Summary of DCMs

There is a critical dependence on representing clinical content in a fashion which is both human readable (usually easy), useful for secondary analysis (hard), and which is machine-interpretable (very hard). Casual approaches nearly always fail, as they effectively tie their logic in knots, and use-specific, highly focused approaches lead to volumes of hard (if not impossible) to use piles of important information. There is a recognized need to have a systematic, precise, yet expressive and flexible approach to representation of structured clinical content specification at the level of clinical, research, and legal detail required.

Detailed Clinical Models (DCM) are a precisely defined set of data element specifications which capture the meaning and context of clinical information is a format which is amenable to analysis and use in clinical decision support systems. They provide a common information model for clinical data (including procedures, history and physical exam findings, diagnosis, problems, medication use) which is independent of the use of the information, the source of the information, and which can be shared between applications/services to facilitate semantic interoperability.
Benefits of DCMs include reducing the long-term cost of system development and maintenance by centralizing the management of complexity, and providing data element specifications which can be safely reused for multiple purposes. They also can help simplify migration of data from legacy systems, can be used to define persistence mechanisms which “future proofs” the clinical data, and makes it available for use outside the application which created it.

Specific examples where DCMs are the key enabling technology include interfacing “best of breed” systems, robust clinical decision support, rich data warehouses with precisely defined data, cross organizational quality measures, early recognition of infectious disease outbreaks, and computable electronic health record information exchange.

Design of DCMs is as complex as the clinical information they represent. They need to be able to capture data at the most specific and detailed level which is clinically relevant and which meets the basic requirements of clinical documentation. They also need to be able to represent simple findings simply, while allowing for arbitrary complexity. One of the most important advantages, and requirements, for DCMs is the need to have explicit representation of relationships between data elements, such as those linking a test result to a diagnosis to a therapy.

Detailed Clinical Models include both compositional rules to combine simple models into more complex models, and using multiple hierarchical design patterns (i.e. a broader pattern is constrained into a more specific child pattern) to create models which can be easily parsed and which have deterministic meaning. Similarly, terminology is bound and further constrained as part of the model definitions. All DCMs are a constraint on a common reference model (in the HL7 context, this would be the Clinical Statement Pattern), and will follow a common approach to definition to provide consistent data structures.