Lessons learned in detailed clinical modeling at Intermountain Healthcare

Abstract

Background and objective: Intermountain Healthcare has a long history of using coded terminology and detailed clinical models (DCMs) to govern storage of clinical data to facilitate decision support and semantic interoperability. The latest iteration of DCMs at Intermountain is called the clinical element model (CEM). We describe the lessons learned from our CEM efforts with regard to subjective decisions a modeler frequently needs to make in creating a CEM. We present insights and guidelines, but also describe situations in which use cases conflict with the guidelines. We propose strategies that can help reconcile the conflicts. The hope is that these lessons will be helpful to others who are developing and maintaining DCMs in order to promote sharing and interoperability.

Methods: We have used the Clinical Element Modeling Language (CEML) to author approximately 5000 CEMs.

Results: Based on our experience, we have formulated guidelines to lead our modelers through the subjective decisions they need to make when authoring models. Reported here are guidelines regarding precoordination/postcoordination, dividing content between the model and the terminology, modeling logical attributes, and creating iso-semantic models. We place our lessons in context, exploring the potential benefits of an implementation layer, an iso-semantic modeling framework, and ontologic technologies.

Conclusions: We assert that detailed clinical models can advance interoperability and sharing, and that our guidelines, an implementation layer, and an iso-semantic framework will support our progress toward that goal.

Keywords: Health Information Systems/Standards; Information Storage and Retrieval; Medical Records Systems, Computerized/Standards; Semantics; Vocabulary, Controlled.

Figure 1

An example detailed clinical model (for a heart rate measurement).

Figure 2

Meaning in precoordinated keys versus meaning in multiple attributes. (A) A precoordinated approach in which multiple separate models, each with a precoordinated 'key', are created. (B) A postcoordinated approach in which a single model is created and differences are captured in multiple attributes.

Figure 3

Meaning in precoordinated values versus meaning in value+attribute. (A) A precoordinated approach in which the value of ‘BodyLocation’ is drawn from a set of precoordinated values. (B) A postcoordinated approach in which the value of ‘BodyLocation’ is captured by the combination of a value and a laterality.

Figure 4

An example of a model versus terminology choice.

Figure 5

An example of an implementation model.

Figure 6

An example of an iso-semantic transform.