to one vocabulary. This common vocabulary bridges the gaps in the proprietary languages from the different systems allowing for unambiguous, shared meaning of the information. Together the syntactic and semantic standards combine to create a language that represents accurate, explicit, and reliable machine-to-machine communication. Unambiguous and shared meaning are prerequisites for data analysis, computable logic, inferencing, and knowledge discovery.
Semantic vocabularies are comprised of reference models, metadata, archetypes, ontologies, and terminologies that bridge the proprietary languages of different systems to uniformly define healthcare and healthcare processes. This level of shared understanding is required if organizations are to aggregate, interpret, use, and reuse health information to analyze populations, identify longitudinal trends, apply advanced algorithms, and transform models of care. A few examples of semantic frameworks are listed in the terminology section below.
Interoperability Architecture Services
Data & Information Modeling - data aggregation models based on Object Oriented design principles (UML) and FHIR Resource/Objects. Services cover reference models, archetypes, metadata, terminologies, nomenclatures, code sets, etc.
Terminology Services: cross mapping local information models/requirements to standards-based models, terminologies and ontologies(SNOMED CT, RXNORM, CVS, LOINC, HL7 standard code and value sets, ICD9-10, CPT, etc.) ensuring accurate and safe interpretation of data.
Exchange Services: quality review of HL7, CCD, C-CDA documents and data
Integration/Extension: SMART on FHIR (Cerner), API design, workflow integration
