CLINICAL DATA AND PROCESS MODELING
Healthcare IT Services
J P Systems worked through the ONC to model 39 healthcare domains for the Federal Health Information Model (FHIM) Program. These highly detailed information models were developed over a period of 12 years. They were updated to incorporate the FHIR terminology standards. They serve as a starting point for the development of clinical data models for Federal health. Information Models exist at a higher level architecturally than Data models.
The Federal Health Information Model was a project under an initiative of the Federal Health Architecture (FHA). Briefly, the U.S. federal government had established a Federal Enterprise Architecture (FEA), which provides guidance to federal agencies on how they should develop their enterprise architectures. The methodology used by FEA, the Federal Segment Architecture Methodology (FSAM) recognizes that some "lines of businesses" in which the federal government is engaged cross agency boundaries. The healthcare line of business is one such case. As a result, the FHA was established as a partnership of over 20 departments and agencies to coordinate Healthcare Information Technology activities.
The FHIMS program was intended to coordinate the efforts of the partner agencies with respect to information and terminology standards, including the coordination of agency efforts at relevant Standards Development Organizations (SDOs) such as Health Level Seven (HL7), the National Council for Prescription Drug Programs (NCPDP), Integrating the Healthcare Enterprise (IHE), and others.
Another FHIMS initiative is the Federal Health Terminology Model project, which coordinates partner agency efforts to develop healthcare terminology models (i.e., new content), and to enumerate "value sets" that can be associated with the Information Model. The Terminology Model is closely related to the Information Model, as they are each describing the same real-world concepts from two different angles. The Information Modeling team will work very closely with the Terminology Modeling team to identify those concepts which should be enumerated in a value set, to define that value set, and to define the members of the value set.
Data and information models are components of data architecture and are data architecture artifacts. Clinical data is highly complex, ever changing, and always flows from the mission of the organization. A neonatal facility collects different data about patients than an Allergist or an ER. J P Systems has many decades of experience modeling clinical data and processes. The FHIMS model may be viewed here.
Domain specific knowledge is as important as the technical knowledge to the proper construction of model designs. Data modeling is a critical step when embarking on a new domain design. Therefore, we optimize and standardize the reference data to facilitate its exchange with other healthcare providers. The development of the HL7's US FHIR Core is a critical step in the nation's data architecture: US FHIR Core Data Elements.
The data standards and standardized reference terminologies are specified in the data elements in the models to facilitate interoperability:
- The standardized reference terminology specify the valid data sets for each data element.
- Leverage the semantics to ensure consistency across models and model artifacts.
- Using the same semantics as a basis for logical and physical database model generation, software component and service generation, rule development (e.g., in production rule-based systems), etc.
- Logical consistency, validation, and reuse
Our design methodologies are finely tuned to increase interoperability within an enterprise, and between an enterprise and its data exchange partners. Standardized data elements are used to create a core of data used in the United States.
Data models help relate the Business Architecture (what you do as an organization) to the Information Technology (what you capture, store, and process). IA is the high-level description of business information and communications which enables the translation of business perspectives to and from your IT systems. Data Architecture occurs on a lower level, closer to that of your data files and their actual contents. Business Strategy and IA are closely related in that IA enables the existing Business Strategy and fosters new Business Strategies.
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